John Kutay

According to CGOC, 60% of data that’s collected today has lost some or all of its business value. Trends change rapidly; if an organization uses last month’s data to make a decision for a current problem, they may draw an erroneous conclusion, formulate the wrong response, or worse.

Today, organizations must respond to the real-time demands of their business by overhauling their data infrastructure. In this age of smartphones and IoT devices that work in real time, analyzing historical data in batches for all business tasks is not good enough. They need to do more by getting instantaneous insights through real-time analytics. This can help them to understand their customers better and respond to market changes quickly. According to Garner, by this year, more than 50% of business systems will make decisions based on real-time context data.

Real-Time Analytics Use Cases

The emergence of real-time analytics has allowed organizations to collect data from user interactions, machines, and operational infrastructure in real time. They can now act on data immediately — soon after it makes its way to their systems. This can help businesses earn a competitive edge by offering a broad array of use cases in different industries, including detecting fraud in finance, increasing the speed at which goods are delivered in the supply chain, and optimizing the management of inventory in manufacturing.

Real-Time Analytics for Supply Chain

Real-time analytics can be useful for addressing efficiencies in the supply chain. These inefficiencies are costly; they led to almost a loss of $2 billion in the UK. The supply chain industry has a complicated ecosystem due to the presence of several channels — both offline and online — and participants, such as vendors and manufacturers.

Supply chain management is always looking to improve cost savings, speed, and productivity, but the lack of real-time integration between all the external and internal stakeholders is a challenge. There’s also the equipment failure dilemma — a piece of equipment or machine is always vulnerable to failing at a critical time. Lastly, data related to supply and demand isn’t always reliable with batch processing since batch data can be a few hours (or days) old.

With the introduction of real-time analytics, the discussion has moved from merely automating processes to integrating data in real time and using it to make better decisions. Now, it’s possible to view real-time data feeds to manage the supply chain and plan better for demand and supply. Perhaps that’s why around 66% of supply chain leaders think that the use of analytics will be of critical importance to their operations in the future.

Optimizing route and train drivers

Logistics fleet managers can use real-time analytics to track shipping fleets and trucks, improve route optimization, and prevent bottlenecks, such as traffic issues, to ensure the swift and safe delivery of goods.

Modern data analytics software for transportation and logistics optimizes routes through a route planning algorithm. A route planning algorithm is fed real-time data to find the most affordable, efficient, and fastest route of delivery. For example, these algorithms can analyze real-time data on fuel consumption, weather conditions, and traffic patterns on key roadways to revise routes, minimize delivery time, and reduce the frequency of damaged and expired products. This is beneficial for drivers as well, as they can save time and avoid hurdles during their routes.

Over time, when real-time data is continuously aggregated, it can help to spot recurring issues faced by drivers. Many companies collect real-time data on fuel by installing fuel-level sensors in their vehicles. These sensors can provide data on fuel consumption, fuel level volumes, and locations and dates of refills. For instance, if two drivers drive on the same route and the sensors convey that one of them is using significantly more fuel, then the fleet manager can look into the matter.

Fleet managers can also use an electronic logging device (ELD) for driver behavior analytics. For instance, you can use an accelerometer and gyroscope with ELDs to collect real-time information on collision, braking, and harsh turning. This way, you can create awareness of safe driving among your drivers and avoid potential catastrophic future events by sending details to drivers about areas having dangerous turns.

Reducing operational risks

You can use real-time analytics to mitigate operational risks. Sometimes, there are unscheduled fleet or factory maintenance requirements that can hinder operations in the supply chain. With real-time analytics, data science–based methods can help you with estimating when your equipment might fail. For this purpose, thermal imaging, vibration analysis, infrared, and acoustics are used. Real-time analytics takes advantage of these technologies to measure and collect operations and equipment data in real time via remote sensor networks (e.g., oil sensors to detect debris from wear). This can help minimize maintenance costs.

For example, you can use an accelerometer to collect data for vibration analysis in your real-time analytics system. The accelerometer produces a voltage signal that shows the frequency and amount of vibration the machine is generating every minute or second. These signals are transformed as a fast Fourier transform (amplitude vs. frequency) or time waveform (amplitude vs. time).

With real-time analytics, vibration analysts can review this data through algorithms and assess the machine’s health and detect potential issues, such as electrical motor faults, misalignment, mechanical looseness, bearing failures, and imbalance. This also ensures that your technicians don’t always have to be in proximity to your factory for routine maintenance. In addition, it helps to know what issue your machine is facing, which can save a lot of time.

Improving supply and demand

Traditionally, supply chain management used enterprise resource planning (ERP) systems and disparate storage systems for data. This meant that shared data updates between stakeholders were based on a specific time period (e.g., daily or hourly). Today, supply and demand have constant fluctuations, making it necessary to collect and analyze data from suppliers in real time.

For example, you can view a key inventory metric in your supply chain dashboard: inventory turnover. A higher inventory turnover indicates that your products are moving quickly through the supply chain, and you are meeting the current demand. Similarly, you can analyze the latest sentiment data from social media for demand forecasting.

Real-Time Analytics for Finance

Few industries can use real-time analytics better than the finance industry. That’s because it’s synonymous with large amounts of data, extreme volatilities, and the need for detecting complex patterns in real time. Real-time analytics offers the capability to correlate, analyze, and perform actions on finance-related data like transactional data, company updates, market prices, and trading data. This data comes in large volumes from several sources every millisecond, and acting on it quickly is crucial for financial firms and banks.

Detecting stock market manipulation

Real-time analytics can help to identify trends of manipulation in markets, especially insider trading and price manipulations that are done to gain profit in real time. In stock trading, it’s common to gain profit by using dubious methods, such as insider trading or the artificial deflating/inflating of stock prices. Real-time analytics can be used to collect data from Twitter streams, newsfeeds, company announcements, and other external data streams to identify potential attempts to manipulate the market.

One of the techniques used to identify manipulation in stock pricing is Generative Adversarial Networks (GANs). In this model, a discriminator or a type of classifier is used to separate real data from fake data. A generator model is used to create fake data, which it does by getting feedback from the discriminator. The generator is used to create data that looks like manipulated stock prices, which it uses to train the generator to tell if price data is correct or fake.

Preventing money laundering

The banking sector often struggles with the detection of money laundering and payment fraud. It not only affects the bank financially but also damages its corporate image. Real-time analytics can help banks to use machine learning and Markov modeling to safeguard themselves from fraudulent activities.

Banks can use real-time analytics to transfer their specialized domain knowledge about how fraudulent behavior works to a set of rules that can analyze incoming streams of data in real time.

Markov models are used for modeling systems that undergo random changes. They model the probabilities of different states and identify the rate at which these states transition. This mechanism allows them to be used for recognizing patterns and making predictions — precisely why they are used for fraud detection to find rare transaction sequences. This way, banks can try to identify complex fraudulent activities where experienced criminals break down one transaction into multiple smaller transactions for money laundering.

Real-Time Analytics for Manufacturing

According to a BCG survey, 72% of manufacturer executives find advanced analytics “to be important.” Despite this, only 17% of them have been able to get “satisfactory” value out of it. There’s a lot of room for improvement and a shrewd implementation of real-time analytics can improve your operational efficiency.

Real-time analytics can help you to continuously track, control, and fine-tune manufacturing processes, such as managing inventory. It also allows you to view how your manufacturing plant is functioning in real time and can notify you about bottlenecks. This data can be collected from CRMs, ERPs, machines, sensors, and additional cameras installed in the facility.

Managing inventory

With real-time analytics, you can get an in-depth overview of what’s happening with your inventory in real time. This includes the sales potential, the cost of inventory, and the status of aging products. For instance, viewing a dashboard for aging products can ensure that you aren’t left with expired stock, so you can sell soon-to-be-expired items on a priority basis. You can use real-time analytics for inventory management in four ways:

• Descriptive analytics: It focuses on the “what,” i.e., what are your basic figures in inventory? These are numbers that are shown on dashboards. For instance, you can view a dashboard to check the cost per unit of the newly arrived items at the warehouse.
• Diagnostic analytics: Diagnostics analytics look for the root cause behind your reported data. For example, if you want to know why your organization experienced a Month-over-Month (MoM) growth, then diagnostic analytics can provide insights into the decisions that were the catalyst to it.
• Predictive analytics: Predictive analytics uses your real-time data to predict what the future has in store for you. For instance, real-time analytics can use the news of the outbreak of a new COVID-19 variant to warn you about the possible shortage of PPE equipment.
• Prescriptive analytics: Prescriptive analytics recommends the action that you need to take. For instance, it can tell you to fill 80% of orders for a client in a four-day time frame.

Use Striim to Power Your Real-Time Analytics Architecture

Regardless of which industry you operate in, you can use Striim to perform real-time analytics by using it as an intermediary between your source and target systems. Striim comes with plenty of convenient features. As a real-life example, take a brief look into how Striim transformed Ciena’s real-time analytics ecosystem.

Ciena is a prominent telecommunications equipment supplier. Ciena was looking to create a modern real-time analytics ecosystem to improve the customer experience and make sharing of data access easier. Ciena used Snowflake as a data warehouse for operational reporting. They used Striim as a real-time data analytics tool to replicate changes from Ciena’s data sources — Oracle, SQL Server, MySQL, Salesforce — to Snowflake. Striim collected, filtered, aggregated, and updated this data in real time. This amounted to loading nearly 100 million events per day, enabling Ciena’s business functions (e.g., accounting, manufacturing) to perform advanced real-time analytics with better speed and ease than before.

For starters, here’s what you can do with Striim.

• You can go through Striim’s large library of templates to find a wizard that allows you to connect and integrate your data sources. For instance, Striim can help you to move data from Oracle Database to Kafka, SQL Server CDC to Azure SQL DB, Oracle CDC to BigQuery, and many more.
• The wizard helps you create a data flow application. A data flow application allows you to define how you want to collect, process, and deliver data. This can be as simple as setting up a data source and a target system and moving data through them in real time through a stream.
• Your data flow applications can continuously ingest data, process it in real time, and deliver it to your targets with millisecond latency for real-time analytics and longer-range analyses including historical data.
• You can gain real-time, actionable insights from your streaming data pipelines through streaming analytics. Striim also lets you build dashboards to visualize your data flows in real time.
• You can configure built-in alerts in Striim for a wide range of metrics. In case of failures or errors, you can also set up automated workflows that trigger corrective actions.

To learn more about Striim, request a demo or free trial and see for yourself how Striim can be a useful addition to your real-time analytics architecture.

 

Companies throughout the world generate large amounts of data, which continues to grow at a rapid pace. By 2025, the total number of data created, consumed, and stored in the world is expected to accumulate up to 181 zettabytes.

A significant amount of data is produced as live or real-time streams, also referred to as streaming data. These streams can come from a wide range of sources, including clickstream data from mobile apps and websites, IoT-powered sensors, and server logs. The ability to track and analyze streaming data has become crucial for organizations to lend support to their departmental operations.

However, there are a couple of challenges that make it difficult for organizations to deal with streaming data. 

1. You have to collect large amounts of data from streaming sources that generate events every minute. 
2. In its raw form, streaming data lacks structure and schema, which makes it tricky to query with analytic tools.

Today, there’s an increasing need to process, parse, and structure streaming data before any proper analysis can be done on it. For instance, what happens when someone uses a ride-hailing app? The app uses real-time data for location tracking, traffic data, and pricing to provide the most suitable driver. It also estimates how much time it’ll take to reach the destination based on real-time and historical data. The entire process from the user’s end takes a few seconds. But what if the app fails to collect and process any of this data on time? There’s no value to the app if the data processing isn’t done in real time.

Traditionally, batch-oriented approaches are used for data processing. However, these approaches are unable to handle the vast streams of data generated in real time. To address these issues, many organizations are turning to stream processing architectures as an effective solution for processing vast amounts of incoming data and delivering real-time insights for end users.

What is Stream Processing?

Stream processing is a data processing paradigm that continuously collects and processes real-time or near-real-time data. It can collect data streams from multiple sources and rapidly transform or structure this data, which can be used for different purposes. Examples of this type of real-time data include information from social media networks, e-commerce purchases, in-game player activity, and log files of web or mobile users.

As Alok Pareek mentions in his explanation of stream processing, the main characteristics of data stream processing include:

• Data arrives as an ongoing stream of events
• It requires high-throughput processing
• It requires low-latency processing

Stream processing can be stateless or stateful. The state of the data tells you how previous data affects the processing of current data. In a stateless stream, the processing of current events is independent of the previous ones. Suppose you’re analyzing weblogs, and you need to calculate how many visitors are viewing your page at any moment in time. Since the result of your preceding second doesn’t affect the current second’s outcome, it’s a stateless operation.

With stateful streams, there’s context, as current and preceding events share their state. This context can help past events shape the processing of current events. For instance, a global brand would like to check the number of people buying a specific product every hour. Stateful stream processing can help to process the users who buy the product in real time. This data is then shared in a state, so it can be aggregated after one hour.

Stream Processing vs. Batch Processing

Batch processing is about processing batches containing a large amount of data, which is usually data at rest. Stream processing works with continuous streams of data where there is no start or end point in time for the data. This data is then fed to a streaming analytics tool in real time to generate instant results.

Batch processing requires that the batch data is first loaded into a file system, a database, or any other storage medium before processing can begin. It’s more practical and convenient if there’s no need for real-time analytics. It’s also easier to write code for batch processing. For example, a fitness-based product company goes through its overall revenues generated from multiple stores around the country. If it wants to look at the data at the end of the day, batch processing is good enough to adequately meet its needs.

Stream processing is better when you have to process data in motion and deliver analytics outcomes rapidly. For instance, the fitness company now wants to boost brand interest after airing a commercial. It can use stream processing to feed social media data into an analytics tool for real-time audience insights. This way, it can determine audience response and look into ways to amplify brand messaging in real time.

Is Stream Processing the Same as Complex Event Processing?

Stream processing is sometimes used interchangeably with complex event processing (CEP). Complex event processing is actually a subset of stream processing. It’s a set of techniques and concepts used to process real-time events and extract meaningful information from these event streams on a continuous basis. 

CEP is linked to different data sources in an organization, where pre-built triggers are defined for specific events. When these events occur, alerts and automated actions are triggered. For example, in the stock market, when stock price data arrives, the system can match stock data with real-time and historical patterns and automate the decision to buy or sell a stock. 

How Does Stream Processing Work?

Modern applications process two types of data: bounded and unbounded. Bounded data refers to a dataset of finite size — one where you can easily count the number of elements in the dataset. It has a known endpoint. For instance, a bookstore wants to know the number of books sold at the end of the day. This data is bounded because a fixed number of books were sold throughout the day and sales operations ended for the day, which means it has a known endpoint.

Unbounded data refers to a dataset that is theoretically infinite in size. No matter how advanced modern information systems are, their hardware has a limited number of resources, especially when it comes to storage capacity and memory. It’s not economical or practical to handle unbounded data with traditional approaches.

Stream processing can use a number of techniques to process unbounded data. It partitions data streams by taking a current fragment so they can become fixed chunks of records that can be analyzed. Based on the use case, this current fragment can be from the last two minutes, the last hour, or even the last 200 events. This fragment is referred to as a window. You can use different techniques to window data and process the windowing outcomes.

Next, data manipulation is applied to data accumulated in a window. This can include the following:

• Basic operations (e.g., filter)
• Aggregate (e.g., sum, min, max)
• Fold/reduce

This way, each window has a result value.

Stream Processing Architecture

A stream processing architecture can include the following components:

• Stream processor: A stream producer (also known as a message broker) uses an API to fetch data from a producer — a data source that emits streams to the stream processor. The processor converts this data into a standard messaging format and streams this output regularly to a consumer.
• Real-time ETL tools: Real-time ETL tools collect data from a stream processor to aggregate, transform, and structure it. These operations ensure that your data can be made ready for analysis.
• Data analytics tool: Data analytics tools help analyze your streaming data after it’s aggregated and structured properly. For instance, if you need to send streaming events to applications without compromising on latency, then you can process and persist your streams into a cluster in Cassandra. You can set up an instance in Apache Kafka to send outputs of streams of changes to your apps for real-time decision making.
• Data storage: You can save your streaming data into a number of storage mediums. This can be a message broker, data warehouse, or data lake. For example, you can store your streaming data on Snowflake, which lets you perform real-time analytics with BI tools and dashboards.

Advantages of Stream Processing

Stream processing isn’t right for every organization. After all, not everyone needs real-time data. But for those who do, stream processing is essential. It makes the entire process of dealing with real-time data much smoother and more efficient. Here are some more benefits you can get from stream processing. 

1. Easier to deal with continuous streams. With batch processing, you have to stop collecting data at some point and process it. This creates the need for a cycle of accept, aggregate, and process, which can be prohibitively complicated and increase overhead. Stream processing can identify patterns, examine results, and easily show data from several streams at once. 
2. Can be done with affordable hardware. Batch processing allows data to accumulate and then process it, which might require powerful hardware. Stream processing deals with data as soon as it arrives and doesn’t let it build up, which is why it can be done without the need for costly hardware. 
3. Deal with large amounts of data. Sometimes, there’s a large amount of data that can’t be stored. In these scenarios, stream processing helps you process data and retain the useful bits.
4. Handle the latest data sources. With the rise of IoT, there’s an increase in streaming data, which comes from a wide range of sources. Stream processing’s inherent architecture makes it a natural solution to deal with these sources. 

Stream Processing Frameworks

A stream processing framework is a comprehensive processing system that collects streaming data as input via a dataflow pipeline and produces real-time analytics by delivering actionable insights. These frameworks save you from going through the hassle of building a solution to implement stream processing. 

Before you get started with a stream processing framework, you need to make sure it can meet your needs. Here’s what you should consider:

• Does the framework support stateful stream processing?
• Does the framework support both batch and stream processing functionalities?
• Does the framework offer support for your developers’ desired programming languages? 
• How does the framework fare in terms of scalability?
• How does the framework deal with fault tolerance and crashes?
• How easy is the framework to build upon? 
• How quickly can your developers learn to use the framework?

Answering these questions will ensure you end up with a stream processing framework that fulfills all of your needs. You don’t want to pay for new hardware to find it only does half of what you want. The following three frameworks are some of the most popular options available and fulfill each of the criteria above.

Apache Spark

Apache Spark is an analytics engine that’s built to process big data workloads on an enterprise scale. The core Spark API contains Spark Streaming, an extension that supports stream processing data streams at high throughput. It ingests data from a wide range of sources, such as TCP sockets, Kineses, and Kafka. 

Spark Streaming collects real-time streams and divides them into batches of data with regular time intervals. A typical interval is between 500 milliseconds to several seconds. Spark Streaming comes with an abstraction known as DStream (Discretized Stream) to represent continuous data streams. 

You can use high-level functions like window, join, and reduce to process data with complex algorithms. This data can then be sent to live dashboards, databases, and file systems, where you can use Spark’s graph processing and machine learning algorithms on your streams. 

Kafka Streams

Kafka Streams is a Java API that processes and transforms data stored in Kafka topics. You can use it to filter data in a topic and publish it to another. Think of it as a Java-powered toolkit that helps you to modify Kafka messages in real time before they’re sent to external consumers. 

A Kafka Stream is made of the following components:

• Source processors: Used for representing a topic in Kafka and can send event data to one or several stream processors.
• Stream processors: Used to perform data transformations, such as mapping, counting, and grouping, on input data streams. 
• Sink processors: Used for representing the output streams and are connected to a topic. 
• Topology: A graph that the Kafka Streams instance uses to figure out the relationship between sources, processors, and sinks. 

Apache Flink

Apache Flink is an open-source distributed framework that offers stream processing for large volumes of data. It provides low latency and high throughput while also supporting horizontal scaling. 

DataStream API is the primary API in Flink for stream processing. It helps you write programs in Python, Scala, and Java to perform data transformations on streams. These streams can come from various sources at once, such as files, socket streams, or message queues. The output of these streams is routed through Data Sinks, so you write this data to a target, like distributed files. 

Flink doesn’t only offer runtime operators for unbounded data — it also comes with specialized operators to process bounded data by configuring the Table API or DataSet API in Flink. That means you can use Flink for both stream processing and batch analytics. 

Streaming SQL

In stream processing, you can’t use normal SQL for writing queries. You can write SQL queries for bounded data that are stored in a database at the moment, but you can’t use these queries for real-time streams. For that, you need a special type of SQL known as Streaming SQL.

Streaming SQL helps you write queries for stored data as well as data that are expected to come in the future. That’s why these queries never stop running and continuously generate results as streams. For instance, if a manufacturing plant is using sensors to record temperature data for its machinery, you can represent this output as a stream. Normal SQL queries will collect stored data from your machinery’s database table, process it, and send it to the target system. Streaming SQL not only ingests stored data but also collects new data from your sensor and continuously produces it as output in real time. 

Learn more about streaming SQL in detail here.

Stream Processing Use Cases

The ability of stream processing architectures to analyze real-time data can have a major impact in several areas.

Fraud detection

Stream processing architectures can be pivotal in discovering, alerting, and managing fraudulent activities. They go through time-series data to analyze user behavior and look for suspicious patterns. This data can be ingested through a data ingestion tool (e.g., Striim) and can include the following:

• User identity (e.g., phone number)
• Behavioral patterns (e.g., browsing patterns)
• Location (e.g., shipping address)
• Network and device (e.g., IP information, device model)

This data is then processed and analyzed to find hidden fraud patterns. For example, a retailer can process real-time streams to identify credit card fraud during the point of sale. To do this, it can correlate customers’ interactions with different channels and transactions. In this way, any transaction that’s unusual or inconsistent with a customer’s behavior (e.g., using a shipping address from a different country) can be reviewed instantly.

Hyper-personalization

Accenture found that 91% of buyers are more likely to purchase from brands that offer personalized recommendations. Today, businesses need to improve their customer experience by introducing workflows that automate personalization.

Personalization with batch processing has some limitations. Since it uses historical data, it fails to take advantage of data that provides insights into a user’s real-time interactions that are happening at the very moment. In addition, it fails at hyper-personalization since it’s unable to use these real-time streams with customers’ existing data.

Let’s take a seller that deals in computer hardware. Their target market includes both office workers and gamers. With stream processing, the seller can process real-time data to determine which visitors are office workers who need hardware like printers, and which are gamers who are more likely to be looking for graphic cards that can run the latest games.

Log analysis

Log analysis is one of the processes that engineering teams use to identify bugs by reviewing computer-generated records (also known as logs).

In 2009, PayPal’s network infrastructure faced a technical issue, causing it to go offline for one hour. This downtime led to a loss of transactions worth $7.2 million. In such circumstances, engineering teams don’t have a lot of time; they have to quickly find the root cause of the failure via log analysis. To do this, their methods of collecting, analyzing, and understanding data in real time are key to solving the issue. Stream processing architecture makes it a natural solution. Today, PayPal uses stream processing frameworks and processed 5.34 billion payments in the fourth quarter of 2021.

Stream processing can improve log analysis by collecting raw system logs, classifying their structure, converting them into a consistent and standardized format, and sending them to other systems.

Usually, logs contain basic information like the operation performed, network address, and time. Stream processing can add meaning to this data by identifying log data related to remote/local operations, authentication, and system events. For instance, the original log stores user IP addresses but doesn’t tell their physical location. Stream processing can collect geolocation data to identify their location and add it to your systems.

Sensor data

Sensor-powered devices collect and send large amounts of data quickly, which is valuable to organizations (e.g. for predictive maintenance). They can measure a wide variety of data, such as air quality, electricity, gasses, time of flight, luminance, air pressure, humidity, temperature, and GPS. After this data is collected, it must be transmitted to remote servers where it can be processed. One of the challenges that occurs during this process is the processing of millions of records sent by the device’s sensors every second. You might also need to perform different operations like filtering, aggregating, or discarding irrelevant data.

Stream processing systems can process data from sensors, which includes data integration from different sources, and perform various actions, like normalizing data and aggregating it. To transform this data into meaningful events, it can use a number of techniques, including:

Assessment: Storing all data from sensors isn’t practical since a lot of it isn’t relevant. Stream processing applications can standardize this data after collecting it and perform basic transformations to determine if any further processing is required. Irrelevant data is then discarded, saving processing bandwidth.

Aggregation: Aggregation involves performing a calculation on a set of values to return a single output. For instance, let’s say a handbag company wants to identify fraudulent gift card use by looking over its point-of-sale (POS) machine’s sensor data. It can set a condition that tells it when gift card redemptions cross the $1,000 limit within 15 minutes. It can use stream processing to aggregate metrics continuously by using a sliding time window to look for suspicious patterns. A sliding time window is used to group records from a data stream over a specific period. A sliding window of a length of one minute and a sliding interval of 15 minutes will contain records that arrive in a one-minute window and are evaluated every 15 minutes.

Correlation: With stream processing, you can connect to streams over a specific interval to determine how a series of events occurred. For instance, in our POS example, you can set a rule that condition x is followed by conditions y and z. This rule can include an alert that notifies the management as soon as gift card redemptions in one of the outlets are 300% more than the average of other outlets.

Striim: A Unified Stream Processing and Real-time Data Integration Platform

If you’re looking to improve your organization’s processing and management of streaming data, stream processing can be a good solution. However, you need to make sure you have the right tools to effectively implement stream processing. Striim can be your go-to tool for ingesting, processing, and analyzing real-time data streams. As a unified data integration and streaming platform — with over 150 connectors to data sources and targets — Striim brings many capabilities under one roof.

Striim can perform various operations on data streams, such as filtering, masking, aggregation, and transformation. Furthermore, streaming data can be enriched with in-memory, refreshable caches of historical data. WAction Store, a fault-tolerant, distributed results store, maintains an aggregate state. WAction Stores can be continuously queried with Tungsten Query Language (TQL), Striim’s own streaming SQL engine. TQL is 2-3x faster than Kafka’s KSQL and can help you to write queries more efficiently. Streaming data can also be visualized with custom dashboards (e.g., to detect cab-booking hotspots).

Ready to learn more about Striim for real-time data integration and stream processing? Get a product overview, request a personalized demo with one of our product experts, or read our documentation.

 

According to a Gartner prediction, only 20% of data analytics projects will deliver business outcomes. Indeed, given that the current data architectures are not well equipped to handle data’s ubiquitous and increasingly complex interconnected nature, this is not surprising. So, in a bid to address this issue, the question on every company’s lips remains — how can we properly build our data architecture to maximize data efficiency for the growing complexity of data and its use cases?

First defined in 2018 by Zhamak Dehghani, Head of Emerging Technologies at Thoughtworks, the data mesh concept is a new approach to enterprise data architecture that aims to address the pitfalls of the traditional data platforms. Organizations seeking a data architecture to meet their ever-changing data use cases should consider the data mesh architecture to power their analytics and business workloads.

What Is a Data Mesh?

A data mesh is an approach to designing modern distributed data architectures that embrace a decentralized data management approach. The data mesh is not a new paradigm but a new way of looking at how businesses can maximize their data architecture to ensure efficient data availability, access, and management.

How does a data mesh differ from traditional data architectures?

Rather than viewing data as a centralized repository, a data mesh’s decentralized nature distributes data ownership to domain-specific teams that manage, control, and deliver data as a product, enabling easy accessibility and interconnectivity of data across the business.

Today most companies’ data use cases can be split into operational and analytical data. Operational data represents data from the company applications’ day-to-day operations. For example, using an e-commerce store, this will mean customer, inventory, and transaction data. This operational data type is usually stored in databases and used by developers to create various APIs and microservices to power business applications.

Operational vs. analytical data plane

On the other hand, analytical data represents historical organizational data used to enhance business decisions. In our e-commerce store example, analytical data answers questions such as “how many customers have ordered this product in the last 20 years?” or “what products are customers likely to buy in the winter season?” Analytical data is usually transported from multiple operational databases using ETL (Extract, Transform, and Load) techniques to centralized data stores like data lakes and warehouses. Data analysts and scientists use it to power their analytics workloads, and product and marketing teams can make effective decisions with the data.

A data mesh understands the difference between the two broad types of data and attempts to connect these two data types under a different structure — a decentralized approach to data management. A data mesh challenges the idea of the traditional centralization of data into a single big storage platform.

What are the four principles of a data mesh, and what problems do they solve?

A data mesh is technology agnostic and underpins four main principles described in-depth in this blog post by Zhamak Dehghani. The four data mesh principles aim to solve major difficulties that have plagued data and analytics applications for a long time. As a result, learning about them and the problems they were created to tackle is important.

Domain-oriented decentralized data ownership and architecture

This principle means that each organizational data domain (i.e., customer, inventory, transaction domain) takes full control of its data end to end. Indeed, one of the structural weaknesses of centralized data stores is that the people who manage the data are functionally separate from those who use it. As a result, the notion of storing all data together within a centralized platform creates bottlenecks where everyone is mainly dependent on a centralized “data team” to manage, leading to a lack of data ownership. Additionally, moving data from multiple data domains to a central data store to power analytics workloads can be time consuming. Moreover, scaling a centralized data store can be complex and expensive as data volumes increase.

There is no centralized team managing one central data store in a data mesh architecture. Instead, a data mesh entrusts data ownership to the people (and domains) who create it. Organizations can have data product managers who control the data in their domain. They’re responsible for ensuring data quality and making data available to those in the business who might need it. Data consistency is ensured through uniform definitions and governance requirements across the organization, and a comprehensive communication layer allows other teams to discover the data they need. Additionally, the decentralized data storage model reduces the time to value for data consumers by eliminating the need to transport data to a central store to power analytics. Finally, decentralized systems provide more flexibility, are easier to work on in parallel, and scale horizontally, especially when dealing with large datasets spanning multiple clouds.

Data as a product 

This principle can be summarized as applying product thinking to data. Product thinking advocates that organizations must treat data with the same care and attention as customers. However, because most organizations think of data as a by-product, there is little incentive to package and share it with others. For this reason, it is not surprising that 87% of data science projects never make it to production.

Data becomes a first-class citizen in a data mesh architecture with its development and operations teams behind it. Building on the principle of domain-oriented data ownership, data product managers release data in their domains to other teams in the form of a “product.” Product thinking recognizes the existence of both a “problem space” (what people require) and a “solution space” (what can be done to meet those needs). Applying product thinking to data will ensure the team is more conscious of data and its use cases. It entails putting the data’s consumers at the center, recognizing them as customers, understanding their wants, and providing the data with capabilities that seamlessly meet their demands. It also answers questions like “what is the best way to release this data to other teams?” “what do data consumers want to use the data for?” and “what is the best way to structure the data?”

Self-serve data infrastructure as a platform

The principle of creating a self-serve data infrastructure is to provide tools and user-friendly interfaces so that generalist developers (and non-technical people) can quickly get access to data or develop analytical data products speedily and seamlessly. In a recent McKinsey survey, organizations reported spending up to 80% of their data analytics project time on repetitive data pipeline setup, which ultimately slowed down the productivity of their data teams.

The idea of the self-serve data infrastructure as a platform is that there should be an underlying infrastructure for data products that the various business domains can leverage in an organization to get to the work of creating the data products rapidly. For example, data teams should not have to worry about the underlying complexity of servers, operating systems, and networking. Marketing teams should have easy access to the analytical data they need for campaigns. Furthermore, the self-serve data infrastructure should include encryption, data product versioning, data schema, and automation. A self-service data infrastructure is critical to minimizing the time from ideation to a working data-driven application.

Federated computational governance 

This principle advocates that data is governed where it is stored. The problem with centralized data platforms is that they do not account for the dynamic nature of data, its products, and its locations. In addition, large datasets can span multiple regions, each having its own data laws, privacy restrictions, and governing institutions. As a result, implementing data governance in this centralized system can be burdensome.

The data mesh more readily acknowledges the dynamic nature of data and allows for domains to designate the governing structures that are most suitable for their data products. Each business domain is responsible for its data governance and security, and the organization can set up general guiding principles to help keep each domain in check.

While it is prescriptive in many ways about how organizations should leverage technology to implement data mesh principles, perhaps the more significant implementation challenge is how that data flows between business domains.

Why Are Event Streams a Good Fit for Building a Data Mesh?

Event streams are a continuous flow of “events” known as data points that flow from systems that generate data to systems that consume that data for different workloads. In our online store example, when a customer places an order, that “order event” is propagated to the various consumers who listen to that event. The consumer could be a checkout service to process the order, an email service that sends out confirmation emails, or an analytics service carrying out real-time customer order behaviors.

Event streams offer the best option for building a data mesh, mainly when the data involved is used by multiple teams with unique needs across an organization. Because event streams are published in real time, streams enable immediate data propagation across the data mesh. Additionally, event streams are persisted and replayable, so they let you capture both real-time and historical data with one infrastructure. Finally, because the stored events don’t change, they make for a great source of record, which is helpful for data governance.

Three common streaming patterns in a data mesh

In our work with Striim customers, we tend to see three common streaming patterns in a data mesh.

In the first pattern, data is streamed from legacy systems to create new data products on a self-service infrastructure (commonly on a public cloud). For example, medical records data can be streamed from on-premise EHR (electronic health records) systems to a real-time analytics system like GoogleBigQuery, to feed cloud applications used by doctors. In the meantime, operational monitoring applications on the data pipeline help to ensure that pipelines are operating as expected.

In the second pattern, data is also consumed as it moves along the pipeline. Data is processed (e.g. by continuous queries or window-based views) to create “data as a product applications”. For example, a financial institution may build a fraud detection application that analyzes streaming data to identify potential fraud in real time.

Once you have a data product (e.g. freshly analyzed data), you can share it with another data product (e.g. the original data source or day-to-day business applications like Salesforce). This pattern, also known as reverse ETL, enables companies to have actionable information at their points of engagement, allowing for more intelligent interactions.

How to build a data mesh with event streams

To build a data mesh, you need to understand the different components (and patterns) that make up the enterprise data mesh architecture. In this article, Eric Broda gives a detailed overview of data mesh architectural patterns, bringing much-needed clarity to the “how” of a data mesh.

• Data Product APIs: This is the communication layer that makes data within a data product accessible via a contract that is consistent, uniform, and compliant to industry-standard specifications (REST API, GraphQL, MQTT, gRPC).
• Change Data Capture: This is used by an enterprise data mesh to track when data changes in a database. These database transaction changes are captured as “events.”
• Event Streaming Backbone: This concept is used to communicate CDC (Change Data Capture) events and other notable events (for example, an API call to the Data Mesh) to interested consumers (within and between Data Products) in an enterprise data mesh.
• Enterprise Data Product Catalog: This repository allows developers and users to view metadata about data products in the enterprise data mesh.
• Immutable Change/Audit Log: This retains data changes within the enterprise data mesh for future audit and governance purposes.

Still building on our e-commerce example, let’s walk through how these components could  operate in a real-world data mesh scenario. For example, say our retail company has both a brick-and-mortar and online presence, but they lack a single source of truth regarding inventory levels. Disjointed systems in their on-premises data center can result in disappointing customer experiences. For example, customers shopping online may be frustrated to discover out-of-stock items that are actually available in their local store.

The retailer’s goal is to move towards an omnichannel, real-time customer experience, where customers can get a seamless experience no matter where (or when) they place their order. In addition, the retailer needs real-time visibility into inventory, to maintain optimal inventory levels at any point in time (including peak shopping seasons like Black Friday/Cyber Monday).

A data mesh suits this use case perfectly, and allows them to keep their on-premises data center running without disruption. Here’s how they can build a data mesh with Striim’s unified streaming and integration platform.

A data mesh in practice, using Striim

• Operational applications update data in the on-premises inventory, pricing, and catalog databases (e.g. when an online order is placed, the appropriate database is updated)
• Striim’s change data capture (CDC) reader continuously reads the operational database transaction logs, creating database change streams that can be persisted and replayed via a native integration with Kafka
• Striim performs in-memory processing of the event streams, allowing for detection and transformation of mismatched data (e.g. mismatched timestamp fields). Data is continuously synced and validated. Furthermore, Striim automatically detects schema changes in the source databases, either propagating them or alerting users of an issue. All this happens with sub-second latency to ensure that any consumer of data in the mesh has fresh and accurate data.
• Events are streamed to a unified data layer in the cloud, to both lake storage and an inventory database, with the flexibility to add any number of self-service systems (streaming data consumers) to provide an optimal customer experience and support real-time decision-making. So an online customer who wants to pick up an item at their local store can do so without a hitch. A returning customer can be offered a personalized coupon to encourage them to add more items to their order.
• The retail company can integrate Striim with a solution like Confluent’s schema catalog, making it easier to classify, organize, and find event streams

Use Striim as the Event Ingestion and Streaming Backbone of Your Data Mesh

A data mesh unlocks endless possibilities for organizations for various workloads, including analytics and building data-intensive applications. Event streams offer the best communication medium for implementing a data mesh. They provide efficient data access to all data consumers and bridge the operational/analytical divide, giving batch and streaming users a real-time, fast, and consistent view of data.

Striim has all the capabilities to build a data mesh using event streams, as shown above. Striim makes it easy to create new streams for data product owners with a simple streaming SQL query language and role-based access to streams. Additionally, Striim provides real-time data integration, connecting over 150 sources and targets across hybrid and multi-cloud environments.

 

What is Data Replication

Data replication involves creating copies of data and storing them on different servers or sites. This results in multiple, identical copies of data being stored in different locations.

Data Replication Benefits

Data replication makes data available on multiple sites, and in doing so, offers various benefits.

First of all, it enables better data availability. If a system at one site goes down because of hardware issues or other problems, users can access data stored at other nodes. Furthermore, data replication allows for improved data backup. Since data is replicated to multiple sites, IT teams can easily restore deleted or corrupted data.

Data replication also allows faster access to data. Since data is stored in various locations, users can retrieve data from the closest servers and benefit from reduced latency. Also, there’s a much lower chance that any one server will become overwhelmed with user queries since data can be retrieved from multiple servers. Data replication also supports improved analytics, by allowing data to be continuously replicated from a production database to a data warehouse used by business intelligence teams.

Replicating data to the cloud

Replicating data to the cloud offers additional benefits. Data is kept safely off-site and won’t be damaged if a major disaster, such as a flood or fire, damages on-site infrastructure. Cloud replication is also cheaper than deploying on-site data centers. Users won’t have to pay for hardware or maintenance.

Replicating data to the cloud is a safer option for smaller businesses that may not be able to afford full-time cybersecurity staff. Cloud providers are constantly improving their network and physical security. Furthermore, cloud sites provide users with on-demand scalability and flexibility. Data can be replicated to servers in different geographical locations, including in the nearby region.

Data Replication Challenges

Data replication technologies offer many benefits, but IT teams should also keep in mind several challenges.

First of all, keeping replicated data at multiple locations leads to rising storage and processing costs. In addition, setting up and maintaining a data replication system often requires assigning a dedicated internal team.

Replicating data across multiple copies requires deploying new processes and adding more traffic to the network. Finally, managing multiple updates in a distributed environment may cause data to be out of sync on occasion. Database administrators need to ensure consistency in replication processes.

Data Replication Methods

The data replication strategy you choose is crucial as it impacts how and when your data is loaded from source to replica and how long it takes. An application whose database updates frequently wouldn’t want a data replication strategy that could take too long to reproduce the data in the replicas. Similarly, an application with less frequent updates wouldn’t require a data replication strategy that reproduces data in the replicas several times a day.

Log-Based Incremental Replication

Some databases allow you to store transaction logs for a variety of reasons, one of which is for easy recovery in case of a disaster. However, in log-based incremental replication, your replication tool can also look at these logs, identify changes to the data source, and then reproduce the changes in the replica data destination (e.g., database). These changes could be INSERT, UPDATE, or DELETE operations on the source database.

The benefits of this data replication strategy are:

• Because log-based incremental replication only captures row-based changes to the source and updates regularly (say, once every hour), there is low latency when replicating these changes in the destination database.
• There is also reduced load on the source because it streams only changes to the tables.
• Since the source consistently stores changes, we can trust that it doesn’t miss vital business transactions.
• With this data replication strategy, you can scale up without worrying about the additional cost of processing bulkier data queries.

Unfortunately, a log-based incremental replication strategy is not without its challenges:

• It’s only applicable to databases, such as MongoDB, MySQL, and PostgreSQL, that support binary log replication.
• Since each of these databases has its own log formats, it’s difficult to build a generic solution that covers all supported databases.
• In the case where the destination server is down, you have to keep the logs up to date until you restore the server. If not, you lose crucial data.

Despite its challenges, log-based incremental replication is still a valuable data replication strategy because it offers fast, secure, and reliable replication for data storage and analytics.

Key-Based Incremental Replication

As the name implies, key-based replication involves replicating data through the use of a replication key. The replication key is one of the columns in your database table, and it could be an integer, timestamp, float, or ID.

Key-based incremental replication only updates the replica with the changes in the source since the last replication job. During data replication, your replication tool gets the maximum value of your replication key column and stores it. During the next replication, your tool compares this stored maximum value with the maximum value of your replication key column in your source. If the stored maximum value is less than or equal to the source’s maximum value, your replication tool replicates the changes. Finally, the source’s maximum value becomes the stored value.

This process is repeated for every replication job that is key-based, continually using the replication key to spot changes in the source. This data replication strategy offers similar benefits as log-based data replication but comes with its own limitations:

• It doesn’t identify delete operations in the source. When you delete a data entry in your table, you also delete the replication key from the source. So the replication tool is unable to capture changes to that entry.
• There could be duplicate rows if the records have the same replication key values. This occurs because key-based incremental replication also compares values equal to the stored maximum value. So it duplicates the record until it finds another record of greater replication key.

In cases where log-based replication is not feasible or supported, key-based replication would be a close alternative. And knowing these limitations would help you better tackle data discrepancies where they occur.

Full Table Replication

Unlike the incremental data replication strategies that update based on changes to logs and the replication key maximum value, full table replication replicates the entire database. It copies everything: every new, existing, and updated row, from source to destination. It’s not concerned with any change in the source; whether or not some data changes, it replicates it.

The full table data replication strategy is useful in the following ways:

• You’re assured that your replica is a mirror image of the source and no data is missing.
• Full table replication is especially useful when you need to create a replica in another location so that your application’s content loads regardless of where your users are situated.
• Unlike key-based replication, this data replication strategy detects hard deletes to the source.

However, replicating an entire database has notable downsides:

• Because of the high volume of data replicated, full-table replication could take longer, depending on the strength of your network.
• It also requires higher processing power and can cause latency duplicating that amount of data at every replication job.
• The more you use full table replication to replicate to the same database, the more rows you use and the higher the cost to store all that data.
• Low latency and high processing power while replicating data may lead to errors during the replication process.

Although full table replication isn’t an efficient way to replicate data, it’s still a viable option when you need to recover deleted data or there aren’t any logs or suitable replication keys.

Snapshot Replication

Snapshot replication is the most common data replication strategy; it’s also the simplest to use. Snapshot replication involves taking a snapshot of the source and replicating the data at the time of the snapshot in the replicas.

Because it’s only a snapshot of the source, it doesn’t track changes to the source database. This also affects deletes to the source. At the time of the snapshot, the deleted data is no longer in the source. So it captures the source as is, without the deleted record.

For snapshot replication, we need two agents:

• Snapshot Agent: It collects the files containing the database schema and objects, stores them, and records every sync with the distribution database on the Distribution Agent.
• Distribution Agent: It delivers the files to the destination databases.

Snapshot replication is commonly used to sync the source and destination databases for most data replication strategies. However, you may use it on its own, scheduling it according to your custom time.

Just like the full table data replication strategy, snapshot replication may require high processing power if the source has a considerably large dataset. But it is useful if:

• The data you want to replicate is small.
• The source database doesn’t update frequently.
• There are a lot of changes in a short period, such that transactional or merge replication wouldn’t be an efficient option.
• You don’t mind having your replicas being out of sync with your source for a while.

Transactional Replication

In transactional replication, you first duplicate all existing data from the publisher (source) into the subscriber (replica). Subsequently, any changes to the publisher replicate in the subscriber almost immediately and in the same order.

It is important to have a snapshot of the publisher because the subscribers need to have the same data and database schema as the publisher for them to receive consistent updates. Then the Distribution Agent determines the regularity of the scheduled updates to the subscriber.

To perform transactional replication, you need the Distribution Agent, Log Reader Agent, and Snapshot Agent.

• Snapshot Agent: It works the same as the Snapshot Agent for snapshot replication. It generates all relevant snapshot files.
• Log Reader Agent: It observes the publisher’s transaction logs and duplicates the transactions in the distribution database.
• Distribution Agent: It copies the snapshot files and transaction logs from the distribution database to the subscribers.
• Distribution database: It aids the flow of files and transactions from the publisher to the subscribers. It stores the files and transactions until they’re ready to move to the subscribers.

Transactional replication is appropriate to use when:

• Your business can’t afford downtime of more than a few minutes.
• Your database changes frequently.
• You want incremental changes in your subscribers in real time.
• You need up-to-date data to perform analytics.

In transactional replication, subscribers are mostly used for read purposes, and so this data replication strategy is commonly used when servers only need to talk to other servers.

Merge Replication

Merge replication combines (merges) two or more databases into one so that updates to one (primary) database are reflected in the other (secondary) databases. This is one key trait of merge replication that differentiates it from the other data replication strategies. A secondary database may retrieve changes from the primary database, receive updates offline, and then sync with the primary and other secondary databases once back online.

In merge replication, every database, whether it’s primary or secondary, can make changes to your data. This can be useful when one database goes offline and you need the other to operate in production, then get the offline database up to date once it’s back online.

To avoid data conflicts that may arise from allowing modifications from secondary databases, merge replication allows you to configure a set of rules to resolve such conflicts.

Like most data replication strategies, merge replication starts with taking a snapshot of the primary database and then replicating the data in the destination databases. This means that we also begin the merge replication process with the Snapshot Agent.

Merge replication also uses the Merge Agent, which commits or applies the snapshot files in the secondary databases. The Merge Agent then reproduces any incremental updates in the other databases. It also identifies and resolves all data conflicts during the replication job.

You may opt for merge replication if:

• You’re less concerned with how many times a data object changes but more interested in its latest value.
• You need replicas to update and reproduce the updates in the source and other replicas.
• Your replica requires a separate segment of your data.
• You want to avoid data conflicts in your database.

Merge replication remains one of the most complex data replication strategies to set up, but it can be valuable in client-server environments, like mobile apps or applications where you need to incorporate data from multiple sites.

Bidirectional Replication

Bidirectional replication is one of the less common data replication strategies. It is a subset of transactional replication that allows two databases to swap their updates. So both databases permit modifications, like merge replication. However, for a transaction to be successful, both databases have to be active.

Here, there is no definite source database. Each database may be from the same platform (e.g., Oracle to Oracle) or from separate platforms (e.g., Oracle to MySQL). You may choose which rows or columns each database can modify. You may also decide which database is a higher priority in case of record conflicts, i.e., decide which database updates are reflected first.

Bidirectional replication is a good choice if you want to use your databases to their full capacity and also provide disaster recovery.

Your Data Replication Strategy Shouldn’t Slow You Down: Try Striim

Regardless of your type of application, there’s a data replication strategy that best suits your business needs. Combine any data replication strategies you want. Just ensure that the combination offers a more efficient way to replicate your databases according to your business objectives.

Every data replication strategy has one cost in common: the time it takes. Few businesses today can afford to have their systems slowed down by data management, so the faster your data replicates, the less negative impact it will have on your business.

Replicating your database may be time-consuming, and finding the right data replication tool to help speed up and simplify this process, while keeping your data safe, can be beneficial to your business.

For fast, simple, and reliable data replication, Striim is your best bet. Striim provides real-time data replication by extracting data from databases using log-based change data capture and replicating it to targets in real time. Regardless of where your data is, Striim gets your data safely to where you need it to be and shows you the entire process, from source to destination.

Schedule a demo and we’ll give you a personalized walkthrough or try Striim at production-scale for free! Small data volumes or hoping to get hands on quickly? At Striim we also offer a free developer version.

Since announcing our Series C fundraising led by Goldman Sachs we doubled down on our mission to enable companies to power their decisions in real-time, and after a year in private preview, collecting feedback from customers, testing workloads and tweaking and adjusting, we’re thrilled to announce the public launch of Striim Cloud: the industry’s first and only unified data streaming and integration fully managed service. Striim Cloud was uniquely designed to address the challenges of enterprise data streaming with an emphasis on our best-in-market change data capture, fully dedicated infrastructure (no shared data for sensitive environments), and seamless interoperability with on-premise and self-managed versions of Striim.

Unlike other solutions in the market, Striim Cloud leverages over 5 years of experience gained from delivering our self-managed, massively scalable streaming platform to over 100 enterprise customers and 2500 deployments in 6 continents. Striim is also led by the executive team behind GoldenGate Software.

The power of Striim Cloud is also a result of collaborating closely with incredible partners like Microsoft. “Microsoft is committed to making migration to Azure as smooth as possible, while paving the way for continuous innovation for our customers. Our goal is to build technology that empowers today’s innovators to unleash the power of their data and explore possibilities that will improve their businesses and our world,” said Rohan Kumar, Corporate Vice President, Azure Data at Microsoft. “We are pleased to work with Striim to provide our customers with a fast way to replicate their data to the Azure platform and gain mission-critical insights into data from across the organization.”

With that collaboration, we’ve made Striim Cloud available with consumption-based pricing on the Azure marketplace. Microsoft Azure customers can leverage existing investments in the Azure ecosystem to power digital transformation initiatives with real-time data.

With Striim Cloud we’re offering unprecedented speed and simplicity with the following value-adds:

• Fast setup with schema conversion and initial load into your analytics platforms
• Low impact change data capture built by the team from GoldenGate
• Meet fast data SLAs (sub-second delivery) with fast data streaming and end-to-end lag monitoring
• Low cost of ownership with fully managed, fully dedicated cloud infrastructure by Striim
• Enterprise-level security with encrypted data at-rest and in-flight
• Consumption-based pricing; pay only for the data you successfully move from source to target and the compute you need in that moment

But don’t take my word for it, sign up for a free trial and start powering your decisions with real-time data.

 

According to a study by Experian, 98% of companies rely on data to enhance their customer experience. In today’s data age, getting data analytics right is more essential than ever. Organizations compete based on how effective their data-driven insights are at helping them with informed decision-making.

However, executing analytics projects is a bane for many. According to Gartner, more than 60% of data analytics projects bite the dust due to fast-moving and complex data landscapes.

Recognizing the modern data challenges, organizations are adopting DataOps to help them handle enterprise-level datasets, improve data quality, build more trust in their data, and exercise greater control over their data storage and processes.

What Is DataOps?

DataOps is an integrated and Agile process-oriented methodology that helps you develop and deliver analytics. It is aimed at improving the management of data throughout the organization.

There are multiple definitions of DataOps. Some think it’s a magic bullet that solves all data management issues. Others think that it just introduces DevOps practices for building data pipelines. However, DataOps has a broader scope that goes beyond data engineering. Here’s how we define it:

DataOps is an umbrella term that can include processes (e.g., data ingestion), practices (e.g., automation of data processes), frameworks (e.g., enabling technologies like AI), and technologies (e.g., a data pipeline tool) that help organizations to plan, build, and manage distributed and complex data architectures. This includes management, communication, integration and development of data analytics solutions, such as dashboards, reports, machine learning models, and self-service analytics.

DataOps aims to eliminate silos between data, software development, and DevOps teams. It encourages line-of-business stakeholders to coordinate with data analysts, data scientists, and data engineers.

The goal of DataOps is to use Agile and DevOps methodologies to ensure that data management aligns with business goals. For instance, an organization sets a target to increase their lead conversion rate. DataOps can make a difference by creating an infrastructure that provides real-time insights to the marketing team, which can convert more leads.

In this scenario, an Agile methodology can be useful for data governance, where you can use iterative development to develop a data warehouse. Likewise, it can help data science teams use continuous integration and continuous delivery (CI/CD) to build environments for the analysis and deployment of models.

DataOps Can Handle High Data Volume and Versatility

Companies have to tackle high amounts of data compared to a few years ago. They have to process it in a wide range of formats (e.g., graphs, tables, images), while their frequency of using that data varies, too. For example, some reports might be required daily, while others are needed on a weekly, monthly, or ad-hoc basis. DataOps can handle these different types of data and tackle varying big data challenges.

With the advent of the Internet of Things (IoT), organizations have to tackle the demons of heterogeneous data as well. This data comes from wearable health monitors, connected appliances, and smart home security systems.

To manage the incoming data from different sources, DataOps can use data analytics pipelines to consolidate data into a data warehouse or any other storage medium and perform complex data transformations to provide analytics via graphs and charts.

DataOps can use statistical process control (SPC) — a lean manufacturing method — to improve data quality. This includes testing data coming from data pipelines, verifying its status as valid and complete, and meeting the defined statistical limits. It enforces the continuous testing of data from sources to users by running tests to monitor inputs and outputs and ensure business logic remains consistent. In case something goes wrong, SPC notifies data teams with automated alerts. This saves them time as they don’t have to manually check data throughout the data lifecycle.

DataOps Can Secure Cloud Data

Around 75% of companies are expected to move their databases into the cloud by 2022. However, many organizations struggle with data protection after migrating their data to the cloud. According to a survey, 70% of companies have to deal with a security breach in the public cloud.

DataOps borrows some of its elements from DevSecOps — short for development, security, and operations. This fusion is also known as DataSecOps, which can help with data protection. DataSecOps brings a security-focused approach where security is embedded in all data operations and projects from the start.

DataSecOps offers security by focusing on five areas:

1. Awareness – Improve the understanding of data sets and their sensitivity by using data dictionaries or data catalogs.
2. Policy – Incorporate and uphold a data access policy that makes it crystal clear who can access data and what form of data they can access.
3. Anonymization – Introduce anonymization into the data access’ security layer, ensuring that business users, who aren’t supposed to view personal identifiable information (PII) data, aren’t able to see it in the first place.
4. Authentication – Provide a user interface for managing data access and tools.
5. Audit – Offer the ability to track, report, and audit access when required, as well as develop and monitor access control.

DataOps Can Improve Time to Value

The time it takes to turn a raw idea into something of value is integral to businesses. DataOps reduces lead time with its Agile-based development processes. The waiting time across phases decreases too. In addition, the approach of building and making releases in small fragments enables solutions to be implemented in a gradual manner.

If you develop data solutions at a slow pace, then it might lead to shadow IT. Shadow IT happens when other departments build their own solutions without the approval or involvement of the IT department.

DataOps can increase your development speed by getting feedback to you faster via sprints. Sprints are short iterations where a team is tasked with completing a specific amount of work. A sprint review occurs at the end of each sprint, which allows continuous feedback from data consumers. This feedback also brings more clarity by allowing the feedback to steer the development and create a solution that your data consumer wants.

DataOps Can Automate Repetitive and Menial Tasks

Around 18% of a data engineer’s time is spent on troubleshooting. DataOps brings a focus to automation to help data professionals save time and focus on more valuable high-priority tasks.

Consider one of the most common tasks in the data management lifecycle: data cleaning. Some data professionals have to manually modify and remove data that is incomplete, duplicate, incorrect, or flawed in any way. This process is repetitive and doesn’t require any critical thinking. You can automate it by either setting your customized scripts or installing a built-in data cleaning software.

Some other processes that can be automated include:

• Simplifying data maintenance tasks like tuning a data warehouse
• Streamlining data preparation tasks with a tool like KNIME
• Improving data validation to identify flags and typos, such as types and range

Build Your Own DataOps Architecture with Striim

To develop your own DataOps architecture, you need a reliable set of tools that can help you improve your data flows, especially when it comes to key aspects of DataOps, like data ingestion, data pipelines, data integration, and the use of AI in analytics. Striim is a unified real-time data integration and streaming platform that integrates with over 100 data sources and targets, including databases, message queues, log files, data lakes, and IoT. Striim ensures the continuous flow of data with intelligent data pipelines that span public and private clouds. To learn more about how you can implement DataOps with Striim, get a free demo today. 

Both data warehouses and data lakes have been serving companies well for a long time. Despite their pros, each also has its limitations. That’s why data architects envision a single system to store and use data for varying workloads. This is where a data lakehouse has emerged as a major problem-solver in the last few years.

A data lakehouse can help organizations move past the limitations of data warehouses and data lakes. It lets them reach a middle ground where they can get the best of both worlds in terms of data storage and data management.

What is a data lakehouse?

A data lakehouse shores up the gaps left by data warehouses and data lakes — two commonly used data architectures. To understand how a data lakehouse works, let’s first take a brief look at data warehouses and data lakes.

Defining data warehouses

A data warehouse collects data from various data sources within an organization to extract information for analysis and reporting. Usually, data warehouses pull data from databases, which have a specific structure known as schema. This data gets processed into a different database format that’s optimized for BI (business intelligence) use cases, where it’s more effective for complex queries.

This data warehouse process has its advantages. It prioritizes certain factors, such as the integrity of the provided data. However, this approach comes with several drawbacks, including the higher costs due to maintenance and vendor lock-in, necessitating the need for more cost-effective data management approaches. 

Defining data lakes

The data lake was invented in 2010 and rapidly gained mainstream adoption throughout the 2010s. Unlike a data warehouse, a data lake is more adept at processing unstructured data, so it can be used for data analytics. This is the data companies can gather from web scraping, web APIs, or files that don’t follow the structure of a relational database.

In addition, data lakes store data at a more affordable rate. That’s because data lake is installed on low-cost hardware and uses open-source software. But data lakes don’t offer all the features offered by a data warehouse. Consequently, contrary to a data warehouse, the data might be lacking in terms of integrity, quality, and consistency.

Combining the advantages of both into a data lakehouse

A data lakehouse offers the best of both worlds by combining the best aspects of data warehouses and data lakes. Similar to a data warehouse, it offers schema support for structured data and keeps data consistent by supporting ACID transactions.

And like data lakes, a data lakehouse can handle unstructured, semi-structured, and structured data. This data can be stored, transformed, and analyzed for text, audio, video, and images. Finally, data lakehouses offer a more affordable method of storing large volumes of data because they utilize the low-cost object storage options of data lakes to cut costs.

What problems can a data lakehouse solve?

Many organizations use data warehouses and data lakes with plenty of success. However, certain problems show up in certain cases.

• Data duplication: If a company uses many data warehouses and a data lake, then it’s bound to create data redundancy — when the same piece of data is stored in two or more separate places. Not only is it inefficient, but it may also cause data inconsistency (when the same data is stored in different versions in more than one table). A data lakehouse can help consolidate everything, remove additional copies of data, and create a single version of truth for the company.
• Siloes between analytics and BI: Data scientists use analytics techniques on data lakes to go through unsorted data, while BI analysts use a data warehouse. A data lakehouse helps both teams to work within a single and shared repository. This aids in reducing data silos.
• Data staleness: According to a survey by Exasol, 58% of companies make decisions based on outdated data. Data warehouses are part of the problem because it is generally expensive to constantly process and refresh real-time data. A data lakehouse supports reliable and convenient integration of real-time streaming along with micro-batches. This makes sure that analysts can always use the latest data.

The common features of a data lakehouse

A data lakehouse aims to improve efficiency by building a data warehouse on data lake technology. According to a paper from Databricks, a data lakehouse does this by providing the following features:

• Extended data types: Data lakehouses have access to a broader range of data than data warehouses, allowing them to access system logs, audio, video, and files.
• Data streaming: Data lakehouses allow enterprises to perform real-time reporting by supporting streaming analytics. Especially when used with streaming data integration products like Striim in concert. 
• Schemas: Unlike data lakes, data lakehouses apply schemas to data, which helps in the standardization of high volumes of data.
• BI and analytics support: BI and analytics professionals can share the same data repository. Since a data lakehouse’s data goes through cleaning and integration, it’s useful for analytics. Also, it can store more updated data than a data warehouse. This enhances BI quality.
• Transaction support: Data lakehouses can handle concurrent write and read transactions and thus can work with several data pipelines.
• Openness: Data lakehouses support open storage formats (e.g., Parquet). This way, data professionals can use R and Python to access it easily.
• Processing/storage decoupling: Data lakehouses reduce storage costs by using clusters that run on cheap hardware. A lakehouse can offer data storage in one cluster and query execution on a separate cluster. This decoupling of processing and storage can help to make the most of resources.

Layers in a data lakehouse

Based on Amazon and Databricks data lakehouse architectures, a data lakehouse can have five layers, as shown below:

 

1- Ingestion layer

The first layer pulls data from multiple data sources and delivers it to the storage layer. The layer uses different protocols to link to a variety of external and internal sources, such as CRM applications, relational databases, and NoSQL databases.

2- Storage layer

The storage layer stores open-source file formats to store unstructured, semi-structured, and structured data. A lakehouse is designed to accept all types of data as objects in affordable object stores (e.g., AWS S3).

You can use open file formats to read these objects via the client tools. As a result, consumption layer components and different APIs can access and work with the same data.

3- Metadata layer

The metadata layer is a unified catalog that encompasses metadata for data lake objects. This layer provides the data warehouse features that are accessible in relational database management systems (RDBMS). For instance, you can create tables, implement upserts, and define features that enhance RDBMS performance.

4- API Layer

This layer is used to host different APIs to allow end-users to process tasks quickly and take advantage of advanced analytics. This layer produces a level of abstraction that enables consumers and developers to get the benefit from using a plethora of languages and libraries. These APIs and libraries are optimized to consume your data assets in your data lake layer (e.g., DataFrames APIs in Apache Spark).

5- Data consumption layer

This layer is used to host different tools and applications, such as Tableau. Client applications can use the data lakehouse architecture to access data stores in the data lake. Employees within a company can use the data lakehouse to perform different analytics activities, such as SQL queries, BI dashboards, and data visualization.

Leverage a data lakehouse for the right use cases

A data lakehouse isn’t a silver bullet that’ll address all your data-related challenges. It can be tricky to build and maintain a data lakehouse due to its monolithic architecture. In addition, its one-size-fits-all design might not always provide the same quality that you can get with other approaches that are designed to tackle more specific use cases. 

On the other hand, there are many scenarios where a data lakehouse can add value to your organization. Data lakehouses can help you to stage all your data in a single tier. You can then optimize this data for various types of queries on unstructured and structured data. For example, if you’re looking to use both AI and BI, then the versatility of a data lakehouse can be useful. You can also use a data lakehouse to address the data inconsistency and redundancy caused by multiple systems. For more details, go through this comparison and decide which data management solution is best for you. 

Data has become one of the most valuable assets in modern times. As more companies rely on insights from data to drive critical business decisions, this data must be accurate, reliable, and of high quality. A study by Gartner predicts that only 20% of analytic insights will deliver business outcomes, with this other study citing poor data quality as the number one reason why the anticipated value of all business initiatives is never achieved.

Gaining insights from data is essential, but it is also crucial to understand the health of the data in your system to ensure the data is not missing, incorrectly added, or misused. That’s where data observability comes in. Data observability helps organizations manage, monitor, and detect problems in their data and data systems before they lead to “data downtimes,” i.e., periods when your data is incomplete or inaccurate.

What is Data Observability?

Data observability refers to a company’s ability to understand the state of its data and data systems completely. With good data observability, organizations get full visibility into their data pipelines. Data observability empowers teams to develop tools and processes to understand how data flows within the organization, identify data bottlenecks, and eventually prevent data downtimes and inconsistencies.

The Five Pillars of Data Observability

The pillars of data observability provide details that can accurately describe the state of the organization’s data at any given time. These five pillars make a data system observable to the highest degree when combined. According to Barr Moses – CEO of  Monte Carlo Data – there are the five pillars of data observability:

1. Freshness: Ensuring the data in the data systems is up to date and in sync is one of the biggest issues modern organizations face, especially with multiple and complex data sources. Having data freshness in your data observability stack helps monitor your data system for data timeline inconsistencies and ensures your organization’s data remains up to date. 
2. Distribution: Data accuracy is critical for building quality and reliable data systems. Distribution refers to the measure of variance in the system. If data wildly varies in the system, then there is an issue with the accuracy. The distribution pillar focuses on the quality of data produced and consumed by the data system. With distribution in your data observability stack, you can monitor your data values for inconsistencies and avoid erroneous data values being injected into your data system.
3. Volume: Monitoring data volumes is essential to creating a healthy data system. Having the volume pillar in your data observability stack answers questions such as “Is my data intake meeting the estimated thresholds?” and “Is there enough data storage capacity to meet the data demands?” Keeping track of volume helps ensure data requirements are within defined limits.
4. Schema: As the organization grows and new features are added to the application database, schema changes are inevitable. However, changes to the schema that aren’t well managed can introduce downtimes in your application. The schema pillar in the data observability stack ensures that database schema such as data tables, fields, columns, and names are accurate, up to date, and regularly audited.
5. Lineage: Having a full picture of your data ecosystem is essential for managing and monitoring the pulse of your data system. Lineage refers to how easy it is to trace the flow of the data through our data systems. Data lineage answers questions such as “how many tables do we have?” “how are they connected?” “what external data sources are we connecting to?” Data lineage in your data observability stack combines the other four pillars into a unified view allowing you to create a blueprint of your data system.

Why Is Data Observability Important?

Data observability goes beyond monitoring and alerting; it allows organizations to understand their data systems fully and enables them to fix data problems in increasingly complex data scenarios or even prevent them in the first place.

Data observability enhances trust in data so businesses can confidently make data-driven decisions

Data insights and machine learning algorithms can be invaluable, but inaccurate and mismanaged data can have catastrophic consequences.

For example, in October 2020, Public Health England (PHE), which tallies daily new Covid-19 infections, discovered an Excel spreadsheet error that caused them to overlook 15,841 new cases between September 25 and October 2. The PHE reported that the error was caused by the Excel spreadsheet used to collect the data reaching its data limit. As a result, the number of daily new instances was far larger than initially reported, and tens of thousands of people who tested positive for Covid-19 were not contacted by the government’s “test and trace” program. 

Data observability helps monitor and track situations quickly and efficiently, enabling organizations to become more confident when making decisions based on data.

Data observability helps timely delivery of quality data for business workloads

Ensuring data is readily available and in the correct format is critical for every organization. Different departments in the organization depend on quality data to carry out business operations — data engineers, data scientists, and data analysts depend on data to deliver insights and analytics. Lack of accurate quality data could result in a breakdown in business processes that can be costly.

For example, let’s imagine your organization runs an ecommerce store with multiple sources of data (sales transactions, stock quantities, user analytics) that consolidate to a data warehouse. The sales department needs sales transactions data to generate financial reports. The marketing department depends on user analytics data to effectively conduct marketing campaigns. Data scientists depend on data to train and deploy machine learning models for the product recommendation engine. If one of the data sources is out of sync or incorrect, it could harm the different aspects of the business.

Data observability ensures the quality, reliability, and consistency of data in the data pipeline by giving organizations a 360-degree view of their data ecosystem, allowing them to drill down and resolve issues that can cause a breakdown in your data pipeline.

Data observability helps you discover and resolve data issues before they affect the business

One of the biggest flaws with pure monitoring systems is they only check for “metrics” or unusual conditions you anticipate or are already aware of. But what about cases you didn’t see coming?

In 2014, Amsterdam’s city council lost €188 million due to a housing benefits error. The software the council used to disburse housing benefits to low-income families was programmed in cents rather than euros, which inadvertently caused the error. The software error caused families to receive significantly more than they expected. People who would typically receive €155 ended up receiving €15,500. More alarming, in this case, is that nothing in the software alerted administrators of the error.

Data observability detects situations you aren’t aware of or wouldn’t think to look for and can prevent issues before they seriously affect the business. Data observability can track relationships to specific issues and provide context and relevant information for root cause analysis and remediation.

A new stage of maturity for data

Furthermore, the rise of Data Observability products like Monte Carlo Data demonstrate that data has entered a new stage of maturity where data teams must adhere to strict Service Level Agreements (SLAs) to meet the needs of their business. Data must be fresh, accurate, traceable, and scalable with maximum uptime so businesses can effectively operationalize the data. But how does the rest of the data stack live up to the challenge?

Deliver Fresh Data With Striim

Striim provides real-time data integration and data streaming, connecting sources and targets across hybrid and multi-cloud environments. With access to granular data integration metrics via a REST API, Striim customers can ensure data delivery SLAs in centralized monitoring and observability tools. 

To meet the demands of online buyers, Macy’s uses Striim to replicate inventory data with sub-second latency, scaling to peak holiday shopping workloads.

Furthermore, Striim’s automated data integration capabilities eliminates integration downtime by detecting schema changes on source databases and automatically replicating the changes to target systems or taking other actions (e.g. sending alerts to third party systems). 

Learn more about Striim with a technical demo with one of our data integration experts or start your free trial here.

As more companies rely on data to drive critical business decisions, improve product offerings, and serve customers better, the amount of data companies capture is higher than ever. This study by Domo estimates 2.5 quintillion bytes of data were generated every day in 2017, with this figure set to increase to 463 exabytes in 2025. But what good is all that data if companies can’t utilize it quickly? The topic of the most optimal data storage for data analytics needs has been long debated.

Data warehouses and data lakes have been the most widely used storage architectures for big data. But what about using a data lakehouse vs. a data warehouse? A data lakehouse is a new data storage architecture that combines the flexibility of data lakes and the data management of data warehouses.

Depending on your company’s needs, understanding the different big-data storage techniques is instrumental to developing a robust data storage pipeline for business intelligence (BI), data analytics, and machine learning (ML) workloads.

• What is a Data Warehouse?
• What is a Data Lake?
• What is a Data Lakehouse? A Combined Approach
• Data Warehouse vs. Data Lake vs. Data Lakehouse: A Quick Overview
• Data Lakehouse vs. Data Warehouse vs. Data Lake: Which One Is Right for Your Needs?

What Is a Data Warehouse?

A data warehouse is a unified data repository for storing large amounts of information from multiple sources within an organization. A data warehouse represents a single source of “data truth” in an organization and serves as a core reporting and business analytics component.

Typically, data warehouses store historical data by combining relational data sets from multiple sources, including application, business, and transactional data. Data warehouses extract data from multiple sources and transform and clean the data before loading it into the warehousing system to serve as a single source of data truth. Organizations invest in data warehouses because of their ability to quickly deliver business insights from across the organization.

Data warehouses enable business analysts, data engineers, and decision-makers to access data via BI tools, SQL clients, and other less advanced (i.e., non-data science) analytics applications.

The benefits of a data warehouse

Data warehouses, when implemented, offer tremendous advantages to an organization. Some of the benefits include:

• Improving data standardization, quality, and consistency: Organizations generate data from various sources, including sales, users, and transactional data. Data warehousing consolidates corporate data into a consistent, standardized format that can serve as a single source of data truth, giving the organization the confidence to rely on the data for business needs.
• Delivering enhanced business intelligence: Data warehousing bridges the gap between voluminous raw data, often collected automatically as a matter of practice, and the curated data that offers insights. They serve as the data storage backbone for organizations, allowing them to answer complex questions about their data and use the answers to make informed business decisions.
• Increasing the power and speed of data analytics and business intelligence workloads: Data warehouses speed up the time required to prepare and analyze data. Since the data warehouse’s data is consistent and accurate, they can effortlessly connect to data analytics and business intelligence tools. Data warehouses also cut down the time required to gather data and give teams the power to leverage data for reports, dashboards, and other analytics needs.
• Improving the overall decision-making process: Data warehousing improves decision-making by providing a single repository of current and historical data. Decision-makers can evaluate risks, understand customers’ needs, and improve products and services by transforming data in data warehouses for accurate insights.

For example, Walgreens migrated its inventory management data into Azure Synapse to enable supply chain analysts to query data and create visualizations using tools such as Microsoft Power BI. The move to a cloud data warehouse also decreased time-to-insights: previous-day reports are now available at the start of the business day, instead of hours later.

The disadvantages of a data warehouse

Data warehouses empower businesses with highly performant and scalable analytics. However, they present specific challenges, some of which include:

• Lack of data flexibility: Although data warehouses perform well with structured data, they can struggle with semi-structured and unstructured data formats such as log analytics, streaming, and social media data. This makes it hard to recommend data warehouses for machine learning and artificial intelligence use cases.
• High implementation and maintenance costs: Data warehouses can be expensive to implement and maintain. This article by Cooladata estimates the annual cost of an in-house data warehouse with one terabyte of storage and 100,000 queries per month to be $468,000. Additionally, the data warehouse is typically not static; it becomes outdated and requires regular maintenance, which can be costly.

What Is a Data Lake?

A data lake is a centralized, highly flexible storage repository that stores large amounts of structured and unstructured data in its raw, original, and unformatted form. In contrast to data warehouses, which store already “cleaned” relational data, a data lake stores data using a flat architecture and object storage in its raw form. Data lakes are flexible, durable, and cost-effective and enable organizations to gain advanced insight from unstructured data, unlike data warehouses that struggle with data in this format.

In data lakes, the schema or data is not defined when data is captured; instead, data is extracted, loaded, and transformed (ELT) for analysis purposes. Data lakes allow for machine learning and predictive analytics using tools for various data types from IoT devices, social media, and streaming data.

The benefits of a data lake

Because data lakes can store both structured and unstructured data, they offer several benefits, such as:

• Data consolidation: Data lakes can store both structured and unstructured data to eliminate the need to store both data formats in different environments. They provide a central repository to store all types of organizational data.
• Data flexibility: A significant benefit of data lakes is their flexibility; you can store data in any format or medium without the need to have a predefined schema. Allowing the data to remain in its native format allows for more data for analysis and caters to future data use cases.
• Cost savings: Data lakes are less expensive than traditional data warehouses; they are designed to be stored on low-cost commodity hardware, like object storage, usually optimized for a lower cost per GB stored. For example, Amazon S3 standard object storage offers an unbelievable low price of $0.023 per GB for the first 50 TB/month.
• Support for a wide variety of data science and machine learning use cases: Data in data lakes is stored in an open, raw format, making it easier to apply various machine and deep learning algorithms to process the data to produce meaningful insights.

The disadvantages of a data lake

Although data lakes offer quite a few benefits, they also present challenges:

• Poor performance for business intelligence and data analytics use cases: If not properly managed, data lakes can become disorganized, making it hard to connect them with business intelligence and analytics tools. Also, a lack of consistent data structure and ACID (atomicity, consistency, isolation, and durability) transactional support can result in sub-optimal query performance when required for reporting and analytics use cases.
• Lack of data reliability and security: Data lakes’ lack of data consistency makes it difficult to enforce data reliability and security. Because data lakes can accommodate all data formats, it might be challenging to implement proper data security and governance policies to cater to sensitive data types.

What Is a Data Lakehouse? A Combined Approach

A data lakehouse is a new, big-data storage architecture that combines the best features of both data warehouses and data lakes. A data lakehouse enables a single repository for all your data (structured, semi-structured, and unstructured) while enabling best-in-class machine learning, business intelligence, and streaming capabilities.

Data lakehouses usually start as data lakes containing all data types; the data is then converted to Delta Lake format (an open-source storage layer that brings reliability to data lakes). Delta lakes enable ACID transactional processes from traditional data warehouses on data lakes.

The benefits of a data lakehouse

Data lakehouse architecture combines a data warehouse’s data structure and management features with a data lake’s low-cost storage and flexibility. The benefits of this implementation are enormous and include:

• Reduced data redundancy: Data lakehouses reduce data duplication by providing a single all-purpose data storage platform to cater to all business data demands. Because of the advantages of the data warehouse and the data lake, most companies opt for a hybrid solution. However, this approach could lead to data duplication, which can be costly.
• Cost-effectiveness: Data lakehouses implement the cost-effective storage features of data lakes by utilizing low-cost object storage options. Additionally, data lakehouses eliminate the costs and time of maintaining multiple data storage systems by providing a single solution.
• Support for a wider variety of workloads: Data lakehouses provide direct access to some of the most widely used business intelligence tools (Tableau, PowerBI) to enable advanced analytics. Additionally, data lakehouses use open-data formats (such as Parquet) with APIs and machine learning libraries, including Python/R, making it straightforward for data scientists and machine learning engineers to utilize the data.
• Ease of data versioning, governance, and security: Data lakehouse architecture enforces schema and data integrity making it easier to implement robust data security and governance mechanisms.

The disadvantages of a data lakehouse

The main disadvantage of a data lakehouse is it’s still a relatively new and immature technology. As such, it’s unclear whether it will live up to its promises. It may be years before data lakehouses can compete with mature big-data storage solutions. But with the current speed of modern innovation, it’s difficult to predict whether a new data storage solution could eventually usurp it.

Data Warehouse vs. Data Lake vs. Data Lakehouse: A Quick Overview

The data warehouse is the oldest big-data storage technology with a long history in business intelligence, reporting, and analytics applications. However, data warehouses are expensive and struggle with unstructured data such as streaming and data with variety.

Data lakes emerged to handle raw data in various formats on cheap storage for machine learning and data science workloads. Though data lakes work well with unstructured data, they lack data warehouses’ ACID transactional features, making it difficult to ensure data consistency and reliability.

The data lakehouse is the newest data storage architecture that combines the cost-efficiency and flexibility of data lakes with data warehouses’ reliability and consistency.

This table summarizes the differences between the data warehouse vs. data lake vs. data lakehouse.

	Data Warehouse	Data Lake	Data Lakehouse
Storage Data Type	Works well with structured data	Works well with semi-structured and unstructured data	Can handle structured, semi-structured, and unstructured data
Purpose	Optimal for data analytics and business intelligence (BI) use-cases	Suitable for machine learning (ML) and artificial intelligence (AI) workloads	Suitable for both data analytics and machine learning workloads
Cost	Storage is costly and time-consuming	Storage is cost-effective, fast, and flexible	Storage is cost-effective, fast, and flexible
ACID Compliance	Records data in an ACID-compliant manner to ensure the highest levels of integrity	Non-ACID compliance: updates and deletes are complex operations	ACID-compliant to ensure consistency as multiple parties concurrently read or write data

The “data lakehouse vs. data warehouse vs. data lake” is still an ongoing conversation. The choice of which big-data storage architecture to choose will ultimately depend on the type of data you’re dealing with, the data source, and how the stakeholders will use the data. Although a data lakehouse combines all the benefits of data warehouses and data lakes, we don’t advise you to throw your existing data storage technology out the window for a data lakehouse.

Data Lakehouse vs. Data Warehouse vs. Data Lake: Which One Is Right for Your Needs?

Data lakehouses can be complex to build from scratch. And you’ll most likely use a platform built to support open data lakehouse architecture. So, ensure you research each platform’s different capabilities and implementations before making a purchase.

A data warehouse is a good choice for companies seeking a mature, structured data solution that focuses on business intelligence and data analytics use cases. However, data lakes are suitable for organizations seeking a flexible, low-cost, big-data solution to drive machine learning and data science workloads on unstructured data.

Suppose the data warehouse and data lake approaches aren’t meeting your company’s data demands, or you’re looking for ways to implement both advanced analytics and machine learning workloads on your data. In that case, a data lakehouse is a reasonable choice.

Whichever solution you choose, Striim can help. Striim makes it simple to continuously and non-intrusively ingest all your enterprise data from various sources in real-time for data warehousing. Striim can also be used to preprocess your data in real-time as it is being delivered into the data lake stores to speed up downstream activities.

Schedule a demo and we’ll give you a personalized walkthrough or try Striim at production-scale for free! Small data volumes or hoping to get hands on quickly? At Striim we also offer a free developer version.

The demand for data engineers and data architects is higher than ever. A report by Opinium, in collaboration with the UK’s Department for Digital, Culture, Media and Sport, shows UK companies alone are currently recruiting for 178,000 to 234,000 roles that require hard data skills. And as we continue to generate and use data to enhance critical business decisions, the demand for data professionals will continue to increase.

The data architect and data engineer roles are sometimes used interchangeably. Although there is some overlap, and they share some specific duties, understanding how both roles operate in the organization can benefit engineering managers looking to build an engineering team and students and professionals looking to develop a career in either field.

What Is a Data Architect? The Role and Its Responsibilities

A data architect is responsible for formulating the organizational data strategy and defining the data management standards and principles on which the organization operates. Data architects design the “data blueprint” that other data consumers follow and implement.

They create the organization’s logical and physical data assets and set data policies based on company requirements. Data architects are often veterans in the industry who have had experience in many data roles and have gained experience navigating complex business scenarios and designing solutions that data teams can implement.

Data architect responsibilities

The data architect’s primary responsibility revolves around providing deep technical expertise for designing, creating, managing, and deploying large-scale data systems in the organization.

A data architect’s responsibilities include:

• Designing, developing, implementing, and translating business requirements and the overall organizational data strategy, including standards, principles, data sources, storage, pipelines, data flow, and data security policies
• Collaborating with data engineers, data scientists, and other stakeholders to execute the data strategy
• Communicating and defining data architecture patterns in the organization that guide the data framework
• Leading data teams to develop secure, scalable, high-performance, and reliable big data and analytics software and services

What skills do data architects have?

Data architects need a combination of technical and soft skills to thrive. Typically, data architects start in other data roles, such as data scientist, data analyst, or data engineer, and work their way up to becoming data architects after years of experience with data modeling, data design, and data management. Some of the skills data architects have include:

• Data modeling, integration, design, and data management: Data architects understand the concepts, principles, and implementation of data modeling, design, and data management. They can produce relevant data models and design the organization’s data flow.
• Databases and operating systems: Data architects are experienced in the various SQL and NoSQL databases. They understand the advantages and drawbacks of each and how they can be set up effectively and securely across different operating systems (Linux, Windows) and environments (development and production).
• Data architecture: Data architects know the best practices on data architecture for enterprise data warehouse development. They have a solid understanding of the organization’s data infrastructure and how different systems interact.
• Data security and governance: Data architects understand the processes, roles, policies, standards, and metrics that ensure the effective and efficient use of data/information. Data architects are skilled in data governance strategies with a good understanding of the risks and mitigations of each. They ensure data governance is in line with the overall organizational strategy.
• Communication and leadership: Data architects are usually leaders of the data management team; as such, they must communicate clear technical solutions to complex data problems to both technical and non-technical audiences.

Although every organization has slightly different requirements, you’ll notice similar skills and common themes (like the ones outlined above) throughout job descriptions for this role. For example, this data solutions architect role with Lightspeed asks that candidates have experience with data management for software as a service (SaaS) tools and build data solutions and models for this team.

What Is a Data Engineer? The Role and Its Responsibilities

A data engineer is responsible for designing, maintaining, and optimizing data infrastructure for data collection, management, transformation, and access. Data engineers create pipelines that convert raw data into usable formats for data scientists and other data consumers to utilize.

The data engineer role evolved to handle the core data aspects of software engineering and data science; they use software engineering principles to develop algorithms that automate the data flow process. They also collaborate with data scientists to build machine learning and analytics infrastructure, from testing to deployment.

Data engineer responsibilities

The primary responsibility of a data engineer is ensuring that data is readily available, secure, and accessible to stakeholders when they need it.

A data engineer’s responsibilities typically include:

• Building and maintaining data infrastructure for the optimal extraction, transformation, and loading of data from a wide variety of sources such as Amazon Web Services (AWS) and Google Cloud big data platforms
• Ensuring data accessibility at all times and implementing company data policies with respect to data privacy and confidentiality
• Cleaning and wrangling data from primary and secondary sources into formats that can be efficiently utilized by data scientists and other data consumers
• Collaborating with engineering teams, data scientists, and other stakeholders to understand how data can be leveraged to meet business needs

What skills do data engineers have?

Data engineering is a synthesis of software engineering and data science, so knowledge of both fields is advantageous. Because data engineering is heavily reliant on programming, most data engineers begin their careers as software engineers and then pivot to data engineering.

Some of the skills required of data engineers include:

• Database systems (SQL and NoSQL): Data engineers have a good knowledge of SQL and NoSQL databases and are skilled in writing queries to manipulate and retrieve data.
• Data migration and integration: Data engineers are often tasked with aggregating data from multiple sources and migrating data from one platform to another based on business needs. Data engineers understand data migration and integration techniques (Big-Bang, trickle, lift and shift )and the tools required to perform them. Striim is a popular tool used by data engineers for data integration and migration; it provides modern, reliable data integration and migration across the public and private cloud.
• Data wrangling: Data wrangling is the process of gathering, cleaning, enriching, and transforming data into the desired format to incorporate better decision-making in less time. A data engineer is skilled in various data wrangling techniques and tools, such as extraction, transformation, and loading (ETL).
• Data processing techniques and tools: Data engineers are experienced in various data processing techniques, such as real-time processing and batch processing; they are comfortable working with data processing tools such as Apache Kafka and Apache Spark.
• Programming languages (Python): Data engineering relies heavily on programming; data engineers are typically fluent in at least one programming language, with Python being regarded as the most popular and widely used programming language in the data engineering community.
• Cloud computing and distributed systems: With more companies relying on cloud providers for data infrastructure needs, companies rely on data engineers to create data solutions using popular cloud providers, such as Amazon Web Services, Google Cloud, and Azure. Data engineers have experience working with tools such as Hadoop for the distributed processing of large datasets.

Although each organization’s requirements are slightly different, you’ll notice similar skills and common themes (such as the ones outlined above) throughout the job descriptions for data engineer roles. For example, the candidate for this data engineer, infrastructure engineering role at TikTok will be responsible for collaborating with software engineers and data scientists to build big data solutions.

Data Architect vs. Data Engineer: What Are the Differences?

The data architect and data engineer titles are closely related and, as such, frequently confused. The difference in both roles lies in their primary responsibilities.

• Data architects design the vision and blueprint of the organization’s data framework, while the data engineer is responsible for creating that vision.
• Data architects provide technical expertise and guide data teams on bringing business requirements to life; data engineers ensure data is readily available, secure, and accessible to stakeholders (data scientists, data analysts) when they need it.
• Data architects have substantial experience in data modeling, data integration, and data design and are often experienced in other data roles; data engineers have a strong foundation in programming with software engineering experience.
• The data architect and the data engineer work together to build the organization’s data system.

Here’s a table to briefly summarize the key differences and help you visualize the contrast in responsibilities:

Data Architect	Data Engineer
Visualizes the blueprint for the organizational data framework, defining how the data will be stored, consumed, integrated, and managed by different data entities and IT systems	Builds and maintains the data systems and information specified by data architects in the data framework
Deep expertise in databases, data modeling, data architecture, and operating systems	Strong background in software engineering, algorithms, and application development
Focused on leadership and high-level data strategy	Focused on the day-to-day tasks of cleaning, wrangling, and preparing data for other data consumers, such as data scientists

Data Architect vs. Data Engineer: Which Is Right for You?

When choosing a career or hiring, note that the data architecture role requires years of experience in a previous data-related role; both roles require a deep understanding of database systems, data processing tools, and experience working with big data. To put together an effective data management team, you must first understand the differences between the roles.

When interviewing for data engineers, consider whether the candidate has experience building software and APIs, as well as a solid understanding of various databases, data wrangling, and data processing techniques.

For data architects, on the other hand, be sure to ask what data projects they’ve led in the past and get a sense of their “data philosophy.” Remember that a data architect will be the leader of your data management team to whom you should feel confident delegating authority.

Schedule a demo and we’ll give you a personalized walkthrough or try Striim at production-scale for free! Small data volumes or hoping to get hands on quickly? At Striim we also offer a free developer version.