Category: Blog

On-premises-to-cloud migration is the necessary first step to cloud adoption, which offers a fast lane to data infrastructure modernization, innovation, and the ability to rapidly transform business operations. But many companies still restrict themselves to using the cloud for non-critical projects, rather than mission-critical operations, out of concern over the difficulties and the risks of migration. Are you one of them? If so, read on to discover a new approach that addresses critical data migration challenges.

Common Risks of On-Premises-to-Cloud Migration

A major component of the cloud migration effort is data migration from existing legacy databases. Many data migration solutions require you to lock the legacy database to preserve the consistent state after a snapshot is taken.

Depending on the size of the database, network bandwidth, and required transformations, the whole process for loading the data to the cloud, restoring the database, and testing the new system can take days, weeks, or even months. I am not aware of any digital business that would be good with locking databases that support critical business operations for such an extensive time.

In addition, you run the risk of having a database with an inconsistent state after the migration process. Some solutions might lose data in transit because of a process failure or network outage. Or the data might not be applied to the target system in the right transactional order. As a result, your cloud database winds up diverging from the source legacy system.

To ensure that the new environment is stable, you have to test the new system thoroughly before moving all your users over. Time pressures to minimize downtime can lead to rushed testing, which in turn results in an unstable cloud environment after you do a big bang switchover. Certainly, not the goal of your modernization effort!

It is no wonder with all these risks and disruptions to operations, the systems that should move to the cloud as the top priority – because they can bring the greatest positive impact for business transformation – end up being de-prioritized in favor of less risky migrations. As a result, your organization may fail to extract the full value from your cloud investment and limit the speed of innovation and modernization.

Mitigating the Risks of On-Premises-to-Cloud Migration

Here comes the good news that I love sharing: Today, newer, more sophisticated streaming data integration with change data capture technology minimizes disruptions and risks mentioned earlier. This solution combines initial batch load with real-time change data capture (CDC) and delivery capabilities.

As the system performs the bulk load, the CDC component collects the changes in real time as they occur. As soon as the initial load is complete, the system applies the changes to the target environment to maintain the legacy and cloud database consistent.

Let’s review how the streaming data integration approach tackles each of these risks that delay your business in getting the fullest benefits from your cloud investments.

Eliminating Database Downtime

Combining bulk load with CDC removes the need to pause the legacy database. During the bulk load process, your database is open to any new transactions. All new transactions are immediately captured and applied to the target as soon as the bulk load is complete, keeping the two systems in-sync.

The only downtime for the migration process occurs during the application switchover process. Therefore, this configuration enables zero database downtime during on-premises-to-cloud migration.

Avoiding Data Loss

To prevent data loss throughout the data migration process, streaming data integration tracks data movement and processing. Striim’s streaming data integration platform provides delivery validation that all your data has been moved to the target.

Also, with built-in exactly once processing (E1P), the software platform can avoid data duplicates. Striim’s CDC offering is designed to maintain the transaction integrity (i.e., ACID properties) during the real-time data movement so the target database remains consistent with the source.

Thorough Testing Without Time Limitation

Because during and after the initial load, CDC keeps up with transactions happening in the legacy system, your team can take the time necessary to thoroughly test the new system before moving users. Having live production data in the cloud database, combined with unlimited testing time, provides the comprehensive assessments and assurances that many mission-critical systems need for such a significant transition.

Fallback Option

After the switchover, performing reverse real-time data movement from the cloud database back to the legacy database enables you to keep the legacy system up-to-date with the new transactions taking place in the cloud. In short, if necessary, you have a fallback option to put everyone back on the old system as you troubleshoot any issues in the new system.

During this troubleshooting and retesting time, the CDC process can be set up to collect the new transactions happening in the legacy database to bring the cloud database to a consistent state with the legacy system. You can point the application to the cloud database, once again, after testing thoroughly.

Phased Migration with Parallel Use

A more complex but highly effective approach to further facilitate your risk mitigation and thorough testing is a gradual migration. Bi-directional real-time data replication is your solution to keep both the cloud and the on-premises legacy systems in-sync while they are both open to transactions and support the application.

You can move some users to the new system and leave others in the old, running both in parallel as you test the new system. As the testing with the production workload progresses as planned, you can add new users in a phased and gradual manner that minimizes risks.

Migration Is Only the First Step

Streaming data integration is not only for on-premises to cloud migration. Once you have the cloud database in production, you perform ongoing integration with relevant data sources and applications across the enterprise, including in other clouds.

Striim is designed for continuous data integration to support your hybrid cloud architecture with stream processing capabilities, as well. When you use a single cloud integration solution for both the database migration and ongoing data integration, you minimize development efforts, shorten the learning curve, and reduce risks with simplified solution architecture.

With strong partnerships with leading cloud vendors, Striim offers proven solutions that minimize your risks during data migration and ongoing integration. To learn more about how Striim can help with your on-premises-to-cloud migration, I invite you to schedule a brief demo with a Striim technologist.

 

 

For those who need to migrate an Oracle database to Google Cloud, the ability to move mission-critical data in real-time between on-premises and cloud environments without either database downtime or data loss data is paramount. In this video Alok Pareek, Founder and EVP of Products at Striim demonstrates how the Striim platform enables Google Cloud users to build streaming data pipelines from their on-premises databases into their Cloud SQL environment with reliability, security, and scalability. The full 8-minute video is available to watch below:

Easy to Use

Striim offers an easy-to-use platform that maximizes the value gained from cloud initiatives; including cloud adoption, hybrid cloud data integration, and in-memory stream processing. This demonstration illustrates how Striim feeds real-time data from mission-critical applications from a variety of on-prem and cloud-based sources to Google Cloud without interruption of critical business operations.

Visualize Your Data

Through different interactive views, Striim users can develop Apps to build data pipelines to Google Cloud, create custom Dashboards to visualize their data, and Preview the Source data as it streams to ensure they’re getting the data they need. For this demonstration, Apps is the starting point from which to build the data pipeline.

There are two critical phases in this zero-downtime data migration scenario. The first involves the initial load of data from the on-premise Oracle database into the Cloud SQL Postgres database. The second is the synchronization phase, achieved through specialized readers to keep the source and target consistent.

The pipeline from the source to the target is built using a flow designer that easily creates and modifies streaming data pipelines. The data can also be transformed while in motion, to be realigned or delivered in a different format. Through the interface, the properties of the Oracle database can also be configured – allowing users extensive flexibility in how the data is moved.

Once the application is started, the data can be previewed, and progress monitored. While in-motion, data can be filtered, transformed, aggregated, enriched, and analyzed before delivery. With up-to-the-second visibility of the data pipeline, users can quickly and easily verify the ingestion, processing, and delivery of their streaming data.

During the time of initial load, the source data in the database is continually changing. Striim keeps the Cloud SQL Postgres database up-to-date with the on-premises Oracle database using change data capture (CDC). By reading the database transactions in the Oracle redo logs, Striim collects the insert, update, and delete operations as soon as the transactions commit, and makes only the changes to the target, This is done without impacting the performance of source systems, while avoiding any outage to the production database.

By generating DML activity using a simulator, the demonstration shows how inserts, updates, and deletes are managed. Running DMLS operations against the orders table, the preview shows not only the data being captured, but also metadata including the transaction ID, the system commit number, the table name, and the operation type. When you log into the orders table, the data is present in the table.

The initial upload of data from the source to the target, followed by change data capture to ensure source and target remain in-sync, allows businesses to move data from on-premises databases into Google Cloud with the peace of mind that there will be no data loss and no interruption of mission-critical applications.

Additional Resources

To learn more about Striim’s capabilities to support the data integration requirements for a Google hybrid cloud architecture, check out all of Striim’s solutions for Google Cloud Platform.

To read more about real-time data integration, please visit our Real-Time Data Integration solutions page.

To learn more about how Striim can help you migrate Oracle database to Google Cloud, we invite you to schedule a demo with a Striim technologist.

 

Striim’s Real-Time Data is for Much More Than Just Analytics article was originally published on Forbes.

The conversation around real-time data, fast data and streaming data is getting louder and more energetic. As the age of big data fades into the sunset — and many industry folks are even reluctant to use the term — there is much more focus on fast data and obtaining timely insights. The focus of many of these discussions is on real-time analytics (otherwise known as streaming analytics), but this only scratches the surface of what real-time data can be used for.

If you look at how real-time data pipelines are actually being utilized, you find that about 75% of the use cases are integration related. That is, continuous data collection creates real-time data streams, which are processed and enriched and then delivered to other systems. Often these other systems are not themselves streaming. The target could be a database, data warehouse or cloud storage, with a goal of ensuring that these systems are always up to date. This leaves only about 25% of companies doing immediate streaming analytics on real-time data. But these are the use cases that are getting much more attention.

There are many reasons why streaming data integration is more common, but the main reason is quite simple: This is a relatively new technology, and you cannot do streaming analytics without first sourcing real-time data. This is known as a “streaming first” data architecture, where the first problem to solve is obtaining real-time data feeds.

Organizations can be quite pragmatic about this and approach stream-enabling their sources on a need-to-have, use-case-specific basis. This could be because batch ETL systems no longer scale or batch windows have gone away in a 24/7 enterprise. Or, they want to move to more modern technologies, which are most suitable for the task at hand, and keep them continually up to date as part of a digital transformation initiative.

Cloud Is Driving Streaming Data Integration

The rise of cloud has made a streaming-first approach to data integration much more attractive. Simple use cases, like migrating an on-premise database that services an in-house business application to the cloud, are often not even viable without streaming data integration.

The naive approach would be to back up the database, load it into the cloud and point the cloud application at it. However, this assumes a few things:

1. You can afford application downtime.

2. Your application can be stopped while you are doing this.

3. You can spin up and use the cloud application without testing it.

For most business-critical applications, none of these things are true.

A better approach to minimizing or eliminating downtime is an online migration that keeps the application running. To perform this task, source changes from the in-house database, using a technology called change data capture (CDC), as real-time data streams, load the database to the cloud, then apply any changes from the real-time stream that happened while you were doing the loading. The change delivery to the cloud can be kept running while you test the cloud application, and when you cut over, it will be already up to date.

Streaming data integration is a crucial element of this type of use case, and it can also be applied to cloud bursting, operational machine learning, large scale cloud analytics or any other scenario where having up-to-the-second data is essential.

Streaming Data Integration Is The Precursor To Streaming Analytics

Once organizations are doing real-time data collection, typically for integration purposes, it then opens the door to doing streaming analytics. But you can’t put the cart before the horse and do streaming analytics unless you already have streaming data.

Streaming analytics also requires preprepared data. It’s a commonly known metric that 80% of the time spent in data science is in data preparation. This is true for machine learning and also true for streaming analytics. Obtaining the real-time data feed is just the beginning. You may also need to transform, join, cleanse and enrich data streams to give the data more context before performing analytics.

As a simple example, imagine you are performing CDC on a source database and have a stream of orders being made by customers. In any well-normalized, relational database, these tables are mostly just numbers relating to detail contained in other tables.

This might be perfect for a relational, transactional system, but it’s not very useful for analytics. However, if you can join the streaming data with reference data for customers and items, you have now added more context and more value. The analytics can now show real-time sales by customer location or item category and truly provide business insights.

Without the processing steps of streaming data integration, the streaming analytics would lose value, again showing how important the real-time integration layer really is.

Busting The Myth That Real-Time Data Is Prohibitively Expensive

A final consideration is cost. Something that has been said repeatedly is that real-time systems are expensive and should only be used when absolutely necessary. The typically cited use cases are algorithmic trading and critical control systems.

While this may have been true in the past, the massive improvements in the price-performance equation for CPU and memory over the last few decades have made real-time systems, and in-memory processing in general, affordable for mass consumption. Coupled with cloud deployments and containerization, the capability to have real-time data streamed to any system is within reach of any enterprise.

While real-time analytics and instant operational insights may get the most publicity and represent the long-term goal of many organizations, the real workhorse behind the scenes is streaming data integration. 

While the typical conversation about Azure hybrid cloud architecture may be centered around scaling applications, VMs, and microservices, the bigger consideration is the data. Spinning up additional services on-demand in Azure is useless if the cloud services cannot access the data they need, when they need it.

“According to a March 2018 hybrid cloud report from 451 Research and NTT Communications, around 63% of firms have a formal strategy for hybrid infrastructure. In this case, hybrid cloud does not simply mean using a public cloud and a private cloud. It means having a seamless flow of data between all clouds, on and off-premises.” – Data Foundry

To help simplify providing a seamless flow of data to your Microsoft Azure hybrid cloud infrastructure, we’re happy to announce that the Striim platform is available in the Microsoft Azure Marketplace.

How Streaming Data Integration Simplifies Your Azure Hybrid Cloud Architecture

Enterprise-grade streaming data integration enables continuous real-time data movement and processing for hybrid cloud, connecting on-prem data sources and cloud environments, as well as bridging a wide variety of cloud services. With in-memory stream processing for hybrid cloud, companies can store only the data they need, in the format that they need. Additionally, streaming data integration enables delivery validation and data pipeline monitoring in real time.

Streaming data integration simplifies real-time streaming data pipelines for cloud environments. Through non-intrusive change data capture (CDC), organizations can collect real-time data without affecting source transactional databases. This enables cloud migration with zero database downtime and minimized risk, and feeds real-time data to targets with full context – ready for rich analytics on the cloud – by performing filtering, transformation, aggregation, and enrichment on data-in-motion.

Key Traits of a Streaming Data Integration Solution for Your Azure Hybrid Cloud Architecture

There are three important objectives to consider when implementing a streaming data integration solution in an Azure hybrid cloud architecture:

• Make it easy to build and maintain –The ability to use a graphical user interface (GUI) and a SQL-based language can significantly reduce the complexity of building streaming data pipelines, allowing more team members within the company to maintain the environment.
• Make it reliable – Enterprise hybrid cloud environments require a data integration solution that is inherently reliable with failover, recovery and exactly-once processing guaranteed end-to-end, not just in one slice of the architecture.
• Make it secure –Security needs to be treated holistically, with a single authentication and authorization model protecting everything from individual data streams to complete end-user dashboards. The security model should be role-based with fine-grained access, and provide encryption for sensitive resources.

Striim for Microsoft Azure

The Striim platform for Azure is an enterprise-grade data integration platform that simplifies an Azure-based hybrid cloud infrastructure. Striim provides real-time data collection and movement from a variety of sources such as enterprise databases (ie, Oracle, HPE NonStop, SQL Server, PostgreSQL, Amazon RDS for Oracle, Amazon RDS for MySQL via low-impact, log-based change data capture), as well as log files, sensors, messaging systems, NoSQL and Hadoop solutions.

Once the data is collected in real time, it can be streamed to a wide variety of Azure services including Azure Cosmos DB, Azure SQL Database, Azure SQL Data Warehouse, Azure Event Hubs, Azure Data Lake Storage, and Azure Database for PostgreSQL.

While the data is streaming to Azure, Striim enables in-stream processing such as filtering, transformations, aggregations, masking, and enrichment, making the data more valuable when it lands. This is all done with sub-second latency, reliability and securty via an easy-to-use interface and SQL-based programming language.

To learn more about Striim’s capabilities to support the data integration requirements for an Azure hybrid cloud architecture, read today’s press release announcing the availability of the Striim platform in the Microsoft Azure Marketplace, and check out all of Striim’s solutions for Azure.

Change data capture, and in particular log-based change data capture, has become popular in the last two decades as organizations have discovered that sharing real-time transactional data from OLTP databases enables a wide variety of use-cases. The fast adoption of cloud solutions requires building real-time data pipelines from in-house databases, in order to ensure the cloud systems are continually up to date. Turning enterprise databases into a streaming source, without the constraints of batch windows, lays the foundation for today’s modern data architectures. In this blog post, I would like to discuss Striim’s CDC capabilities along with its unique features that enhance the change data capture, as well as its processing and delivery across a wide range of sources and targets.

Log-Based Change Data Capture

In our blog post about Change Data Capture, we explained why log-based change data capture is a better method to identify and capture change data. Striim uses the log-based CDC technique for the same reasons we stated in that post: Log-based CDC minimizes the overhead on the source systems, reducing the chances of performance degradation. In addition, it is non-intrusive. It does not require changes to the application, such as adding triggers to tables would do. It is a light-weight but also a highly-performant way to ingest change data. While Striim reads DML operations (INSERTS, UPDATES, DELETES) from the database logs, these systems continue to run with high-performance for their end users.

Striim’s strengths for real-time CDC are not limited to the ingestion point. Here are a few capabilities of the Striim platform that build on its real-time, log-based change data capture in enabling robust, end-to-end streaming data integration solutions:

Log-based CDC from heterogeneous databases for non-intrusive, low-impact real-time data ingestion

Striim uses log-based change data capture when ingesting from major enterprise databases including Oracle, HPE NonStop, MySQL, PostgreSQL, MongoDB, among others. It minimizes CPU overhead on sources, does not require application changes, and substantial management overhead to maintain the solution.

Ingestion from multiple, concurrent data sources to combine database transactions with semi-structured and unstructured data

Striim’s real-time data ingestion is not limited to databases and the CDC method. With Striim you can merge real-time transactional data from OLTP systems with real-time log data (i.e., machine data), messaging systems’ events, sensor data, NoSQL, and Hadoop data to obtain rich, comprehensive, and reliable information about your business.

End-to-end change data integration

Striim is designed from the ground-up to ingest, process, secure, scale, monitor, and deliver change data across a diverse set of sources and targets in real time. It does so by offering several robust capabilities out of the box:

• Transaction integrity: When ingesting the change data from database logs, Striim moves committed transactions with the transactional context (i.e., ACID properties) maintained. Throughout the whole data movement, processing, and delivery steps, this transactional context is preserved so that users can create reliable replica databases, such as in the case of cloud bursting.
• In-flight change data processing: Striim offers out-of-the-box transformers, and in-memory stream processing capabilities to filter, aggregate, mask, transform, and enrich change data while it is in motion. Using SQL-based continuous queries, Striim immediately turns change data into a consumable format for end users, without losing transactional context.
• Built-in checkpointing for reliability: As the data moves and gets processed through the in-memory components of the Striim platform, every operation is recorded and tracked by the solution. If there is an outage, Striim can replay the transactions from where it was left off — without missing data or having duplicates.
• Distributed processing in a clustered environment: Striim comes with a clustered environment for scalability and high availability. Without much effort, and using inexpensive hardware, you can scale out for very high data volumes with failover and recoverability assurances. With Striim, you don’t need to build your own clusters with third-party products.
• Continuous monitoring of change data streams: Striim continuously tracks change data capture, movement, processing, and delivery processes, as well as the end-to-end integration solution via real-time dashboards. With Striim’s transparent pipelines, you have a clear view into the health of your integration solutions.
• Schema change replication: When source Oracle database schema is modified and a DDL statement is created, Striim applies the schema change to the target system without pausing the processes.
• Data delivery validation. For database sources and targets, Striim offers out-of-the-box data delivery verification. The platform continuously compares the source and target systems, as the data is moving, validating that the databases are consistent and all changed data has been applied to the target. In use cases, where data loss must be avoided, such as migration to a new cloud data store, this feature immensely minimizes migration risks.
• Concurrent, real-time delivery to a wide range of targets: With the same software, Striim can deliver change data in real time not only to on-premise databases but also to databases running in the cloud, cloud services, messaging systems, files, IoT solutions, Hadoop and NoSQL environments. Striim’s integration applications can have multiple targets with concurrent real-time data delivery.
• Pre-packaged applications for initial load and CDC: Striim comes with example integration applications that include initial load and CDC for PostgreSQL environments. These integration applications enable setting up data pipelines in seconds, and serve as a template for other CDC sources as well.

Turning Change Data to Time-Sensitive Insights

In addition to building real-time integration solutions for change data, Striim can perform streaming analytics with flexible time windows allowing you to gain immediate insights from your data in motion. For example, if you are moving financial transactions using Striim, you can build real-time dashboards that alert on potential fraud cases before Striim delivers the data to your analytics solution.

Log-based change data capture is the modern way to turn databases into streaming data sources. However, ingesting the change data is only the first of many concerns that integration solutions should address. You can learn more about Striim’s CDC offering by scheduling a demo with a Striim technologist or experience its enterprise-grade streaming integration solution first-hand by downloading a free trial.

 

By delivering high volumes of data using Microsoft SQL Server CDC to Kafka, organizations gain visibility of their business and the vital context needed for timely operational decision making. Getting maximum value from Kafka solutions requires ingesting data from a wide variety of sources – in real time – and delivering it to users and applications that need it to take informed action to support the business.

Traditional methods used to move data, such as ETL, are just not sufficient to support high-volume, high-velocity data environments. These approaches delay getting data to where it can be of real value to the organization. Moving all the data, regardless of relevance, to the target creates challenges in storing it and getting actionable data to the applications and users that need it. Microsoft SQL Server CDC to Kafka minimizes latency and prepares data so it is delivered in the correct format for different consumers to utilize.

In most cases, the data that resides in transactional databases like Microsoft SQL Server is the most valuable to the organization. The data is constantly changing reflecting every event or transaction that occurs.  Using non-intrusive, low-impact change data capture (CDC) the Striim platform moves and processes only the changed data. With Microsoft SQL Server CDC to Kafka users manage their data integration processes more efficiently and in real time. 

Using a drag-and-drop UI and pre-built wizards, Striim simplifies creating data flows for Microsoft SQL Server CDC to Kafka. Depending on the requirements of users, the data can either be delivered “as-is,” or in-flight processing can filter, transform, aggregate, mask, and enrich the data. This delivers the data in the format needed with all the relevant context to meet the needs of different Kafka consumers –with sub-second latency.

Striim is an end-to-end platform that delivers the security, recoverability, reliability (including exactly once processing), and scalability required by an enterprise-grade solution. Built-in monitoring also compares sources and targets and validates that all data has been delivered successfully. 

In addition to Microsoft SQL Server CDC to Kafka, Striim offers non-intrusive change data capture (CDC) solutions for a range of enterprise databases including Oracle, Microsoft SQL Server, PostgreSQL, MongoDB, HPE NonStop SQL/MX, HPE NonStop SQL/MP, HPE NonStop Enscribe, and MariaDB.

For more information about how to use Microsoft SQL Server CDC to Kafka to maintain real-time pipelines for continuous data movement, please visit our Change Data Capture solutions page.

If you would like a demo of how Microsoft SQL Server CDC to Kafka works and to talk to one of our technologists, please contact us to schedule a demo.

 

 

The integration and analysis of data from both on-premises and cloud environments give an organization a deeper understanding of the state of their business. Real-time data ingestion for analytical or transactional processing enables businesses to make timely operational decisions that are critical to the success of the organization – while the data is still current.

Transactional and operational data contain valuable insights that drive informed and appropriate actions. Achieving visibility into business operations in real time allows organizations to identify and act on opportunities and address situations where improvements are needed. Real-time data ingestion to feed powerful analytics solutions demands the movement of high volumes of data from diverse sources without impacting source systems and with sub-second latency.

Using traditional batch methods to move the data introduces unwelcome delays. By the time the data is collected and delivered it is already out of date and cannot support real-time operational decision making. Real-time data ingestion is a critical step in the collection and delivery of volumes of high-velocity data – in a wide range of formats – in the timeframe necessary for organizations to optimize their value.

The Striim platform enables the continuous movement of structured, semi-structured, and unstructured data – extracting it from a wide range of sources and delivering it to cloud and on-premises endpoints – in real time and available immediately to users and applications.

The Striim platform supports real-time data ingestion from sources including databases, log files, sensors, and message queues and delivery to targets that include Big Data, Cloud, Transactional Databases, Files, and Messaging Systems. Using non-intrusive Change Data Capture (CDC) Striim reads new database transactions from source databases’ transaction or redo logs and moves only the changed data without impacting the database workload.

Real-time data ingestion is critical to accessing data that delivers significant value to a business. With clear visibility into the organization, based on data that is current and comprehensive, organizations can make more informed operational decisions faster.

To read more about real-time data ingestion, please visit our Real-Time Data Integration solutions page.

To have one of our experts guide you through a brief demo of our real-time data ingestion offering, please schedule a demo.

The scalable and reliable delivery of high volumes of Kafka data to enterprise targets via real-time Kafka integration gives organizations current and relevant information about their business. Loading data from Kafka to MySQL enables organizations run rich custom queries on data enhanced with pub/sub messaging data to make key operational decisions in the timeframe for them to be most effective.

To get optimal value from the rich messaging data generated by CRM, ERP, and e-commerce applications, large data sets need to be delivered from Kafka to MySQL with sub-second latency. Integrating data from Kafka to MySQL enhances transactional data – providing greater understanding of the state of operations. With access to this data, users and applications have the context to make decisions and take essential and timely action to support the business.

Using traditional batch-based approaches to the movement of data from Kafka to MySQL creates an unacceptable bottleneck – delaying the delivery of data to where it can be of real value to the organization. This latency limits the potential for this data to make critical operational decisions that enhance customer experiences, optimize processes, and drive revenue.

ETL methods move the data “as is” – without any pre-processing. However, depending on the requirements not all the data may be needed and the data that is necessary may need to be augmented with other data to make it useful. Ingesting high volumes of raw data creates additional challenges when it comes to storage and getting high value actionable data to users and applications.

Building real time data pipelines from Kafka to MySQL, Striim allows users to minimize latency and support their high-volume, high-velocity data environments. Striim offers real time data ingestion with in-flight processing including filtering, transformations, aggregations, masking, and enrichment to deliver relevant data from Kafka to MySQL in the right format and with full context.

Striim also includes built-in security, delivery validation, and additional features to essential for the scalability and reliability requirements of mission-critical applications. Real time pipeline monitoring detects any patterns or anomalies as the data is moving from Kafka to MySQL. Interactive dashboards provide visibility into the health of the data pipelines and highlight issues with instantaneous alerts – allowing for timely corrective action to be taken on the results of comprehensive pattern matching, correlation, outlier detection, and predictive analytics.

For more information about gaining timely intelligence from integrating high volumes of rich messaging data from Kafka to MySQL, please visit our Kafka integration page at: https://www.striim.com/blog/kafka-stream-processing-with-striim/

If you would like a demo of real time data integration from Kafka to MySQL, and to talk to one of our experts, please contact us to schedule a demo.

 

 

Building a streaming data pipeline to cloud services is essential to moving enterprise datain real time between on-premises and cloud environments.

Extending data infrastructure to hybrid and multi-cloud architectures enables businesses to scale easily and leverage a variety of powerful cloud-based services. Data must be a key consideration when migrating applications to the cloud, to ensure that services have access to the data they need, when they need it, and in the format required.

Although adopting a cloud architecture offers significant benefits in terms of savings and flexibility, it also creates challenges in managing data across different locations. Using traditional approaches to data movement introduces latency for applications that demand up-to-the-second information. Batch ETL methods are also constrained by the number of sources and targets that can be supported.

The Striim platform simplifies the building of a streaming data pipeline to cloud, allowing organizations to leverage fully connected hybrid cloud environments across a variety of use cases. Examples include offloading operational workloads, and extending a data center to the cloud, as well as gaining insights from cloud-based analytics.

Taking advantage of Striim’s easy-to-use wizards to build and modify a highly reliable and scalable data pipeline to cloud environments, data can be moved continuously and in real time from heterogenous on-premises or cloud-based sources – including transactional databases, log files, sensors, Kafka, Hadoop, and NoSQL databases – without slowing down source systems. Using non-intrusive, real-time change data capture (CDC) ensures continuous data synchronization by moving and processing only changed data.

Striim feeds real-time data with full-context via the data pipeline to cloud and other targets, processing and formatting it in-memory. Filtering, transforming, aggregating, enriching, and analyzing data all occurs while the data is in-flight, before delivery of the relevant data sets to multiple endpoints.

Built-in data pipeline to cloud monitoring via interactive dashboards and real-time alerts allows users to visualize the data flow and the content of datain real time. With up-to-the-second visibility of the data pipeline to cloud infrastructure, users can quickly and easily verify the ingestion, processing, and delivery of their streaming data.

To read more about building a real time data pipeline to cloud using Striim, please go to: https://www.striim.com/use-case/real-time-analytics/

If you would like to see how a data pipeline to cloud is built, please schedule a demo with one of our technologists.