Month: May 2019

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.