Sign up for our newsletter and get the latest big data news and analysis.

End-to-End, SQL-based Streaming Data Platform Simplifies and Speeds Delivery of Kafka-driven Analytics Applications

Striim, Inc., provider of a leading end-to-end, real-time data integration and streaming analytics platform, announced that it has launched version 3.7.4 of the Striim platform, bolstering its ease of use, connectivity, manageability, and scalability for delivering streaming analytics applications involving Apache Kafka.

The Striim platform’s enterprise-grade, SQL-based integration with Apache Kafka has been generally available for several product releases, and boasts numerous deployments among Fortune 500 customers. These customers are using the Striim solution to enable high-volume, high-velocity data correlation and analytics involving Kafka data, along with other enterprise data sources. Based on input from these production customers, Striim has further strengthened the platform’s ease-of-use, connectivity, manageability, and scalability in support of Kafka-related deployments.

It’s a daunting challenge, integrating multiple tiers when building Streaming Applications with Kafka as an underlying message store. Striim makes that problem go away,” said Alok Pareek, co-founder and EVP of Products at Striim. “For several years, Striim has been the leader in defining an integrated Streaming Data Platform that includes not just Kafka, but also SQL-based applications and universal connectivity with a wide variety of event delivery semantics. With the 3.7.4 release, we have added Kafka diagnostic utilities, advanced monitoring metrics, and additional connectors to reduce the complexity of managing Kafka in production environments.”

Striim 3.7.4 introduces new utilities specifically designed to speed the adoption of Kafka as part of an end-to-end flow. These utilities help users quickly and easily scale Kafka applications by gathering baseline performance metrics for real world applications that involve parsing, formatting, buffer management, and external connectivity. These enhancements in the Kafka producer, consumer, and broker metrics help increase the monitoring, manageability and scalability of streaming applications.

SQL-query-based processing and analytics, a drag-and-drop UI, configuration wizards, and custom utilities such as these make the Striim platform the easiest solution to deliver end-to-end streaming integration and analytics applications involving Kafka.

In addition, Striim has bolstered the platform’s connectivity with hundreds of data sources and targets to include a new real-time Smart NetFlow Reader. The Striim Cloud Readiness offering for Kafka has also been expanded, enabling writing from Kafka queues to AWS Redshift and S3, Google Cloud, and several Microsoft Azure solutions including Azure SQL Server, Azure Storage, and Azure HDInsight.

In the area of stream processing, Striim has augmented its solution’s Exactly Once Processing (E1P) guarantees across the data pipeline, spanning the entire end-to-end streaming architecture. Because Apache Kafka is built into the Striim platform, a parallel Kafka stream can act as an intermediary persistent store, helping to ensure that users are processing and writing data once and only once.

 

Sign up for the free insideBIGDATA newsletter.

Leave a Comment

*

Resource Links: