MemSQL Launches Spark Streamliner Giving Customers Immediate Access to Real-Time Analytics

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MemSQL, a provider of real-time databases for transactions and analytics, has announced Spark Streamliner, an integrated Spark solution to give enterprises immediate access to real-time analytics.

With more devices, interconnectivity and user demand, enterprises are encountering an a greater number of data points across different sources, and the need to integrate varied data types. By deploying real-time data pipelines with MemSQL and Spark, companies can keep up with this dynamic flow of data while transactional and analytical features in MemSQL enable businesses to build applications that meet each users' needs without sacrificing performance. 

MemSQL Spark Streamliner offers a one click deployment of integrated Apache Spark to eliminate the pain of batch ETL, and provides ease-of-use for broad adoption. With a web-based UI for pipeline setup, users can create multiple real-time data pipelines in minutes, perform custom transformations in real-time, and develop innovative applications inspired by fresh analytics.

“Spark has opened the door for enterprises to interact more quickly and efficiently with data. With this integration, enterprises can go from many narrow purpose solutions to fewer multi-purpose solutions,” said Eric Frenkiel, CEO at MemSQL. “Spark Streamliner is the first of many integrated Spark offerings. Our vision is to operationalize Spark for a wide range of use cases so customers and partners can easily take advantage of the data processing framework available in Spark and spend their time gaining actionable insights from data.”

MemSQL Spark Streamliner, available as open source on GitHub, is expected to fuel development of innovative applications inspired by real-time data and easy access with full transactional SQL. Areas such as mobile applications, trading analytics, cybersecurity, ad personalization, omnichannel retail, and Internet of Things are expected to benefit from the capability to deploy and manage multiple real-time data pipelines with a single interface and shared resource pool.

Spark Streamliner, along with a real-time data source like Apache Kafka, can support thousands of concurrent users running real-time analytical queries, reduce data latency from days or hours to zero and stream data directly into MemSQL across the memory-based rowstore or disk-based columnstore. In addition, with an easy-to-use SQL interface, enterprises can capitalize on its ubiquity and breadth to connect numerous analytical  tools.

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