MemSQL, a leader in real-time databases for transactions and analytics, announced Spark Streamliner, an integrated Spark solution to give enterprises immediate access to real-time analytics. With more devices, interconnectivity and user demand, enterprises face an increasing number of data points across varied sources, and need to synthesize that data. By deploying real-time data pipelines with MemSQL and Spark, companies can keep up with this dynamic data flow, bringing each data point into the light of day. Transactional and analytical features in MemSQL enable businesses to build applications meeting each individual user’s needs without sacrificing performance.
Enterprises see the in-memory performance and functionality of Spark as a valuable building block for real-time data pipelines and streaming analytics. MemSQL Spark Streamliner offers a one click deployment of integrated Apache Spark for fast installs, and a web-based UI for pipeline setup. This lets companies use Spark without writing code. Spark Streamliner enables users to create multiple data pipelines in minutes, perform custom transformations in real-time, and develop innovative applications with fresh analytics, all while eliminating the pain of batch ETL.
Spark Streamliner increases the opportunity for enterprises to work with real-time data, and now they can easily persist multiple data streams for ongoing analytics,” said Eric Frenkiel, CEO and co-founder, MemSQL. “Streamliner is the first of many integrated Spark solutions to operationalize Spark, delivering intuitive access to sophisticated capabilities with the relational and in-memory architecture of MemSQL.”
MemSQL Spark Streamliner, available as open source on GitHub, accelerates the development of innovative applications by persisting real-time data streams and enabling access with full transactional SQL. Business solutions supporting real-time analytics, cybersecurity, and personalization, along with mobile applications and the Internet of Things, all benefit from the ability to deploy and manage multiple real-time data pipelines through a single interface.
SQL is the traditional standard for data manipulation. Spark is the new standard for data transformation. Spark plus a RDBMS can be a powerful combination,” said Curt Monash, president, Monash Research, and editor of DBMS 2. “For many use cases, SQL offers the familiar flexibility of transactions, declarative queries, and joins.”
Spark Streamliner, when fed by a real-time data source like Apache Kafka, supports thousands of concurrent users running real-time analytical queries, reduces data latency from days or hours down to zero, and streams data directly into the MemSQL in-memory rowstore or flash and disk-based columnstore. A simple SQL interface allows enterprises to capitalize on SQL’s ubiquity by easily connecting popular analytical tools. Spark Streamliner also allows customers to share a single resource pool for multiple pipelines, effectively reducing the total hardware footprint.
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