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MemSQL paves a smoother path to Spark for real-time analytics

MemSQL paves a smoother path to Spark for real-time analytics

Its new tool lets companies tap Spark without writing any code

Now that companies are recognizing the benefits of analytics and big data, the next step is putting those benefits within closer reach. Toward that end, MemSQL on Thursday unveiled a new tool designed to help companies tap Apache Spark without writing any code.

Spark Streamliner is a tool that integrates MemSQL's in-memory database and Apache Spark's in-memory data-processing framework for streaming data from real-time sources such as sensors, Internet-of-Things (IoT) devices, transactions, applications and logs.

Offering "one click" deployment of integrated Spark along with a Web-based interface, it allows users to create multiple data pipelines in minutes, perform custom transformations in real time and develop new analytics applications, MemSQL said.

Hooked up with a real-time data source like Apache Kafka, Spark Streamliner supports thousands of concurrent users running real-time analytical queries. Data is streamed directly into MemSQL. There's no need to extract, transform and load (ETL) data in batch fashion; rather, users can process data as it streams in, thereby eliminating analytic latency.

Featuring a simple SQL interface, Spark Streamliner can easily be connected to popular analytical tools, MemSQL said. Users can also share a single resource pool for multiple pipelines, effectively reducing their total hardware footprint.

A video demonstrates MemSQL Spark Streamliner in action. The open source tool and a library of example extractors and transformers are now available on GitHub.


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