MEMSQL STREAMLINER

MemSQL Streamliner is the easiest way to use Apache Spark with a fast, operational database to drive real-time analytics.

Three Ways to Use Streamliner

DEPLOY SPARK WITH STREAMLINER

MemSQL Streamliner is the simplest way to manage real-time data pipelines using Apache Spark. Test, deploy, and manage multiple data pipelines through a rich, graphical UI and eliminate batch ETL.

CONNECT MEMSQL WITH APACHE SPARK

MemSQL provides the easiest, most performant path for operationalizing Spark in the enterprise. The MemSQL Spark Connector rapidly transfers data in parallel between MemSQL and Spark.
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IMPORT FROM POPULAR DATA STORES

Connect MemSQL with HDFS, S3, and MySQL using Streamliner Import which includes an utility to import data from popular datastores. MemSQL is the perfect complement to these data stores, providing the tools to load and analyze real-time data.

Using MemSQL Streamliner for Real-Time Pipelines

MemSQL Streamliner is the simplest way to manage real-time data pipelines using Apache Spark. Test, deploy, and manage multiple data pipelines through a rich, graphical UI and eliminate batch ETL.

One-click deployment of Apache Spark
Deploy Spark using MemSQL Ops for a seamlessly integrated pipeline

Eliminate batch ETL
Process data as it streams in to eliminate analytic latency

Manage multiple pipelines through a single interface
Streamliner dynamically allocates resources to pipelines to better utilize available infrastructure

streamliner for apache spark

Get Started with Streamliner

Download and install MemSQL Community Edition.

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See Streamliner in Action

 

Real-time Analytics at Pinterest

Find out how Pinterest runs SQL queries on real-time events by ingesting data into MemSQL using Spark Streaming.

READ THE BLOG POST

Bring your business fully online with Kafka, Spark, and MemSQL

Immediate SQL access to real-time data
Use MemSQL to ingest, persist, and query real-time data with analytical SQL

Scale out as data volumes grow
All three systems are distributed and fault-tolerant

Structure data on the fly
Leverage Kafka and Spark's versatility to process many data formats so, when data lands in MemSQL, it's already stuctured for analytics

streamliner architecture