Create a live, in-memory datamart from MYSQL/Oracle/SQL Server and other databases

Your traditional relational datawarehouse/database can be a bottleneck for reporting and analytics. Move data to SNAP for faster response times.
Leverage SNAP beyond SQL. Run machine learning workloads and SQL on one single data access layer.

Query data in S3/HDFS

Query data on S3 or Hadoop in seconds using your favorite visualization tool.

Speed and Scale with the SQL you know

Use SQL with joins without worrying about query times. SNAP allows you to express your SQL joins but eliminates joins under the hood.

Ad-Hoc Slice and Dice

Query multi-dimensional datasets and drill down on dimensions in the datasets with interactive response times. Perform aggregations on the fly and keep data at its lowest grain, eliminating expensive extracts and summary tables.

Migrate from legacy datamarts and OLAP tools to a modern Spark SQL powered in-memory datamart

Sparkline SNAP combines the benefits of old style OLAP and MPP databases with a modern architecture designed from the ground up for Big Data

Deploy on the cloud or on-premise

Variety of use cases and workloads

Tableau + SNAP

Running ad-hoc queries from Tableau at interactive response times on Big Data is a challenge. Running it on live datasets, real time with hundreds of users accessing various segments of the data can seem impossible. SNAP can now turbo charge your Tableau visualizations by connecting to live data with think time response times, directly acting on terabyte scale datawarehouses and datamarts. No extracts needed.

Python + SNAP

The modern business analyst is part business analyst and part data scientist. The new “Business Scientist” uses Python and R with Jupyter Notebooks extracting stories from raw data. Traditionally, data science was restricted to local memory computations and data that can fit into that. With scale out architectures like SNAP, data is completely unshackled. Drill down to the lowest detail in seconds.

WebServices + SNAP

Querying legacy datamarts/Hadoop based stacks through REST API’s for sub second response times is impossible. Traditional databases/datamarts are not designed for modern consumption. SNAP can respond to point queries in milliseconds. Now you can write APIs that can query SNAP 24×7 and bring any data from your datawarehouse to your web applications. Build conversational analytics, sending queries constantly to SNAP for immediate response.


SQL is the language of datawarehousing. In most meaningful data warehousing projects, business domain data is modeled and queried as star joins. Often SQL queries can be a complicated set of correlated sub-queries, common table expressions, windowing functions etc. Most datwarehouses cannot handle the demands of interactivity when dealing with complex SQL expressions.

With SNAP, you can now express your queries with SQL, using its full power, and get answers in seconds. SNAP eliminates joins behind the scenes but let you keep the logical model of using joins in your SQL. SNAP is built with advanced optimization techniques designed to reduce scans of facts to exactly what is needed in the query.

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