A new real-time analytics platform that combines probabilistic data structures, approximate query processing and in memory distributed data management to deliver powerful analytic querying and alerting capabilities on Apache Spark at a fraction of the cost of traditional big data analytics platforms. Learn more
OLTP + OLAP Inside Spark
Instantaneous analysis on streaming data, written to a data store that support high writes, point updates, point queries as well as analytic queries.
Spark & SQL interfaces
SnappyData delivers ease of use by exposing the Spark programming model and SQL query execution.
Synopsis Data Structures
Data structures with very low memory footprint designed for fast approximate querying.
How does it work?
SnappyData fuses the Spark computational engine with a highly available, multi-tenanted in-memory database to execute OLAP & OLTP queries on streaming data. Further, SnappyData can store data in a variety of synopsis data structures to provide extremely fast responses on less resources. Finally, applications can either submit Spark programs or connect using JDBC/ODBC to run interactive or continuous SQL queries. Learn more
Deploy SnappyData and connect it to historical or streaming data
Write interactive or continuous SQL queries on connected data
Use SnappyData’s synopsis structures to speed up execution
Committed to Open Source
SQL & Spark interfaces
Concurrent OLAP+OLTP workloads
Synopsis data structures designed for speed
Integration with Spark ecosystem
Always-on high availability
Download our open source release
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