Simple, unified programming
for the big data audience
Simple, unified programming for the big data audience
SnappyData’s core programming abstraction is the same as Spark. It exposes all its data as a Spark DataSet or DataFrame (Spark’s Resilient Distributed DataSet).Using this simple extension, Spark can capture a wide range of processing workloads that previously needed separate engines, including SQL, streaming, machine learning, and graph processing.
So, for instance, you can write a stream processing pipeline that consumes a stream, transactionally ingests it into a Snappydata table, draws insight by running a query combining a stream with historical data and even updates a Machine learning model all using a single programming paradigm.
Spark's generality has several important benefits. First, applications are easier to develop because they use a unified API. Second, it is more efficient to combine processing tasks; whereas prior systems required writing the data to storage to pass it to another engine, Spark can run diverse functions over the same data, often in memory.
SnappyData Programming Features
100% compatible with Spark APIs
Support high concurrency across users and applications
Transactions and mutability through SQL and Spark API extensions
High availability (not just Fault tolerance)
Enterprise grade storage and security
SQL extensions: Insert, delete, update, constraints, indexes and more
Declarative SQL support for Stream processing