The Aerospike connector for Spark enables the creation of data-intensive applications such as AI/ML and ETL with advanced Apache Spark 3.0 tools.
A real-time transactional-analytical system needs to combine transactional and streaming data in a single high-performance data platform that can operate as fast as the inbound data streams in. It also needs to work with various analytics frameworks including machine learning and artificial intelligence. Aerospike Connect for Spark addresses these requirements by combining streaming data with historical data so organizations can act in real time.
Aerospike Connect for Spark enables the creation of data-intensive applications such as AI/ML, ETL, and more with familiar and easy to use Spark tools.
Aerospike Connect for Spark supports streaming APIs that leverage Structured Spark Streaming to provide very low latency for both reads and writes. This enables AI/ML use cases that leverage Aerospike as a system of engagement in their Spark Streaming pipeline. Aerospike Connect for Spark coupled with the Aerospike Database scan-by-partition capability, predicate filtering and mapping of Aerospike partitions to Spark partitions allows massive parallelization.
Conduct advanced and predictive data analytics across massive amounts of multi-modal data in real-time across disparate sources.
Aerospike Connect for Spark makes it easy for enterprises to address AI/ML use cases requiring real time actions across billions of transactions.
Learn more about Aerospike Connect for Spark.