A real-time transactional-analytical system needs to combine transactional and streaming data in a single high-performance database 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 for enhanced real-time decisioning and insights.

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How it works

Aerospike Connect for Spark (Figure 1.) enables companies to directly integrate the Aerospike database with their existing Spark infrastructure. In addition, Aerospike Connect for Spark allows companies to combine transactional and historical data stored in the Aerospike database with streaming event data for consumption by machine learning and artificial intelligence engines using Apache Spark.

Aerospike Connect for Spark diagram

Figure 1. Aerospike Connect for Spark

Why Aerospike Connect for Spark

Aerospike Connect for Spark combines streaming data with a high-performance database to create a join with the transactional data. This enables better real-time decisioning and insights.

Real-time Analytics

Get real-time analytics by operating on larger datasets yet with a smaller cluster footprint thus lower TCO

Gain Closed-loop Business Insights

Gain closed-loop business insights by operating on both transactional and stream datasets

Rapid Development

Rapidly develop applications using Spark libraries – no additional skill set required.

Flexibility
Flexibility

Flexibility to utilize either the embedded or customer’s existing Spark instances for analysis

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