As more developers and data scientists try Apache Spark, they ask questions about persistence, transactions and mutable data, and how to deploy statistical models in production. To address some of these questions, our CEO Eric Frenkiel recently wrote an article for Data Informed explaining key use cases integrating SingleStore and Spark together to drive concrete business value.
The article explains how you can combine SingleStore and Spark for applications like stream processing, advanced analytics, and feeding the results of analytics back into operational systems to increase efficiency and revenue. As distributed systems with speedy in-memory processing, SingleStore and Spark naturally complement one another and form the backbone of a flexible, versatile real-time data pipeline.
Read the full article here.
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The 79 page guide covers how to design, build, and deploy Spark applications using the SingleStore Spark Connector. Inside, you will find code samples to help you get started and performance recommendations for your production-ready Apache Spark and SingleStore implementations.
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