Real-Time Analytics with Kafka and SingleStore

Connected devices, IoT, and on-demand user expectations push enterprises to deliver instant answers at scale. Applications that anticipate customer needs and fulfill expectations for fast, personalized services win the attention of consumers. Perceptive companies have taken note of these trends and are turning to memory-optimized technologies like Apache Kafka and SingleStore to power real-time analytics.

High Speed Ingest

Building real-time systems begins with capturing data at its source and using a high-throughput messaging system like Kafka. Taking advantage of a distributed architecture, Kafka is built to scale producers and consumers by simply adding servers to a given cluster. This effective use of memory, combined with commit log on disk, provides ideal performance for real-time pipelines and durability in the event of server failure. From there, data can be transformed and persisted to a database like SingleStore.

Fast, Performant Data Storage

SingleStore persists data from real-time streams coming from Kafka. By combining transactions and analytics in a memory-optimized system, data is rapidly ingested from Kafka, then persisted to SingleStore. Users can then build applications on top of SingleStore also supplies the application with the most recent data available.

We teamed up with the folks at Confluent, the creators of Apache Kafka, to share best practices for architecting real-time systems at our latest meetup. The video recording and slides from that session are now available below.

Meetup Video Recording: Real-Time Analytics with Confluent and SingleStore
Watch now to:

See a live demo of our new showcase application for modeling predictive analytics for global supply chain management
Learn how to architect systems for IoT streaming data ingestion and real-time analytics
Learn how to combine Kafka, Spark, and SingleStore for monitoring and optimizing global supply chain processes with real-time analytics