Video: Building the Ideal Stack for Real-Time Analytics


Mason Hooten

 Digital Marketing Associate

Video: Building the Ideal Stack for Real-Time Analytics

Building a real-time application starts with connecting the pieces of your data pipeline.

To make fast and informed decisions, organizations need to rapidly ingest application data, transform it into a digestible format, store it, and make it easily accessible. All at sub-second speed.

A typical real-time data pipeline is architected as follows:

  • Application data is ingested through a distributed messaging system to capture and publish feeds.
  • A transformation tier is called to distill information, enrich data, and deliver the right formats.
  • Data is stored in an operational (real-time) data warehouse for persistence, easy application development, and analytics.
  • From there, data can be queried with SQL to power real-time dashboards.

As new applications generate increased data complexity and volume, it is important to build an infrastructure for fast data analysis that enables benefits like real-time dashboards, predictive analytics, and machine learning.

At this year’s Spark Summit East, SingleStore Product Manager, Steven Camina shared how to build an ideal technology stack to enable real-time analytics.

video-building-the-ideal-stack-for-real-time-analyticsVideo: Building the Ideal Stack for Real-Time Analytics

slides-building-the-ideal-stack-for-real-time-analyticsSlides: Building the Ideal Stack for Real-Time Analytics

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