Video: Building the Ideal Stack for Real-Time Analytics

Building a real-time application requires connecting the pieces of your data pipeline to ingest, transform, store and make data easily accessible at sub-second speeds. A typical architecture consists of an ingestion layer that captures feeds, a transformation tier that distills information and delivers formats, a storage layer for persistence and analytics, and querying capabilities using SQL to power real-time dashboards. As new applications generate increased data complexity and volume, it's essential to build an infrastructure for fast data analysis to enable benefits like real-time dashboards, predictive analytics, and machine learning. Industry leaders are sharing their experiences on building ideal technology stacks for real-time analytics, such as Pinterest and an energy company that used Kafka and SingleStore to monitor sensor data and reduce risk of drill bit breakage.

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