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Businesses need to react to results immediately; to achieve this, real-time processing is becoming a requirement in many analytic verticals. But sometimes, the move from batch to real-time can leave you in a pinch. How do you handle and correct mistakes in your data? How do you migrate a new system to real-time along with historical data?
Let’s start with how to run Apache Druid locally with your containerized-based development environment. While streaming real-time events from Kafka into Druid, an S3 Complaint Store captures messages via Kafka Connect, for historical processing. An exploration of performance implications when the real-time stream of events contains historical data and how that affects performance and the techniques to prevent those issues, leaving a high-performance analytic platform supporting real-time and historical processing.
You’ll leave with the tools of doing real-time analytic processing and historical batch processing from a single source of truth. Your Druid cluster will have better rollups (pre-computed aggregates) and fewer segments, which reduces cost and improves query performance.