Getting started with fast data architecture
summary
Argues that streaming needs sub-second near real-time processing and elasticity so systems keep running under load, and that practical fast-data stacks use components like Spark and Elasticsearch (and other battle-tested pieces) wired into feedback loops that learn and tune over time. Recommends choosing an acceptable delta of error (example cited ~5–10%) per use case. Announces a Lightbend fast-data platform reaching alpha soon and emphasizes pragmatic reuse over rebuilding everything.
AI-written summary · about
chapters (5)
- 0:00Introduction
- 0:15Reactive architectures
- 6:30Fast data expert
- 8:35Challenges of fast data
- 10:09Future of Lightbend
related talks
39mDeploying Fast Data Pipelines
47mExpert Panel: Current & Future State of the Art in Real-Time Data Infrastructure
32mBig Data Processing : Where do we go next ?
45mFrom Big Data to Fast Data: Apache Spark
16mLife is But a (Data) Stream: Building Quality Data Pipelines
08mReal-Time Data Systems: Speed, Scale, and Resilience
45mModern Data Architecture
10mRoad to Real-Time Digital Business