Real-time Analytics with Presto and Apache Pinot
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The document discusses real-time analytics using Presto and Apache Pinot, highlighting their capabilities for user-facing applications and business metrics. It mentions the ingestion of millions of events per second, the handling of thousands of queries per second, and the balance between latency and flexibility. Additionally, it provides links for getting started with tutorials and community support.
Real-time Analytics with Presto and Apache Pinot
- 1. Real-time Analytics with Presto and Apache Pinot Xiang Fu Sept. 24, 2020
- 2. User Facing Applications Business Facing Metrics Apache Pinot Anomaly Detection - Ingestion: Millions of events/sec - Workload: Thousands of queries/sec - Performance: Millisecond
- 3. Fact Table Dimension Table Pre-Join Pre-Aggregation Pre-Cube Presto Pinot Latency Flexibility low high low high Latency vs Flexibility
- 6. Thank you - Getting Started https://tinyurl.com/prestoPinotTutorial - Pinot Slack Channel https://tinyurl.com/pinotSlackChannel Contributors: Devesh Agrawal, Dharak Kharod, Haibo Wang, James Sun, Venki Korukanti, Xiang Fu, Zhenxiao Luo
Editor's Notes
- #3: Realtime OLAP Database Columnar, Indexed Storage Low latency analytics Distributed – highly available, reliable, scalable Lambda architecture Offline data pushes Real-time stream ingestion Open Source
- #6: Pinot - Fast single table OLAP Presto - Powerful connector ecosystem Complete system - covers entire landscape Get the best of Presto and Pinot Proven stack at Uber and many more