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Faster Analytics for Fast Data with Apache Pinot and Flink SQL

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Faster Analytics for Fast Data with Apache Pinot and Flink SQL
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69
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CC Attribution 3.0 Unported:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal purpose as long as the work is attributed to the author in the manner specified by the author or licensor.
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OLAP data stores like Apache Pinot are emerging to serve low-latency analytical queries at web scale. With its columnar data format and rich indexing strategies, Pinot is a perfect fit for running complex, interactive queries on multi-dimensional data within milliseconds. In some cases, though, streaming data will require non-trivial pre-processing that is not supported in Pinot, like joins and pre-aggregations. What then? In this talk, we’ll cover the benefits of combining Pinot and stream processing with Flink SQL to power near real-time OLAP use cases, and build a simple demo to analyze streaming Twitch data (#meta) — from ingestion to visualization!