On-Demand Recordings from PrestoCon’s, Webinars, Meetups, and more
Tecton is the leading feature platform for real-time machine learning. Rather than build new SQL engines from scratch, Tecton connects to your existing engine to transform raw data into features for machine learning. This talk will cover Tecton’s new integration with Athena for feature engineering. Derek will demonstrate how Tecton with Athena is the fastest way to build feature pipelines and put new models in production.
In this session, Yoni will present on querying streaming data with Presto and Amazon Athena including performance, data partitioning and compaction. In addition, we will demo using the Upsolver platform with Amazon Athena. In addition, he will share what they are working on with Prestodb.
In complex analytics queries, we often see repeated expressions, for example parsing the same JSON column but extracting different fields, elaborate CASE statement with common predicates and different ones. Previously, Presto will compute the same expression many times as they appear in query. With common sub expression optimization, we would only evaluate the same expression once within the same project operator or filter operator. In our workload, we’ve seen 3x improvements on certain queries with expensive common sub expressions like JSON_PARSE. Microbenchmark also shows a consistent ~10% performance improvement for simple common sub-expressions like x + y. In this talk, we will talk about how this is implemented.