Quick Stats – Runtime ANALYZE for Better Query Plans – Anant Aneja, Ahana

Quick Stats – Runtime ANALYZE for Better Query Plans – Anant Aneja, Ahana

An optimizer’s plans are only as good as the estimates available for the tables its querying. For queries over recently ingested data that is not yet ANALYZE-d to update table or partition stats, the Presto optimizer flies blind; it is unable to make good query plans and resorts to syntactic join orders. To solve this problem, we propose building ‘Quick Stats’ : By utilizing file level metadata available in open data lake formats such as Delta & Hudi, and by examining stats from Parquet & ORC footers, we can build a representative stats sample at a per partition level. These stats can be cached for use be newer queries, and can also be persisted back to the metastore. New strategies for tuning these stats, such as sampling, can be added to improve their precision.

Ending DAG Distress: Building Self-Orchestrating Pipelines for Presto – Roy Hasson, Upsolver

Ending DAG Distress: Building Self-Orchestrating Pipelines for Presto – Roy Hasson, Upsolver

Ending DAG Distress: Building Self-Orchestrating Pipelines for Presto – Roy Hasson, Upsolver dbt and Airflow is a popular combination for creating and scheduling batch data modeling and transformation jobs that execute in a data warehouse like Snowflake. Presto users querying the data lake need a similar solution that is simple to use and makes it easy to ingest, model, transform and maintain datasets, without having to write or manage complex DAGs. In this session you will learn how Upsolver built a tool that allows engineers, developers and analysts to write data pipelines using SQL. Pipelines are automatically orchestrated, are data-aware and maintain a consistent data contract between each stage of the pipeline. You will also learn how to introduce the idea of data products into your company to enable more self-service for your Presto users.

Querying streaming data with Presto, Amazon Athena and Upsolver

Querying streaming data with Presto, Amazon Athena and Upsolver

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.