Get the slides: https://www.datacouncil.ai/talks/presto-optimizing-performance-of-sql-on-anything?hsLang=en

ABOUT THE TALK

Presto, an open source distributed SQL engine, is widely recognized for its low-latency queries, high concurrency, and native ability to query multiple data sources. Proven at scale in a variety of use cases at Airbnb, Comcast, GrubHub, Facebook, FINRA, LinkedIn, Lyft, Netflix, Twitter, and Uber, in the last few years Presto experienced an unprecedented growth in popularity in both on-premises and cloud deployments over Object Stores, HDFS, NoSQL and RDBMS data stores.

With the ever-growing list of connectors to new data sources such as Azure Blob Storage, Google Cloud Storage, Elasticsearch, Netflix Iceberg, Apache Kudu, and Apache Pulsar, the recently introduced Cost-Based Optimizer in Presto must account for heterogeneous inputs with differing and often incomplete data statistics. This talk will explore this topic in detail as well as discuss best use cases for Presto across several industries. In addition, we will present the recent Presto advancements such as Geospatial analytics at scale and the project roadmap going forward.

ABOUT THE SPEAKER

Kamil is a technology leader in the large scale data warehousing and analytics space. He is CTO of Starburst, the enterprise Presto company. Prior to co-founding Starburst, Kamil was the Chief Architect at the Teradata Center for Hadoop in Boston, focusing on the open source SQL engine Presto. Previously, he was the co-founder and chief software architect of Hadapt, the first SQL-on-Hadoop company, acquired by Teradata in 2014.

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