Simplify Your Data Architecture With The Presto Distributed SQL Engine

Summary

Databases are limited in scope to the information that they directly contain. For analytical use cases you often want to combine data across multiple sources and storage locations. This frequently requires cumbersome and time-consuming data integration. To address this problem Martin Traverso and his colleagues at Facebook built the Presto distributed query engine. In this episode he explains how it is designed to allow for querying and combining data where it resides, the use cases that such an architecture unlocks, and the innovative ways that it is being employed at companies across the world. If you need to work with data in your cloud data lake, your on-premise database, or a collection of flat files, then give this episode a listen and then try out Presto today.

Announcements

• Hello and welcome to the Data Engineering Podcast, the show about modern data management
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• Your host is Tobias Macey and today I’m interviewing Martin Traverso about PrestoSQL, a distributed SQL engine that queries data in place

Interview

• Introduction
• How did you get involved in the area of data management?
• Can you start by giving an overview of what Presto is and its origin story?
  • What was the motivation for releasing Presto as open source?
• For someone who is responsible for architecting their organization’s data platform, what are some of the signals that Presto will be a good fit for them?
  • What are the primary ways that Presto is being used?
• I interviewed your colleague at Starburst, Kamil 2 years ago. How has Presto changed or evolved in that time, both technically and in terms of community and ecosystem growth?
• What are some of the deployment and scaling considerations that operators of Presto should be aware of?
• What are the best practices that have been established for working with data through Presto in terms of centralizing in a data lake vs. federating across disparate storage locations?
• What are the tradeoffs of using Presto on top of a data lake vs a vertically integrated warehouse solution?
• When designing the layout of a data lake that will be interacted with via Presto, what are some of the data modeling considerations that can improve the odds of success?
• What are some of the most interesting, unexpected, or innovative ways that you have seen Presto used?
• What are the most interesting, unexpected, or challenging lessons that you have learned while building, growing, and supporting the Presto project?
• When is Presto the wrong choice?
• What is in store for the future of the Presto project and community?

Contact Info

• LinkedIn
• @mtraverso on Twitter
• martint on GitHub

Parting Question

• From your perspective, what is the biggest gap in the tooling or technology for data management today?

Closing Announcements

• Thank you for listening! Don’t forget to check out our other show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used.
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Links

• Presto
• Starburst Data
  • Podcast Episode
• Hadoop
• Hive
• Glue Metastore
• BigQuery
• Kinesis
• Apache Pinot
• Elasticsearch
• ORC
• Parquet
• AWS Redshift
• Avro
  • Podcast Episode
• LZ4
• Zstandard
• KafkaSQL
• Flink
  • Podcast Episode
• PyTorch
  • Podcast.__init__ Episode
• Tensorflow
• Spark

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA