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.
Your data platform needs to be scalable, fault tolerant, and performant, which means that you need the same from your cloud provider. Linode has been powering production systems for over 17 years, and now they’ve launched a fully managed Kubernetes platform. With the combined power of the Kubernetes engine for flexible and scalable deployments, and features like dedicated CPU instances, GPU instances, and object storage you’ve got everything you need to build a bulletproof data pipeline. If you go to dataengineeringpodcast.com/linode today you’ll even get a $100 credit to use on building your own cluster, or object storage, or reliable backups, or… And while you’re there don’t forget to thank them for being a long-time supporter of the Data Engineering Podcast!
- Hello and welcome to the Data Engineering Podcast, the show about modern data management
- What are the pieces of advice that you wish you had received early in your career of data engineering? If you hand a book to a new data engineer, what wisdom would you add to it? I’m working with O’Reilly on a project to collect the 97 things that every data engineer should know, and I need your help. Go to dataengineeringpodcast.com/97things to add your voice and share your hard-earned expertise.
- When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $60 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!
- You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data platforms. For more opportunities to stay up to date, gain new skills, and learn from your peers there are a growing number of virtual events that you can attend from the comfort and safety of your home. Go to dataengineeringpodcast.com/conferences to check out the upcoming events being offered by our partners and get registered today!
- Your host is Tobias Macey and today I’m interviewing Martin Traverso about PrestoSQL, a distributed SQL engine that queries data in place
- 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?
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
- 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.
- Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
- If you’ve learned something or tried out a project from the show then tell us about it! Email email@example.com) with your story.
- To help other people find the show please leave a review on iTunes and tell your friends and co-workers
- Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat
- Starburst Data
- Glue Metastore
- Apache Pinot
- AWS Redshift