May 21, 2018

PrestoDB and Starburst Data with Kamil Bajda-Pawlikowski - Episode 32

42 minutes

Summary

Most businesses end up with data in a myriad of places with varying levels of structure. This makes it difficult to gain insights from across departments, projects, or people. Presto is a distributed SQL engine that allows you to tie all of your information together without having to first aggregate it all into a data warehouse. Kamil Bajda-Pawlikowski co-founded Starburst Data to provide support and tooling for Presto, as well as contributing advanced features back to the project. In this episode he describes how Presto is architected, how you can use it for your analytics, and the work that he is doing at Starburst Data.

Preamble

Hello and welcome to the Data Engineering Podcast, the show about modern data management

When you’re ready to build your next pipeline you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to run a bullet-proof data platform. Go to dataengineeringpodcast.com/linode to get a $20 credit and launch a new server in under a minute.

Go to dataengineeringpodcast.com to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch.

Your host is Tobias Macey and today I’m interviewing Kamil Bajda-Pawlikowski about Presto and his experiences with supporting it at Starburst Data

Interview

Introduction

How did you get involved in the area of data management?

Can you start by explaining what Presto is?

What are some of the common use cases and deployment patterns for Presto?

How does Presto compare to Drill or Impala?

What is it about Presto that led you to building a business around it?

What are some of the most challenging aspects of running and scaling Presto?

For someone who is using the Presto SQL interface, what are some of the considerations that they should keep in mind to avoid writing poorly performing queries?

How does Presto represent data for translating between its SQL dialect and the API of the data stores that it interfaces with?

What are some cases in which Presto is not the right solution?

What types of support have you found to be the most commonly requested?

What are some of the types of tooling or improvements that you have made to Presto in your distribution?

What are some of the notable changes that your team has contributed upstream to Presto?

Contact Info

Website

E-mail

Twitter – @starburstdata

Twitter – @prestodb

Parting Question

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

Links

Starburst Data

Presto

Hadapt

Hadoop

Hive

Teradata

PrestoCare

Cost Based Optimizer

ANSI SQL

Spill To Disk

Tempto

Benchto

Geospatial Functions

Cassandra

Accumulo

Kafka

Redis

PostGreSQL

The intro and outro music is from The Hug by The Freak Fandango Orchestra / {CC BY-SA](http://creativecommons.org/licenses/by-sa/3.0/)?utm_source=rss&utm_medium=rss

Support Data Engineering Podcast

...more

View all episodes

By Tobias Macey

4.5

140140 ratings