Data Engineering Podcast Paving The Road For Fast Analytics On Distributed Clouds With The Yellowbrick Data Warehouse
May 28, 2021
52:41
Summary
The data warehouse has become the focal point of the modern data platform. With increased usage of data across businesses, and a diversity of locations and environments where data needs to be managed, the warehouse engine needs to be fast and easy to manage. Yellowbrick is a data warehouse platform that was built from the ground up for speed, and can work across clouds and all the way to the edge. In this episode CTO Mark Cusack explains how the engine is architected, the benefits that speed and predictable pricing has for the organization, and how you can simplify your platform by putting the warehouse close to the data, instead of the other way around.
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 Mark Cusack about Yellowbrick, a data warehouse designed for distributed clouds
Interview
- Introduction
- How did you get involved in the area of data management?
- Can you start by describing what Yellowbrick is and some of the story behind it?
- What does the term "distributed cloud" signify and what challenges are associated with it?
- How would you characterize Yellowbrick’s position in the database/DWH market?
- How is Yellowbrick architected?
- How have the goals and design of the platform changed or evolved over time?
- How does Yellowbrick maintain visibility across the different data locations that it is responsible for?
- What capabilities does it offer for being able to join across the disparate "clouds"?
- What are some data modeling strategies that users should consider when designing their deployment of Yellowbrick?
- What are some of the capabilities of Yellowbrick that you find most useful or technically interesting?
- For someone who is adopting Yellowbrick, what is the process for getting it integrated into their data systems?
- What are the most underutilized, overlooked, or misunderstood features of Yellowbrick?
- What are the most interesting, innovative, or unexpected ways that you have seen Yellowbrick used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on and with Yellowbrick?
- When is Yellowbrick the wrong choice?
- What do you have planned for the future of the product?
Contact Info
- @markcusack on Twitter
Parting Question
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
Links
- Yellowbrick
- Teradata
- Rainstor
- Distributed Cloud
- Hybrid Cloud
- SwimOS
- Kafka
- Pulsar
- Snowflake
- AWS Redshift
- MPP == Massively Parallel Processing
- Presto
- Trino
- L3 Cache
- NVMe
- Reactive Programming
- Coroutine
- Star Schema
- Denodo
- Lexis Nexis
- Vertica
- Netezza
- Grenplum
- PostgreSQL
- Clickhouse
- Erasure Coding
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA