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.
Have you ever woken up to a crisis because a number on a dashboard is broken and no one knows why? Or sent out frustrating slack messages trying to find the right data set? Or tried to understand what a column name means?
Our friends at Atlan started out as a data team themselves and faced all this collaboration chaos themselves, and started building Atlan as an internal tool for themselves. Atlan is a collaborative workspace for data-driven teams, like Github for engineering or Figma for design teams. By acting as a virtual hub for data assets ranging from tables and dashboards to SQL snippets & code, Atlan enables teams to create a single source of truth for all their data assets, and collaborate across the modern data stack through deep integrations with tools like Snowflake, Slack, Looker and more.
Go to dataengineeringpodcast.com/atlan and sign up for a free trial. If you’re a data engineering podcast listener, you get credits worth $3000 on an annual subscription.
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!
Firebolt is the world’s fastest cloud data warehouse, purpose-built for high performance analytics. It provides orders of magnitude faster query performance at a fraction of the cost compared to alternatives. Companies that adopted Firebolt have been able to deploy data warehouses in weeks and deliver sub-second performance at terabyte to petabyte scale for a wide range of interactive, high performance analytics across internal BI as well as customer facing analytics use cases. Visit dataengineeringpodcast.com/firebolt to get started.
- Hello and welcome to the Data Engineering Podcast, the show about modern data management
- 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 $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!
- Firebolt is the fastest cloud data warehouse. Visit dataengineeringpodcast.com/firebolt to get started. The first 25 visitors will receive a Firebolt t-shirt.
- Atlan is a collaborative workspace for data-driven teams, like Github for engineering or Figma for design teams. By acting as a virtual hub for data assets ranging from tables and dashboards to SQL snippets & code, Atlan enables teams to create a single source of truth for all their data assets, and collaborate across the modern data stack through deep integrations with tools like Snowflake, Slack, Looker and more. Go to dataengineeringpodcast.com/atlan today and sign up for a free trial. If you’re a data engineering podcast listener, you get credits worth $3000 on an annual subscription
- Your host is Tobias Macey and today I’m interviewing Mark Cusack about Yellowbrick, a data warehouse designed for distributed clouds
- 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?
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
- Distributed Cloud
- Hybrid Cloud
- AWS Redshift
- MPP == Massively Parallel Processing
- L3 Cache
- Reactive Programming
- Star Schema
- Lexis Nexis
- Erasure Coding