The latest generation of data warehouse platforms have brought unprecedented operational simplicity and effectively infinite scale. Along with those benefits, they have also introduced a new consumption model that can lead to incredibly expensive bills at the end of the month. In order to ensure that you can explore and analyze your data without spending money on inefficient queries Mingsheng Hong and Zheng Shao created Bluesky Data. In this episode they explain how their platform optimizes your Snowflake warehouses to reduce cost, as well as identifying improvements that you can make in your queries to reduce their contribution to your bill.
PostHog is an open source, product analytics platform. PostHog enables software teams to understand user behavior – auto-capturing events, performing product analytics and dashboarding, enabling video replays, and rolling out new features behind feature flags, all based on their single open source platform. The product’s open source approach enables companies to self-host, removing the need to send data externally. Try it out today at dataengineeringpodcast.com/posthog
The modern data stack needs a reimagined metadata management platform. Acryl Data’s vision is to bring clarity to your data through its next generation multi-cloud metadata management platform. Founded by the leaders that created projects like LinkedIn DataHub and Airbnb Dataportal, Acryl Data enables delightful search and discovery, data observability, and federated governance across data ecosystems. Signup for the SaaS product today at dataengineeringpodcast.com/acryl
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
- 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!
- This episode is brought to you by Acryl Data, the company behind DataHub, the leading developer-friendly data catalog for the modern data stack. Open Source DataHub is running in production at several companies like Peloton, Optum, Udemy, Zynga and others. Acryl Data provides DataHub as an easy to consume SaaS product which has been adopted by several companies. Signup for the SaaS product at dataengineeringpodcast.com/acryl
- RudderStack helps you build a customer data platform on your warehouse or data lake. Instead of trapping data in a black box, they enable you to easily collect customer data from the entire stack and build an identity graph on your warehouse, giving you full visibility and control. Their SDKs make event streaming from any app or website easy, and their state-of-the-art reverse ETL pipelines enable you to send enriched data to any cloud tool. Sign up free… or just get the free t-shirt for being a listener of the Data Engineering Podcast at dataengineeringpodcast.com/rudder.
- The most important piece of any data project is the data itself, which is why it is critical that your data source is high quality. PostHog is your all-in-one product analytics suite including product analysis, user funnels, feature flags, experimentation, and it’s open source so you can host it yourself or let them do it for you! You have full control over your data and their plugin system lets you integrate with all of your other data tools, including data warehouses and SaaS platforms. Give it a try today with their generous free tier at dataengineeringpodcast.com/posthog
- Your host is Tobias Macey and today I’m interviewing Mingsheng Hong and Zheng Shao about Bluesky Data where they are combining domain expertise and machine learning to optimize your cloud warehouse usage and reduce operational costs
- How did you get involved in the area of data management?
- Can you describe what Bluesky is and the story behind it?
- What are the platforms/technologies that you are focused on in your current early stage?
- What are some of the other targets that you are considering once you validate your initial hypothesis?
- Cloud cost optimization is an active area for application infrastructures as well. What are the corollaries and differences between compute and storage optimization strategies and what you are doing at Bluesky?
- How have your experiences at hyperscale companies using various combinations of cloud and on-premise data platforms informed your approach to the cost management problem faced by adopters of cloud data systems?
- What are the most significant drivers of cost in cloud data systems?
- What are the factors (e.g. pricing models, organizational usage, inefficiencies) that lead to such inflated costs?
- What are the signals that you collect for identifying targets for optimization and tuning?
- Can you describe how the Bluesky mission control platform is architected?
- What are the current areas of uncertainty or active research that you are focused on?
- What is the workflow for a team or organization that is adding Bluesky to their system?
- How does the usage of Bluesky change as teams move from the initial optimization and dramatic cost reduction into a steady state?
- What are the most interesting, innovative, or unexpected ways that you have seen teams approaching cost management in the absence of Bluesky?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on Bluesky?
- When is Bluesky the wrong choice?
- What do you have planned for the future of Bluesky?
- 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.
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- Bluesky Data
- Michael Stonebraker
- C-Store Paper
- Subtract: The Untapped Science of Less by Leidy Klotz