Data Cloud Cost Optimization With Bluesky Data
Data Engineering Podcast
00:00
Identifying Opportunities for Cost Optimization and Performance Tuning
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Summary
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.
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 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
Interview
- Introduction
- 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?
Contact Info
- Mingsheng
- @mingshenghong on Twitter
- Zheng
Parting Question
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
Closing Announcements
- 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 hosts@dataengineeringpodcast.com) with your story.
- To help other people find the show please leave a review on iTunes and tell your friends and co-workers
Links
- Bluesky Data
- RocksDB
- Snowflake
- Trino
- Firebolt
- Bigquery
- Hive
- Vertica
- Michael Stonebraker
- Teradata
- C-Store Paper
- Ottertune
- dbt
- infracost
- Subtract: The Untapped Science of Less by Leidy Klotz
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
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