Data lakes have been gaining popularity alongside an increase in their sophistication and usability. Despite improvements in performance and data architecture they still require significant knowledge and experience to deploy and manage. In this episode Vikrant Dubey discusses his work on the Cuelake project which allows data analysts to build a lakehouse with SQL queries. By building on top of Zeppelin, Spark, and Iceberg he and his team at Cuebook have built an autoscaled cloud native system that abstracts the underlying complexity.
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- 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 Vikrant Dubey about Cuebook and their Cuelake project for building ELT pipelines for your data lakehouse entirely in SQL
- How did you get involved in the area of data management?
- Can you describe what Cuelake is and the story behind it?
- There are a number of platforms and projects for running SQL workloads and transformations on a data lake. What was lacking in those systems that you are addressing with Cuelake?
- Who are the target users of Cuelake and how has that influenced the features and design of the system?
- Can you describe how Cuelake is implemented?
- What was your selection process for the various components?
- What are some of the sharp edges that you have had to work around when integrating these components?
- What involved in getting Cuelake deployed?
- How are you using Cuelake in your work at Cuebook?
- Given your focus on machine learning for anomaly detection of business metrics, what are the challenges that you faced in using a data warehouse for those workloads?
- What are the advantages that a data lake/lakehouse architecture maintains over a warehouse?
- What are the shortcomings of the lake/lakehouse approach that are solved by using a warehouse?
- What are the most interesting, innovative, or unexpected ways that you have seen Cuelake used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on Cuelake?
- When is Cuelake the wrong choice?
- What do you have planned for the future of Cuelake?
- 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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