With the constant evolution of technology for data management it can seem impossible to make an informed decision about whether to build a data warehouse, or a data lake, or just leave your data wherever it currently rests. What’s worse is that any time you have to migrate to a new architecture, all of your analytical code has to change too. Thankfully it’s possible to add an abstraction layer to eliminate the churn in your client code, allowing you to evolve your data platform without disrupting your downstream data users. In this episode AtScale co-founder and CTO Matthew Baird describes how the data virtualization and data engineering automation capabilities that are built into the platform free up your engineers to focus on your business needs without having to waste cycles on premature optimization. This was a great conversation about the power of abstractions and appreciating the value of increasing the efficiency of your data team.
- 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 Matt Baird about AtScale, a platform that
- How did you get involved in the area of data management?
- Can you start by describing the AtScale platform and how it fits in the ecosystem of data tools?
- What was your motivation for building the platform and what were some of the early challenges that you faced in achieving your current level of success?
- How is the AtScale platform architected and what have been some of the main areas of evolution and change since you first began building it?
- How has the surrounding data ecosystem changed since AtScale was founded?
- How are current industry trends influencing your product focus?
- Can you talk through the workflow for someone implementing AtScale?
- What are some of the main use cases that benefit from data virtualization capabilities?
- How does it influence the relevancy of data warehouses or data lakes?
- What are some of the types of tools or patterns that AtScale replaces in a data platform?
- What are some of the most interesting or unexpected ways that you have seen AtScale used?
- What have been some of the most challenging aspects of building and growing the platform?
- When is AtScale the wrong choice?
- What do you have planned for the future of the platform and business?
- 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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