This show goes behind the scenes for the tools, techniques, and difficulties associated with the discipline of data engineering. Databases, workflows, automation, and data manipulation are just some of the topics that you will find here.
Supporting And Expanding The Arrow Ecosystem For Fast And Efficient Data Processing At Voltron Data
The data ecosystem has been growing rapidly, with new communities joining and bringing their preferred programming languages to the mix. This has led to inefficiencies in how data is stored, accessed, and shared across process and system boundaries. The Arrow project is designed to eliminate wasted effort in translating between languages, and Voltron Data was created to help grow and support its technology and community. In this episode Wes McKinney shares the ways that Arrow and its related projects are improving the efficiency of data systems and driving their next stage of evolution.
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 Wes McKinney about his work at Voltron Data and on the Arrow ecosystem
How did you get involved in the area of data management?
Can you describe what you are building at Voltron Data and the story behind it?
What is the vision for the broader data ecosystem that you are trying to realize through your investment in Arrow and related projects?
How does your work at Voltron Data contribute to the realization of that vision?
What is the impact on engineer productivity and compute efficiency that gets introduced by the impedance mismatches between language and framework representations of data?
The scope and capabilities of the Arrow project have grown substantially since it was first introduced. Can you give an overview of the current features and extensions to the project?
What are some of the ways that ArrowVe and its related projects can be integrated with or replace the different elements of a data platform?
Can you describe how Arrow is implemented?
What are the most complex/challenging aspects of the engineering needed to support interoperable data interchange between language runtimes?
How are you balancing the desire to move quickly and improve the Arrow protocol and implementations, with the need to wait for other players in the ecosystem (e.g. database engines, compute frameworks, etc.) to add support?
With the growing application of data formats such as graphs and vectors, what do you see as the role of Arrow and its ideas in those use cases?
For workflows that rely on integrating structured and unstructured data, what are the options for interaction with non-tabular data? (e.g. images, documents, etc.)
With your support-focused business model, how are you approaching marketing and customer education to make it viable and scalable?
What are the most interesting, innovative, or unexpected ways that you have seen Arrow used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on Arrow and its ecosystem?
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 shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning.
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