Summary
One of the biggest pain points when working with data is getting is dealing with the boilerplate code to load it into a usable format. Intake encapsulates all of that and puts it behind a single API. In this episode Martin Durant explains how to use the Intake data catalogs for encapsulating source information, how it simplifies data science workflows, and how to incorporate it into your projects. It is a lightweight way to enable collaboration between data engineers and data scientists in the PyData ecosystem.
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- Your host as usual is Tobias Macey and today I’m interviewing Martin Durant about Intake, a lightweight package for finding, investigating, loading and disseminating data
Interview
- Introductions
- How did you get introduced to Python?
- Can you start by explaining what Intake is and the story behind its creation?
- Can you outline some of the other projects and products that intersect with the functionality of Intake and describe where it fits in terms of use case and capabilities? (e.g. Quilt Data, Arrow, Data Retriever)
- Can you describe the workflows for using Intake, both from the data scientist and the data engineer perspective?
- One of the persistent challenges in working with data is that of cataloging and discovery of what already exists. In what ways does Intake address that problem?
- Does it have any facilities for capturing and exposing data lineage?
- For someone who needs to customize their usage of Intake, what are the extension points and what is involved in building a plugin?
- Can you describe how Intake is implemented and how it has evolved since it first started?
- What are some of the most challenging, complex, or novel aspects of the Intake implementation?
- Intake focuses primarily on integrating with the PyData ecosystem (e.g. NumPy, Pandas, SciPy, etc.). What are some other communities that are, or could be, benefiting from the work being done on Intake?
- What are some of the assumptions that are baked into Intake that would need to be modified to make it more broadly applicable?
- What are some of the assumptions that were made going into this project that have needed to be reconsidered after digging deeper into the problem space?
- What are some of the most interesting/unexpected/innovative ways that you have seen Intake leveraged?
- What are your plans for the future of Intake?
Keep In Touch
- martindurant on GitHub
- Website
- @martin_durant_ on Twitter
Picks
- Tobias
- Ubersuggest SEO tool
Links
- Intake
- Anaconda
- Dask
- Fast Parquet
- IDL
- Space Telescope Institute
- Blaze
- Quilt Data
- Arrow
- Data Retriever
- Parquet
- DataFrame
- Apache Spark
- Dremio
- Dat Project – distributed peer-to-peer data sharing
- GeoPandas
- XArray
- Solr
- Streamz
- PyViz
- S3FS
The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
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