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Data Retriever with Henry Senyondo episode artwork

Aug 12, 2017 17 min

Data Retriever with Henry Senyondo

from The Python Podcast.__init__ Hosted by Tobias Macey

Summary Analyzing and interpreting data is a large portion of the work involved in scientific research. Getting to that point can be a lot of work on its own because of all of the steps required to download, clean, and organize the data prior to analysis. This week Henry Senyondo talks about the work he is doing with Data Retriever to make data preparation as easy as retriever install. Preface Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great. I would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable. When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at www.podastinit.com/linode?utm_source=rss&utm_medium=rss and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app. Visit the site to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch. To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media. Your host as usual is Tobias Macey and today I’m interviewing Henry Senyondo about Data Retriever, the package manager for public data sets. Interview Introductions How did you get introduced to Python? Can you explain what data retriever is and the problem that it was built to solve? Are there limitations as to the types of data that can be managed by data retriever? What kinds of data sets are currently available and who are the target users? What is involved in preparing a new dataset to be available for installation? How much of the logic for installing the data is shared between the R and Python implementations of Data Retriever and how do you ensure that the two packages evolve in parallel? How is the project designed and what are some of the most difficult technical aspects of building it? What is in store for the future of data retriever? Keep In Touch Github @henrykironde on Twitter Picks Tobias Otium Bluetooth Receiver Panasonic Ergofit Headphones Nitize Adhesive Pocket Clip Henry The Three Idiots Links Weecology Lab University of Florida Data Retriever LG R Julia Open Knowledge Foundation Frictionless Data Format Data Weaver The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA

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Data Retriever with Henry Senyondo

0:00 17:55

Chapters

  1. 0:13 Introduction and Guest Introduction
  2. 1:12 Henry's Journey with Python
  3. 1:45 Overview of DataRetriever
  4. 3:03 Data Management and Package Management
  5. 5:14 Types of Datasets and Scalability
  6. 7:00 Target Systems and Data Size
  7. 8:04 Standardization and Data Preparation
  8. 10:08 Project Design and Architecture
  9. 12:29 Challenges in Development and Maintenance
  10. 14:04 Community and Future Features
  11. 15:31 Choice of Python and Final Thoughts
  12. 16:09 Picks and Recommendations

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This episode was published on August 12, 2017.

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