Summary

The responsibilities of a data scientist and a data engineer often overlap and occasionally come to cross purposes. Despite these challenges it is possible for the two roles to work together effectively and produce valuable business outcomes. In this episode Will McGinnis discusses the opinions that he has gained from experience on how data teams can play to their strengths to the benefit of all.

Preamble

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Your host is Tobias Macey and today I’m interviewing Will McGinnis about the relationship and boundaries between data engineers and data scientists

Interview

Introduction How did you get involved in the area of data management? The terms “Data Scientist” and “Data Engineer” are fluid and seem to have a different meaning for everyone who uses them. Can you share how you define those terms? What parallels do you see between the relationships of data engineers and data scientists and those of developers and systems administrators? Is there a particular size of organization or problem that serves as a tipping point for when you start to separate the two roles into the responsibilities of more than one person or team? What are the benefits of splitting the responsibilities of data engineering and data science?

What are the disadvantages?

What are some strategies to ensure successful interaction between data engineers and data scientists? How do you view these roles evolving as they become more prevalent across companies and industries?

Contact Info

Website wdm0006 on GitHub @willmcginniser on Twitter LinkedIn

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Links

Blog Post: Tendencies of Data Engineers and Data Scientists Predikto Categorical Encoders DevOps SciKit-Learn

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast

Summary

Do you wish that you could track the changes in your data the same way that you track the changes in your code? Pachyderm is a platform for building a data lake with a versioned file system. It also lets you use whatever languages you want to run your analysis with its container based task graph. This week Daniel Whitenack shares the story of how the project got started, how it works under the covers, and how you can get started using it today!

Preamble

Hello and welcome to the Data Engineering Podcast, the show about modern data infrastructure Go to dataengineeringpodcast.com to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch. You can help support the show by checking out the Patreon page which is linked from the site. To help other people find the show you can leave a review on iTunes, or Google Play Music, and tell your friends and co-workers Your host is Tobias Macey and today I’m interviewing Daniel Whitenack about Pachyderm, a modern container based system for building and analyzing a versioned data lake.

Interview with Daniel Whitenack

Introduction How did you get started in the data engineering space? What is pachyderm and what problem were you trying to solve when the project was started? Where does the name come from? What are some of the competing projects in the space and what features does Pachyderm offer that would convince someone to choose it over the other options? Because of the fact that the analysis code and the data that it acts on are all versioned together it allows for tracking the provenance of the end result. Why is this such an important capability in the context of data engineering and analytics? What does Pachyderm use for the distribution and scaling mechanism of the file system? Given that you can version your data and track all of the modifications made to it in a manner that allows for traversal of those changesets, how much additional storage is necessary over and above the original capacity needed for the raw data? For a typical use of Pachyderm would someone keep all of the revisions in perpetuity or are the changesets primarily just useful in the context of an analysis workflow? Given that the state of the data is calculated by applying the diffs in sequence what impact does that have on processing speed and what are some of the ways of mitigating that? Another compelling feature of Pachyderm is the fact that it natively supports the use of any language for interacting with your data. Why is this such an important capability and why is it more difficult with alternative solutions?

How did you implement this feature so that it would be maintainable and easy to implement for end users?

Given that the intent of using containers is for encapsulating the analysis code from experimentation through to production, it seems that there is the potential for the implementations to run into problems as they scale. What are some things that users should be aware of to help mitigate this? The data pipeline and dependency graph tooling is a useful addition to the combination of file system and processing interface. Does that preclude any requirement for external tools such as Luigi or Airflow? I see that the docs mention using the map reduce pattern for analyzing the data in Pachyderm. Does it support other approaches such as streaming or tools like Apache Drill? What are some of the most interesting deployments and uses of Pachyderm that you have seen? What are some of the areas that you are looking for help from the community and are there any particular issues that the listeners can check out to get started with the project?

Keep in touch

Daniel

Twitter – @dwhitena

Pachyderm

Website

Free Weekend Project

GopherNotes

Links

AirBnB RethinkDB Flocker Infinite Project Git LFS Luigi Airflow Kafka Kubernetes Rkt SciKit Learn Docker Minikube General Fusion

The intro and outro music is from The Hug by The Freak Fandango Or