Summary

A lot of the work that goes into data engineering is trying to make sense of the "data exhaust" from other applications and services. There is an undeniable amount of value and utility in that information, but it also introduces significant cost and time requirements. In this episode Nick King discusses how you can be intentional about data creation in your applications and services to reduce the friction and errors involved in building data products and ML applications. He also describes the considerations involved in bringing behavioral data into your systems, and the ways that he and the rest of the Snowplow team are working to make that an easy addition to your platforms.

Announcements

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 Nick King about the utility of behavioral data for your data products and the technical and strategic considerations to collect and integrate it

Interview

Introduction
How did you get involved in the area of data management?
Can you share your definition of "behavioral data" and how it is differentiated from other sources/types of data?
- What are some of the unique characteristics of that information?
- What technical systems are required to generate and collect those interactions?
What are the organizational patterns that are required to support effective workflows for building data generation capabilities?
- What are some of the strategies that have been most effective for bringing together data and application teams to identify and implement what behaviors to track?
What are some of the ethical and privacy considerations that need to be addressed when working with end-user behavioral data?
The data sources associated with business operations services and custom applications already represent some measure of user interaction and behaviors. How can teams use the information available from those systems to inform and augment the types of events/information that should be captured/generated in a system like Snowplow?
Can you describe the workflow for a team using Snowplow to generate data for a given analytical/ML project?
- What are some of the tactical aspects of deciding what interfaces to use for generating interaction events?
- What are some of the event modeling strategies to keep in mind to simplify the analysis and integration of the generated data?
What are some of the notable changes in implementation and focus for Snowplow over the past ~4 years?
- How has the emergence of the "modern data stack" influenced the product direction?
What are the most interesting, innovative, or unexpected ways that you have seen Snowplow used for data generation/behavioral data collection?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on Snowplow?
When is Snowplow the wrong choice?
What do you have planned for the future of Snowplow?

Contact Info

LinkedIn
@nking on Twitter

Parting Question

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

Closing Announcements

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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Links

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

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Build Better Data Products By Creating Data, Not Consuming It 01:05:20 Share

Data Engineering Podcast

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Build Better Data Products By Creating Data, Not Consuming It