Evolving An ETL Pipeline For Better Productivity

Episode • Jun 4, 2019 • 1h 2m

Summary

Building an ETL pipeline can be a significant undertaking, and sometimes it needs to be rebuilt when a better option becomes available. In this episode Aaron Gibralter, director of engineering at Greenhouse, joins Raghu Murthy, founder and CEO of DataCoral, to discuss the journey that he and his team took from an in-house ETL pipeline built out of open source components onto a paid service. He explains how their original implementation was built, why they decided to migrate to a paid service, and how they made that transition. He also discusses how the abstractions provided by DataCoral allows his data scientists to remain productive without requiring dedicated data engineers. If you are either considering how to build a data pipeline or debating whether to migrate your existing ETL to a service this is definitely worth listening to for some perspective.

Announcements

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Your host is Tobias Macey and today I’m interviewing Aaron Gibralter and Raghu Murthy about the experience of Greenhouse migrating their data pipeline to DataCoral

Interview

Introduction
How did you get involved in the area of data management?
Aaron, can you start by describing what Greenhouse is and some of the ways that you use data?
Can you describe your overall data infrastructure and the state of your data pipeline before migrating to DataCoral?
- What are your primary sources of data and what are the targets that you are loading them into?
What were your biggest pain points and what motivated you to re-evaluate your approach to ETL?
- What were your criteria for your replacement technology and how did you gather and evaluate your options?
Once you made the decision to use DataCoral can you talk through the transition and cut-over process?
- What were some of the unexpected edge cases or shortcomings that you experienced when moving to DataCoral?
- What were the big wins?
What was your evaluation framework for determining whether your re-engineering was successful?
Now that you are using DataCoral how would you characterize the experiences of yourself and your team?
- If you have freed up time for your engineers, how are you allocating that spare capacity?
What do you hope to see from DataCoral in the future?
What advice do you have for anyone else who is either evaluating a re-architecture of their existing data platform or planning out a greenfield project?

Contact Info

Aaron
- agribralter on GitHub
- LinkedIn
Raghu
- LinkedIn
- Medium

Parting Question

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

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