December 17, 2018

Advice On Scaling Your Data Pipeline Alongside Your Business with Christian Heinzmann - Episode 61

39 minutes

Summary

Every business needs a pipeline for their critical data, even if it is just pasting into a spreadsheet. As the organization grows and gains more customers, the requirements for that pipeline will change. In this episode Christian Heinzmann, Head of Data Warehousing at Grubhub, discusses the various requirements for data pipelines and how the overall system architecture evolves as more data is being processed. He also covers the changes in how the output of the pipelines are used, how that impacts the expectations for accuracy and availability, and some useful advice on build vs. buy for the components of a data platform.

Preamble

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 Christian Heinzmann about how data pipelines evolve as your business grows

Interview

Introduction

How did you get involved in the area of data management?

Can you start by sharing your definition of a data pipeline?

At what point in the life of a project or organization should you start thinking about building a pipeline?

In the early stages when the scale of the data and business are still small, what are some of the design characteristics that you should be targeting for your pipeline?

What metrics/use cases should you be optimizing for at this point?

What are some of the indicators that you look for to signal that you are reaching the next order of magnitude in terms of scale?

How do the design requirements for a data pipeline change as you reach this stage?

What are some of the challenges and complexities that begin to present themselves as you build and run your pipeline at medium scale?

What are some of the changes that are necessary as you move to a large scale data pipeline?

At each level of scale it is important to minimize the impact of the ETL process on the source systems. What are some strategies that you have employed to avoid degrading the performance of the application systems?

In recent years there has been a shift to using data lakes as a staging ground before performing transformations. What are your thoughts on that approach?

When performing transformations there is a potential for discarding information or losing fidelity. How have you worked to reduce the impact of this effect?

Transformations of the source data can be brittle when the format or volume changes. How do you design the pipeline to be resilient to these types of changes?

What are your selection criteria when determining what workflow or ETL engines to use in your pipeline?

How has your preference of build vs buy changed at different scales of operation and as new/different projects become available?

What are some of the dead ends or edge cases that you have had to deal with in your current role at Grubhub?

What are some of the common mistakes or overlooked aspects of building a data pipeline that you have seen?

What are your plans for improving your current pipeline at Grubhub?

What are some references that you recommend for anyone who is designing a new data platform?

Contact Info

@sirchristian on Twitter

Blog

sirchristian on GitHub

Parting Question

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

Links

Scaling ETL blog post

GrubHub

Data Warehouse

Redshift

Spark

Spark In Action Podcast Episode

Hive

Amazon EMR

Looker

Podcast Episode

Redash

Metabase

Podcast Episode

A Primer on Enterprise Data Curation

Pub/Sub (Publish-Subscribe Pattern)

Change Data Capture

Jenkins

Python

Azkaban

Luigi

Zendesk

Data Lineage

AirBnB Engineering Blog

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

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By Tobias Macey

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