Rebuilding Yelp's Data Pipeline with Justin Cunningham - Episode 5
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Summary

Yelp needs to be able to consume and process all of the user interactions that happen in their platform in as close to real-time as possible. To achieve that goal they embarked on a journey to refactor their monolithic architecture to be more modular and modern, and then they open sourced it! In this episode Justin Cunningham joins me to discuss the decisions they made and the lessons they learned in the process, including what worked, what didn’t, and what he would do differently if he was starting over today.

Preamble

• Hello and welcome to the Data Engineering Podcast, the show about modern data infrastructure

• When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at www.dataengineeringpodcast.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 data pipelines or trying out the tools you hear about on the show.

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• Your host is Tobias Macey and today I’m interviewing Justin Cunningham about Yelp’s data pipeline

Interview with Justin Cunningham

• Introduction

• How did you get involved in the area of data engineering?

• Can you start by giving an overview of your pipeline and the type of workload that you are optimizing for?

• What are some of the dead ends that you experienced while designing and implementing your pipeline?

• As you were picking the components for your pipeline, how did you prioritize the build vs buy decisions and what are the pieces that you ended up building in-house?

• What are some of the failure modes that you have experienced in the various parts of your pipeline and how have you engineered around them?

• What are you using to automate deployment and maintenance of your various components and how do you monitor them for availability and accuracy?

• While you were re-architecting your monolithic application into a service oriented architecture and defining the flows of data, how were you able to make the switch while verifying that you were not introducing unintended mutations into the data being produced?

• Did you plan to open-source the work that you were doing from the start, or was that decision made after the project was completed? What were some of the challenges associated with making sure that it was properly structured to be amenable to making it public?

• What advice would you give to anyone who is starting a brand new project and how would that advice differ for someone who is trying to retrofit a data management architecture onto an existing project?

Keep in touch

• Yelp Engineering Blog)

• Email)

Links

• Kafka)

• Redshift)

• ETL)

• Business Intelligence)

• Change Data Capture)

• LinkedIn Data Bus)

• Apache Storm)

• Apache Flink)

• Confluent)

• Apache Avro)

• Game Days)

• Chaos Monkey)

• Simian Army)

• PaaSta)

• Apache Mesos)

• Marathon)

• SignalFX)

• Sensu)

• Thrift)

• Protocol Buffers)

• JSON Schema)

• Debezium)

• Kafka Connect)

• Apache Beam)

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

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