Summary
Buzzfeed needs to be able to understand how its users are interacting with the myriad articles, videos, etc. that they are posting. This lets them produce new content that will continue to be well-received. To surface the insights that they need to grow their business they need a robust data infrastructure to reliably capture all of those interactions. Walter Menendez is a data engineer on their infrastructure team and in this episode he describes how they manage data ingestion from a wide array of sources and create an interface for their data scientists to produce valuable conclusions.
Preamble
Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at dataengineeringpodcast.com/linode 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. Continuous delivery lets you get new features in front of your users as fast as possible without introducing bugs or breaking production and GoCD is the open source platform made by the people at Thoughtworks who wrote the book about it. Go to dataengineeringpodcast.com/gocd to download and launch it today. Enterprise add-ons and professional support are available for added peace of mind. 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 Walter Menendez about the data engineering platform at Buzzfeed
Interview
Introduction How did you get involved in the area of data management? How is the data engineering team at Buzzfeed structured and what kinds of projects are you responsible for? What are some of the types of data inputs and outputs that you work with at Buzzfeed? Is the core of your system using a real-time streaming approach or is it primarily batch-oriented and what are the business needs that drive that decision? What does the architecture of your data platform look like and what are some of the most significant areas of technical debt? Which platforms and languages are most widely leveraged in your team and what are some of the outliers? What are some of the most significant challenges that you face, both technically and organizationally? What are some of the dead ends that you have run into or failed projects that you have tried? What has been the most successful project that you have completed and how do you measure that success?
Contact Info
@hackwalter on Twitter walterm on GitHub
Links
Data Literacy MIT Media Lab Tumblr Data Capital Data Infrastructure Google Analytics Datadog Python Numpy SciPy NLTK Go Language NSQ Tornado PySpark AWS EMR Redshift Tracking Pixel Google Cloud Don’t try to be google Stop Hiring DevOps Engineers and Start Growing Them
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast