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.
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- Your host is Tobias Macey and today I’m interviewing Walter Menendez about the data engineering platform at Buzzfeed
- 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?
- Data Literacy
- MIT Media Lab
- Data Capital
- Data Infrastructure
- Google Analytics
- Go Language
- AWS EMR
- Tracking Pixel
- Google Cloud
- Don’t try to be google
- Stop Hiring DevOps Engineers and Start Growing Them