To process your data you need to know what shape it has, which is why schemas are important. When you are processing that data in multiple systems it can be difficult to ensure that they all have an accurate representation of that schema, which is why Confluent has built a schema registry that plugs into Kafka. In this episode Ewen Cheslack-Postava explains what the schema registry is, how it can be used, and how they built it. He also discusses how it can be extended for other deployment targets and use cases, and additional features that are planned for future releases.
We have tools and platforms for collaborating on software projects and linking them together, wouldn’t it be nice to have the same capabilities for data? The team at data.world are working on building a platform to host and share data sets for public and private use that can be linked together to build a semantic web of information. The CTO, Bryon Jacob, discusses how the company got started, their mission, and how they have built and evolved their technical infrastructure.
With the wealth of formats for sending and storing data it can be difficult to determine which one to use. In this episode Doug Cutting, creator of Avro, and Julien Le Dem, creator of Parquet, dig into the different classes of serialization formats, what their strengths are, and how to choose one for your workload. They also discuss the role of Arrow as a mechanism for in-memory data sharing and how hardware evolution will influence the state of the art for data formats.
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.
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.