AWS Lambda for Beginners: Overcoming the Most Common Challenges
Learn how to overcome three common challenges while building data pipelines with AWS Lambda. Add external dependencies to your Lambda function with Layers, overcome the 250MB package limit with Docker, and set up continuous deployment with GitHub Actions.
Speeding up data analysis with TimescaleDB and PostgreSQL
Is your data analysis process as fast and efficient as it could be? This four-part blog series will outline common data analysis problems and how TimescaleDB and PostgreSQL fixed them by making data munging tasks within analysis fast, efficient, and easily accessible.
How to Create (Lots!) of Sample Time-Series Data With PostgreSQL generate_series()
Generating sample time-series data with the PostgreSQL generate_series() function is a useful skill to have when evaluating new database features, creating demonstrations, or testing insert and query patterns. Learn what PostgreSQL `generate_series()` is and how to use it for basic data generation.
Hacking NFL data with PostgreSQL, TimescaleDB, and SQL
Learn how to use time-series data provided by the NFL to uncover valuable insights into many player performance metrics – and ways to apply the same methods to improve your fantasy league team, or your knowledge of the game - all with PostgreSQL, SQL, and freely available extensions.
How we’re building a remote-first team culture (aka virtual event ideas that you’re welcome to steal)
As an engineering-centric organization that’s been 60%+ remote for most of our history, we’re creative with the ways we stay connected – and, with COVID-19 making us and all teams 100% remote, we’re sharing how we’ve created camaraderie to help you do the same.
Our Product Team's Reading List: What Moral Psychology, the Rust Belt, and Framing Can Teach Us About UX
Like many people, we've been catching up on our book backlog –and we're sharing our recent reads, plus what they've taught us about our work as product managers and engineers.
Public Dataset Tips & Tricks: How to weave together public datasets to make sense of the world
Public datasets can help us gain insight into our business and our world. Combining public datasets either together or with our own data often requires a series of steps to cleanup (or “normalize”) the data. This blog post walks you through some techniques for data normalization.