#477 Lessons I've Learned While Scaling Up a Data Warehouse

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Lessons I've Learned While Scaling Up a Data Warehouse

In this post, we will cover the main scale obstacles you might face when using a data warehouse. We’ll also cover what you can do to overcome these challenges in terms of technological tools and whether it pays to build these tools in-house or to use a managed service. Addressing these challenges could be very important for a young startup, whose data is just starting to pile up and questions from different stakeholders are popping up, or for an existing data warehouse that has reached its infrastructure limit.

The Configuration Complexity Clock

When I was a young coder, just starting out in the big scary world of enterprise software, an older, far more experienced chap gave me a stern warning about hard coding values in my software. “They will have to change at some point, and you don’t want to recompile and redeploy your application just to change the VAT tax rate.” I took this advice to heart and soon every value that my application needed was loaded from a huge .ini file. I still think it’s good advice, but be warned, like most things in software, it’s good advice up to a point. Beyond that point lies pain.

Uber’s Unified Signup and Login Stack

Every year we have millions of users going through signup and login on our various apps. Over the years we’ve built independent signup and login experiences for each of our lines of business which allowed us to innovate and move a lot quicker. However, as we scaled and added additional lines of business, our experiences began to diverge leading to some of these inconsistencies being amplified.

Mobile-First CSS: Is It Time for a Rethink?

The mobile-first design methodology is great—it focuses on what really matters to the user, it’s well-practiced, and it’s been a common design pattern for years. So developing your CSS mobile-first should also be great, too…right?

What Is Negative Engineering?

It was the second game of a double-header, and the Washington Nationals had a problem. Not on the field, of course: The soon-to-be World Series champions were performing beautifully. But as they waited out a rain delay, something went awry behind the scenes. A task scheduler deep within the team’s analytics infrastructure stopped running.

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