Docker for Data Science for Beginner’s
In the fast-moving conditional field of Data Science, it is an art to ensure that your code works effectively anywhere …
I Don’t Care How Big Your Data Is.
Innuendo aside, “big” data is getting smaller and faster. Data leaders need to adjust to the new paradigm. At some …
Implementing Data Contracts: 7 Key Learnings
Forward: A Data Engineering Movement Afoot? We ran into Andrew, a team lead and senior engineer at GoCardless, at our London …
Is “Self-Service” Data’s Biggest Lie?
Self-serve systems are a big priority for data leaders and a principle of nearly every modern data strategy, but what …
How to Set KPIs for Your Data Team
KPIs & Data Team As analytics professionals, we deal in data: serving ad-hoc reports on a minute’s notice, pulling queries …
Can You Out-Architect Bad Data?
Even with the most well-designed data platforms, systems will break. Without embracing risk, you’re playing with fire. Say it with …
The Fight for Controlled Freedom of the Data Warehouse
The data gatekeeper is dead, long live the…oh no what have we done? A silent alarm rings in my head …
What’s Next for Data Engineering in 2023? 7 Predictions
What’s in store for the future of data engineering? In this article, I share some of my topline predictions for …
6 Reasons Why Companies Fail at Data Governance
Everywhere we go in the cloud data space today, we’re hearing one message loud and clear: “you should be thinking …
6 Steps to Making Data Reliability a Habit
Some organizations approach data quality like a crash diet. Instead, try these 6 steps for sustainable data quality at scale. …