3 years ago

Category: Database, Data, github, automation

As data professionals, we can learn a lot from software engineering when it comes to building robust, highly available systems. In a previous article, I discussed why data reliability is must-have for data teams, and here, I share how we can apply this concept in practice through engineering operations.

Now, as data systems reach similar levels of complexity and higher levels of importance in an organization, we can apply these same concepts to our field as data reliability — an organization’s ability to deliver high data availability and health throughout the entire data life cycle.

There have been a number of major application downtime outages for companies as varied as GitHub, IBM, DoorDash, and Slack — and data downtime is a similarly serious threat.

Setting SLOs and SLIs for system reliability is an expected and necessary function of any SRE team, and in my opinion, it’s about time we applied them to data, too.