4 Things that might go wrong in your data-intensive application

Fri September 10, 02:15 PM–02:45 PM • Back to program
Start time 14:15
End time 14:45
Countdown link Open timer

This talk is going to brief things that can go wrong while running a large-scaled data-intensive application for a decade. I will like to share my mistakes and aftermath in production. Also, I'll talk about how I remedied what I've done and what I learned afterward. This talk will help audiences avoid making the same mistakes so they don’t suffer as I did. Audiences may be inspired by this talk and help them to build a successful service that stands the test of time.

This talk is going to brief things that can go wrong while running a large-scaled data-intensive application for a decade.

We always want to do things right at the very first. Have a magical architecture design, 100% test coverage, things like that. However, after running a service for ten years, I realize that those things are just the tip of the iceberg. So many Unimaginable things can happen in production.

In this talk, I will like to share my mistakes and aftermath in production. Also, I'll talk about how I remedied what I've done and what I learned afterward. Due to time limitations, I'll share the most "oops" ones. This talk will help audiences avoid making the same mistakes so they don’t suffer as I did. Audiences may be inspired by this talk and help them to build a successful service that stands the test of time.

Targeted audiences: who like stories about running a large-scale service in the real world.

Audiences are expected to have basic knowledge and experience about backend and DevOps.

Petertc Chu

Open-source enthusiast, Pythoneer.

Research engineer worked on backend/SRE/DevOps, experienced in implementing and maintaining distributed/software-defined storage at scale.

http://hrchu.github.io