protus

Not sure if this company also ran into this issue, but the engineers at my startup absolutely hated the Firebase UI for querying. Genuinely could not fathom how product designers could put together such a poor experience. Needless to say we did our own migration after 1 year to MySQL after

I work at a startup, and recently built the fraud detection algorithm we use to classify and process payments. I have a BS in CS with a minor in Data Science, and also have applied ML experience. My take is that your skills and knowledge are paramount over your degree, especially if you're building ML systems that are adding value to your company.

Will take a look, thank you!

I agree that Kubernetes is overkill, even at our scale. My biggest concern with "serverless" paradigms like Cloud Run or Fargate is with cold starts.

I believe the main pitfall of this approach is related to what the author of the og article mentioned around r/w and query latency from S3. I like the old Twitter eng blog post on their distributed kv store called Manhattan (https://blog.x.com/engineering/en_us/a/2014/manhattan-our-re...). For any serious real-time data, we probably still need some semblance of fast R/W storage, but analytics workloads or long-running tasks can def benefit for cheap blob storage.

I looked at it briefly - it looks interesting, but I'm a bit worried about the same things that are affecting us with our current infra provider. Namely, PaaS overhead cost, lack of customized options for instance configurations, and long term reliability (SLAs, uptimes, managing outages, and etc).

Firestore is great until Google sunsets it