Lots of people here are shitting on mongodb(maybe rightly so). But I think the biggest problem is that the developers making the decision on what kind of DB to use just do not understand these tools. I always recommend reading Designing Data Intensive Applications as soon as you have an inkling that you will be asked to make such decisions in the near future.

I decided to use MongoDB for a pretty big online multiplayer game and it worked very well, not many issues and the development time was a lot faster than trying to use SQL.

Thank you so much for posting this.

Got an elixir book from them and learned alot.

Dude, really thank you for your hard work on this. Out of curiosity, how did you pull it off?

I took the class and highly recommend it, especially if you want to get up to speed on mongodb over a weekend.

Yesterday, https://news.ycombinator.com/item?id=15428526 hit number 1 on HN. Having read the two books, I strongly believe that they not only complement each other, but also must be required reading for any data engineer.

Really loving the book. Definitely a must read for the data engineers. I would add that redbook.io is another great read.

Basically, we improved a shell scripts based ETLs with lambdas. Now we barely can maintain them, and when a something breaks, it takes an engineer an inordinate amount of energy and time to get them fixed. Since we process about 0.5 to 1.5 TB of time series(IOT) I was thinking about an architecture that combine AWS kinesis and airflow

Am I the only one to cry out bullshit! The guy is a terrorist, period. He deserves to pay for what he did. I survived a genocide and you don't see me go around and kill people.

No one is saying his childhood absolves him of any responsibility. The article is merely looking at his upbringing. It's apples and oranges where you compare genocide to child abuse.

Both. It seems to me in the last 12 months or so, the value of niche online community has skyrocketed. For a company like google, what is the value add of these communities?

I'm not sure how much they paid, though you might be able to find out through crunchbase or something similar.

I think the value it brings to Google is several-fold. I'm probably missing a few, but here's what comes to mind.

1- Brand: Google seems very data scientist friendly. Data scientists will want to use Google technologies because Google is the 'data science' company.

2- Information on trends: Are data scientists suddenly spending a lot of time working in some language, or abandoning another? Are the Kaggle forums overflowing with a lot of Tensorflow related ML problems? Google could use this information to know what decisions to make (bonus: the people who'd curate and analyze the information are probably data scientists).

3- Promote/amplify: Google can hawk the crap out of Google related data science products to that market. And Google can probably provide GCP credits to registered Kaggle users, so that they can spin up their own little server, run their models, and get accustomed to using GCP in the process.

Those are just some thoughts. They likely have even better plans!