What do I use in this situation:

1) I need to store 100,000,000+ json files in a database

2) query the data in these json files

3) json files come from thousands upon thousands of different sources, each with their own drastically different "schema"

4) constantly adding more json files from constantly new sources

5) no time to figure out the schema prior to adding into the database

6) don't care if a json file is lost once in awhile

7) only 1 table, no relational tables needed

8) easy replication and sharding across servers sought after

9) don't actually require json, so long as data can be easily mapped from json to database format and back

10) can self host, no cloud only lock-in

Recommendations?

Depends on what your queries look like, I guess.

Ironically once because mongo was such a pain to work with I dumped the data from it into ES to get the better API, usability and Kibana.

Didn't Windows (on desktop) ignore OpenGL in favor of DirectX for years to great success, because it was deeply integrated into the platform?

Yes, Apple's ecosystem is the same walled garden it's always been. Any time it wasn't was an anomaly. The best fanboy response I can give is that they seem MUCH more willing to work with partners this time around.

Game developers: very sexy, easy to show off, has a (very) large supply of talent. Pay not bad, not good, work schedule very demanding.

COBOL developer: extremely unsexy, almost don't want to admit it publicly. Tiny supply, relatively much larger demand. Pay very good. Work schedule very predictable, not demanding. Almost a vacation.

Supply and demand. Ignore at your own peril.

Video games are projects and comparable to movies. You need different talent to perform different tasks at different times. Once that talent delivers, you probably won’t need them again.

Unless you’re an established studio, that’s capable of rotating staff from project to project, then there is really no feasible way of keeping staff around that is no longer necessary.

COBOL programmers on the other hand aren’t workin projects where they become unnecessary, because the systems they work on are forever.

Basically you can say, that nobody is working on Rail Toad Tycoon today, but there are COBOL programmers who are still maintaining main frame software that was build before Rail Road Tycoon even released.

I’m not sure how you’d run video gaming differently though.

But doesn't it also make sense for Amazon to be liable for allowing 3rd parties to sell counterfeit or not-as-advertised items?

Also, how far does this go? Where is the line crossed?

1) 16 notebooks from the book "Python Machine Learning" by Raschka & Mirjalili https://github.com/rasbt/python-machine-learning-book-2nd-ed...

2) Linear Regression, Logistic Regression, Random Forests, and k-Means Clustering notebooks by Nitin Borwankar https://github.com/nborwankar/LearnDataScience

3) scikit-learn tutorial notebooks by Jake VanderPlas https://github.com/jakevdp/sklearn_tutorial

4) Lots of deep learning notebooks from the book "Deep Learning with Python" by François Chollet https://github.com/fchollet/deep-learning-with-python-notebo...

Bonus) Jupyter notebook on AWS tutorial (when your local computer just won't handle your notebook requirements): http://efavdb.com/deep-learning-with-jupyter-on-aws/

Please share your jupyter notebook recommendations.

AWS also recently released a product called AWS SageMaker which is a hosted notebook environment [0]. I haven't been able to find any good tutorials on it unfortunately. They claim you can go from data, to notebook, to model, to production all within the AWS ecosystem integrating with tools like AWS ETL/Glue/S3/etc.

All magic.

[0] - https://aws.amazon.com/sagemaker/

NLP, genetic algorithms, and reinforcement learning.