__coaxialcabal

> Very few BI products have actually surpassed 100 users at target installations. And beyond 1,000 is a real challenge that only few, and even then with a lot of assistance, can support: Tableau, Looker, Microstrategy, maybe Birst, maybe Domo.

At one point Looker was essentially a web application with a query service that could be scaled by adding more servers behind a load balancer. Each end user is essentially working in a shared nothing environment and the query engine is driven by metadata stored in a git repository. Looker itself did not manage cache or analytical processes. All of the real effort to scale was in the database backend.

As it turns out, more data is a far second to doing anything at all.

Recursive CTEs have been implemented as UDx in Vertica, although possibly not available publicly. This is where you need to look carefully at can vs. should and carefully consider what you are trying to accomplish.

Recursive CTEs are significantly more difficult to implement in an MPP platform than SMP.

Related post from 2013.

tl;dr: Don’t build your app on top of a pile of crap in-house framework.

https://www.cloudbees.com/blog/about-paypals-node-vs-java-%E...

Hadoop doesn't take six months for the project you described. It sounds like your team was learning Hadoop, not executing on Hadoop. Are you concerned that you fought a utility in favor of a tactical solution that now requires its own independent maintenance maintenancelifecycle?

How would you describe the difference between an engineer with a specialization or specific expertise in "DevOps" and a "SRE"?

Two differences between a data scientist at the capability level of someone reading this article in earnest at a SV startup and an enterprise company in most of the rest of the country are cost of living and the assumption that moving past step two is a question of one person's independent ethical decisions

I'm not sure if open sourcing the data is absolutely necessary to reproduce the results given access to the prediction API. "Stealing Machine Learning Models via Prediction APIs" (https://arxiv.org/abs/1609.02943) examines techniques in this space. The API might also be used in adversarial training. The underlying data would be useful for advancing research, but not necessarily a prerequisite for reproducing the API.

AWS wins not because they have created an impenetrable black box, but because the economics of reverse engineering (and maintaining) are not cost effective for the majority of users.

I really hope GCP continues to do this with other types of syndicated public data feeds like American FactFinder, FRED, etc. They are missing a substantial audience that would see this as a killer app and compelling reason to move to GCP for this. The success of Enigma.io suggests there is a legit (although easily reproduced) business case here with minimal effort. Anyone who has ever worked to prep and integrate this type of data could now spend that time doing science.

Four Rooms is a great example of Roald Dahl's work..

Four Rooms is a 1995 American anthology comedy film directed by Allison Anders, Alexandre Rockwell, Robert Rodriguez, and Quentin Tarantino, each directing a segment of it that in its entirety is loosely based on the adult short fiction writings of Roald Dahl, especially Man from the South which is the basis for the last one, Penthouse - "The Man from Hollywood" directed by Tarantino. https://en.wikipedia.org/wiki/Four_Rooms

Someone Like You https://www.amazon.com/Someone-Like-You-Roald-Dahl/dp/014003... is a great collection of these short stories and a used copy may be had cheap!