coolio222

Can you download files in headless Firefox or Chrome?

Which is kind of sad. No Chrome Headless library supports dowloading of files.

I encountered that problem. The ad hoc fix, was to have a version field in each event and functions that translate the old event into new event(s). The code that processes the events only processes events of the current version. If your old events had been denormalized this might result into repetition of events when splitted.

Does anyone knows details about the KSQL-engine that computes the queries? According to their git, there can be multiple KSQL-engines in a Client-Server configuration. Is the workload for one query distributed? Is the SQL translated into a program using the Kafka streaming API?

A pump-and-dump scheme drives the price up by promoting the commodity only to allow insiders who bought it earlier to sell when the price peeks just before the the price plunges again.

You can easily see it in penny-stocks and/or OTC-stocks. The price stays flat, then peaks, then crashes to about the pre pump-and-dump price.

I haven't seen such a behaviour in any of the new coins. Does anyone have an example or link to a chart?

Does anyone knows the difference to other languages like STAN?

Both use Hamiltonian Monte Carlo. As far as I know STAN cannot model factors but can restrict a variable to integers. In this case, Turing.jl and STAN do the same and switch back to slower MCMC.

So why should I switch to turing.jl?

Their wiki shows that turing.jl is about 10 times slower than STAN:

  Results
  Model 	Turing 	Stan 	Ratio
  Simple Gaussian Model 	1.2 	0.06 	20.24
  Bernoulli Model 	1.53 	0.05 	32.73
  School 8 	2.34 	0.1 	24.41
  Binormal (sampling from the prior) 	0.88 	0.11 	8.37
  Kid 	32.66 	4.72 	6.92
  LDA-vec 	23.94 	3.78 	6.34
  LDA 	72.78 	3.78 	19.28
  Mixture-of-Categorical 	22.28 	6.41 	3.48
  NOTE 1
  Numbers here are inference time in second - smaller number   indicates better performance.

Thank you very much. I checked Wikipedia for Knuth-Bendix and will try to get hold on the book.

You defined rewrite rules that reduce an expression down to a 'normalized' form such that two expression are equivalent if they reduce to the same 'normalized' form. These rules are obvious for simple arithmetic expressions.

Is it possible to deduce the normalized form and the rewrite rules from a set axioms automatically (e.g. for relational (like SQL) expression with operands like join, project, filter)?

Very cool. The whole pandas API is huge. What was your focus for your implementation? Isn't there another well known js library that allows SQL operations including aggregates? How fast is it compared to that?

What happens if I load a 100MB csv file into a pandas.js dataframe in the browser and compute an aggregate? How fast is this?

May I ask why you have chosen Spark? I get that it it more convenient than Hadoop. But why not use an analytics DB like Redshift or Impala. What's the use case for Spark? Do you store your data in flat files and how do you access it with standard tools ala Power BI, Tableau, Looker?

Are you sure about that?

The European Union General Data Privacy Regulation regulates only personally identifiable information. IF(just an assuption) the collected data does not identify the user, does it still apply?