If you do analytics, you probably usually go straight for Python Notebooks, and create a bunch of graphs to visualize the analysed data. When you don’t want to show that data to someone outside, and hide implementation details, a nice alternative, and my de facto favourite, is to go and use IcCube. As an exercice, I was teaching how to get ready and graph quickly with IcCube, using air quality data.

I had a really time-limited effort to do to prove how to write a command line wrapper for an open API a customer is developping. The target Rest API is the jquants-api, as presented in a previous post. I chose to implement the wrapper in GoLang, which proved to be extremely fast and pleasant to do. The task was eventually done in a short evening, and the resulting Golang wrapper with core features has been uploaded on github.

I am excited about Project Loom. The project focuses on easy to use lightweight concurrency for the JavaVM. Nowadays, the JavaVM provides a one java thread to one OS thread model to the programmer. While it’s actually the current Oracle implementation, it used to be that many JavaVM versions ago, threads provided to the programmer were actually green threads. Project Loom goes down that road again, providing lightweight threads to the programmer.

The just out of beta jquants-api propose sets of daily financial data from over 4000 listed companies on the Tokyo Stock Exchange. Currently, the sets of data that can be accessed via the API are: Listed Issue Information Stock Price Information Market Information with more to come in the near future. While current historical data is only available starting from 2017 users are encouraged to provide feedback to the API via usual github (etc.

Building up on the previous iccube tutorial, here are a few key extensions to reporting. First how to extract data from a command line client written Get data using CLI (or Java) String url, user, password, file; public static void main (String... args) throws Exception { Connection connection = DriverManager.getConnection(url, user, password); OlapConnection olapConnection = connection.unwrap(OlapConnection.class); runMDX(file, olapConnection); if (watch) { try { watch(file, olapConnection); } catch (InterruptedException e) { throw new RuntimeException(e); } } olapConnection.

Modrzyk, N. (2019). Clojure. In: Sakr, S., Zomaya, A.Y. (eds) Encyclopedia of Big Data Technologies. Springer, Cham. https://doi.org/10.1007/978-3-319-77525-8_302 By the same sane community that gives us tablecloth, comes a full blown Machine Learning library named the scicloj. Explaining the details of the whole library or machine learning in general being beyond the scope of this article, we will instead present how the reader can put into action a KNN, K Nearest Neighbor algorithm to guess quotes retrieved from the previously presented JQuants API.