Datetime parsing and rendering sometimes require optimization when iterating over a large dataset. Say you have about a couple million rows of timestamps you'd like to parse into a datatype. It could take some time if you use the wrong `import` or `crate`. In this article, I'll benchmark what it takes to load a couple million datetime stamps with Python's datetime, Numpy, Chrono, and Time

By far, my favorite RDBMS is PostgreSQL. Having worked with MySQL, MSSQL, Cassandra, Redis and more; the versatility of PostgreSQL continues to inspire me to write better and more complex SQL. I have a piece of software that reaches out to various news websites following best practices for crawling. Scraping content using provided by the sitemap.xml and following the rules set forth by the robots.txt file. The content of these sites can be anything, but the general approach is to collect as much news as possible to see if I can develop a series of heuristics to provide as technical indicators.