Not got time for all this? I’ve marked 🔥 for my top reads of the month :)

SQL. Three simple letters. Ess Queue Ell. /ˌɛs kjuː ˈɛl/.

In the data world they bind us together, yet separate us.

As the saying goes, England and America are two countries divided by the same language, and the same goes for the batch and streaming world and some elements of SQL.

Another year, another Current—another 5k run/walk for anyone who’d like to join!

Whether you’re processing data in batch or as a stream, the concept of time is an important part of accurate processing logic.

Because we process data after it happens, there are a minimum of two different types of time to consider:

So. Many. Interesting. Links. Not got time for all this? I’ve marked 🔥 for my top reads of the month :)

I wanted to post a Carousel post in LinkedIn, but had to wade through a million pages of crap in Google from companies trying to sell shit. Here’s how to do it simply.

In this blog post I’m going to explore how as a data engineer in the field today I might go about putting together a rudimentary data pipeline. I’ll take some operational data, and wrangle it into a form that makes it easily pliable for analytics work.

After a somewhat fevered and nightmarish period during which people walked around declaring "Schema on Read" was the future, that "Data is the new oil", and "Look at the size of my big data", the path that is history in IT is somewhat coming back on itself to a more sensible approach to things.

As they say:

This is good news for me, because I am old and what I knew then is 'new' now ;)