Analytics

Most of the focus of getting to 2.0.0 was on accuracy and robustness, however much of the focus since has been on performance and speed. This post is a little peek behind the curtain of how we went about that, and what some of the biggest improvements have been.

Today we released SQLFluff 1.0.0, the first stable release of SQLFluff.

The information you have is not the information you want. The information you want is not the information you have.

…or at least can never be “purely” objective.

The kind of ‘courage’ I’m talking about at the end doesn’t need to be loud. Often courage is presented as just the knight on a horse leading the charge, but I think courage can also be a quiet confidence that doesn’t let an issue drop, or just asking the question that nobody is asking at the right time.

There is a balance to be played in the role of any person who is responsible for metrics and measurement. How to approach the perennial question: “can you make me a dashboard/report so I can see X”.

Once upon a time, there was a company of tailors… and so she drew up a position for a number wizard…

This year I spoke at the Coalesce 2021 conference about a topic a bit broader than just SQL linting or data in small businesses.

Following on from part 1, we go from getting serious into philosophy as we track our growing analytics function.

Many people are rediscovering bread, and many organisations are rediscovering data. Both are things that people are devoting considerable time and attention to right now to provide reassurance, grounding and direction in what is otherwise a bewildering time.

SQL has been around for a long time, as a language for communicating with databases, as a communication protocol. More recently with the rise of data as a business function, or a domain in it’s own right SQL has also become an invaluable tool for defining the structure of data.

In this episode of the Hub and Spoken podcast, I have a chat with Jason Foster at Cynozure, discussing building data functions from scratch in a fast-paced business.

Alan presenting on the dbt setup at tails.com in one of the early London dbt meetups. Talking about continuous deployment, testing, alerting, and making charts on top of your dbt runs.

If you use dbt and Snowflake, we made a dbt package you can use to make sure that you keep your dependencies tidy and understandable.

Lots has been written about data functions with lots of tools and budget. In startup-land that’s not an option. This is the story of how we’ve done Business Analytics on a Shoestring.