Lars Kamp is the Co-Founder & Chief Data Engineer at intermix.io (http://intermix.io/) where he helps customers optimize Amazon Redshift and their analytics queries for peak performance. We’ve invited intermix.io to share a guest piece detailing the new data stack.

The World Chess Championship 2018, a match between reigning champion Magnus Carlsen and challenger Fabiano Caruana, just came to a conclusion in London. Since all twelve classical games were drawn, the players entered a tiebreak round, where Carlsen was crowned world champion. This is the first time all classical games have been drawn, and many chess fans expressed disappointment in what seemed to be a “dull” or “bloodless” series of games.

When we talk to analytics teams about what they're working on and what big projects are coming up, this is a common refrain we hear:

“Right now, we're working on getting data out to people and making sure everyone has the right dashboards. We also want to answer some of the bigger questions that are causing problems for our business, but it's important that people have what they need first.”

In case you missed it, we launched a free product earlier this year, Mode Studio.

Free products aren't common in the world of analytics. Even less common, we launched ours after already having built a strong business around a paid product. Quite often in the last six months, we've been asked why we did it.

The internet is awash in Venn diagrams of SQL JOINs and explanations of why they're necessary. Though it would be nice if we could answer every data question with a single table, more often than not, to get a complete picture, we need to combine different data sets.

As analysts, we know that each question we ask of our data can be answered with multiple potential languages and toolkits. Each language has its strengths and we've often pondered the distinctions. We've previously examined Group By, window functions and a general framework for thinking in SQL and Python.