Looker is a fantastic product. It really makes data and visualizations much more manageable. The main goal of Looker is to allow people who aren’t data analysts to do some basic data analysis. To some extent, it achieves this, but there are limits to how far this can go. Ultimately, Looker is a big graphical user interface for writing SQL and generating charts. Under-the-hood, it’s programmable by data engineers, but it’s limited by the fact that non-technical users are using it.
The major design challenge for Looker is joins. A data engineer writes the joins into what Looker calls “explores”. Explores are rules for how data can be explored, but ultimately just a container for joins. When someone creates a new chart, they start by selecting an explore, and thus selecting the joins that will be used in the chart.
They pick the join from a dropdown under the word “Explore”. This is the main design bottleneck. Such a UI encourages users to have only a limited number of joins that can fit in the vertical resolution of the screen. This means limiting the number of explores, and hence limiting the ways tables are joined. This encourages using pre-existing joins for new charts.
This creates two problems.
- A non-technical user will not understand the implication of choosing an explore. They may not see that the explore they chose limits how the data can be analyzed. In fact, a non-savvy user may pick the wrong explore entirely, and create a chart that is entirely wrong.
- The joins may evolve over time. A programmer might change a join for a new chart, and this may make old charts incorrect.
The problem is that SQL joins are fundamentally interpretations of the data. Unless a join occurs on id fields AND is a one-to-one relationship, then a join interprets the data in some way.
So how can you limit the negative impact of re-using joins?
1. Encourage simple charts
Encourage your teammates to make charts as simple as possible. If possible, a chart should show a single quantity as it changes over a single dimension. This should eliminate or minimize the use of joins in the chart, thus making it far more future-proof.
2. Give explores long, verbose names
Make explore names as descriptive as possible. Try to communicate the choice that a user is making when they choose an explore. For instance, you might name one explore “Products Today” and another one “Product Events Over Time”. These names might indicate that the first explore looks at the products table, but the second explore shows events relating to products joined with a time dimension.
One of the mistakes I made while first starting out with Looker is naming the explores with single word names. I now see that short names create maintenance nightmares. Before assessing the problems with a given chart, I need to know which explore the maker chose for it, and because the names were selected so poorly, the choice was often incorrect.
I hope these ideas help you find a path to a maintainable data project. To be honest, I have a lot of digging-out to do!