LookML or ELT? Three reasons why you need LookML

However, the business also wants to see Monthly Active Users. You can’t just sum up the daily active users because if the same user was active on multiple days, they would be counted multiple times for that month. This is an example of a non-additive metric, where the calculation is dependent on the dimensions it is being grouped by (in this case day or month).

You need to build another table or view that is grouped by month. But the business may also want to slice it by other dimensions, such as the specific pages that the users hit, or products they viewed. If the only tool you have available is the ELT tool, you’re going to end up creating separate tables for every possible combination of dimensions, which is a waste of time and storage space.

Exploratory analysis (powered by flexible metrics)
Even if you wanted to spend your time modeling every permutation, you’d be siloing your users into only analyzing one table or combination at a time. When they change how they want to look at the data, switching daily to monthly for example, they’d have to change the underlying table that they are querying, adding friction to the user experience and leading to less curious and less data-driven users.

LookML avoids this by enabling you to define one measure called “Active User Count” and allowing users to freely query it by day, month, or any other dimension without having to worry about which table and granularity they are querying (as shown below).

Consistency and governance in the “last mile” of your analytics

Even if you believe you’ve perfectly manicured the data inside your data warehouse with your ELT tool, the “last mile” of analytics, between the data warehouse and your user, introduces another level of risk.

Inconsistent logic
No matter how much work you’ve put into your ELT procedures, analysts, who may be working in separate ungoverned BI or visualization tools, still have the ability to define new logic inside their individual report workbooks. What often happens here is users across different teams start to define logic differently or even incorrectly, as their metrics and tools are inconsistent.

Maintenance challenges
Even if you manage to do it correctly and consistently, you are likely duplicating logic in each workbook/dashboard, and inevitably over time, something changes. Whether the logic itself, or a table or column name, you now have to find and edit every report that is using the out-dated logic.

Agile development and maintenance in your BI layer

LookML itself is a “transformation” tool. LookML applies its transformations at query time, making it an agile option that doesn’t require persisting logic into a data-warehouse table before analysis. For example, a hypothetical ecommerce company may have some business logic around how it defines the “days to process” an order.

As an analyst, I have the autonomy to quickly apply this logic in LookML without having to wait for the data engineering team to bake it into the necessary warehouse tables.

This is especially useful for use cases where the business requirements are fluid or not well defined yet (we’ve all been there). You can use LookML to accelerate “user acceptance testing” in these situations by getting reports and dashboards into user’s hands quickly — maybe even before the requirements are fully locked down.

If you’re solely using ELT tools and the requirements change, you will have to truncate and rebuild your tables each time. This is not only time-consuming, but expensive. Using LookML instead of ELT for quick, light-weight transformations puts data in the hands of your users faster and at less cost.

On the other hand, adding too much transformation logic at query time could have a negative impact on the cost and performance of your queries. LookML helps here as well. Using the same example, let’s say the business requirements solidified after a couple weeks and you’d like to add the “days to process” logic to our ELT jobs, rather than LookML. You can swap out the logic with the new database column name, and all of the existing dashboards and reports that were built will continue to work.