Today we’re releasing a powerful, first-class integration with the dbt Semantic Layer.
At the heart of it is a new Metrics Cell: an easy-to-use UI that lets anyone access trusted, governed metrics, without writing any code. Users can specify metrics, dimensions, and time grains, and get back a data frame that they can use to analyze, visualize, and share.
But there’s more – connecting to the dbt Proxy Server unlocks another superpower: the ability to write dbt-flavored SQL, right in Hex. Now you can use refs, macros, and sources directly in your queries.
We’re so excited to unleash the full potential of the work Analytics Engineers do every day, and empower thousands of users to more easily ask and answer questions of data.
For the last few months, our friends at dbt Labs have been working on their new Semantic Layer – a way for Analytics Engineers to define consistent, governed metrics as part of their transformation pipelines. You can read a ton more about it here.
Hex’s integration makes those metrics usable for everyone to ask and answer questions, with high trust that they’re looking at the data the right way.
For example, let’s say the Head of Sales Ops wants to analyze last quarter’s revenue, by month, by location, broken down by rep. Previously, they’d have to understand the underlying data schemas, and know how to write a fairly complex query. And even if they are a SQL master, there’s no guarantee that they’re going to build it in a consistent way – they could well get the incorrect answer.
Now, if their data team is using the dbt Semantic Layer, they can just add a Metrics cell to a Hex project, specify the “revenue” metric, time grain and location, without having to know how to write any SQL themselves. If they choose to switch to looking at the results by week, or add another dimension to slice by, they don’t need to fuss with the SQL and adjust
group by statements; a quick UI change updates the query for them.
Hex workflows using the dbt Semantic Layer mean that everyone can do more with data together:
And in Hex the analysis doesn’t stop there. Just like SQL cells, the Metrics cell returns a data frame, which can be used downstream in any of Hex’s cells. You can visualize the results in a Chart or Map cell, transform the data frame in the Filter or Pivot cell, reshape it using Dataframe SQL, or even use a Python package like Prophet to produce a forward forecast, like in this project.
As soon as we started playing with the dbt Server, we realized that this integration could unlock another superpower: the ability to write dbt-flavored SQL, right in Hex. No more swapping out table names when switching between a dbt project and SQL IDE – refs, sources and macros are all available where you’re already doing your work.
With dbt-SQL, Hex editors can:
You’re even able to mix and match Jinja: if your SQL cell contains Jinja that references a variable in your Hex project, we’ll compile that, and leave the rest for dbt.
We’re pumped to have our dbt Semantic Layer integrations out in the wild, and can’t wait to see what you all build with it. We’d love to hear feedback, or ideas on what you want to see next – get in touch at [email protected].