Bringing autocomplete to Analytics Engineers

Introducing the most powerful real-time autocomplete and code analysis inside dbt Core™ projects.

Brett Griffin

October 25, 2022

The missing piece of our workflow

Today, we're opening Deep Channel up to a public beta by announcing a feature that will push the entire Analytics Engineering field forward: real-time autocomplete and code analysis for dbt Core™ projects.

Autocomplete has long been table stakes for developer tooling in every other area of engineering.

But today, Analytics Engineers spend their day spread across several disparate and disconnected tools, each lacking the valuable context of the others. The result is a lot of time spent guessing about what columns exist or if a model is valid.

Deep Channel's real-time code analysis gives Analytics Engineers the entire scope of their data warehouse and dbt Core™ project directly in their workflow.

Real-time code analysis means we'll spend less time guessing about whether sources, CTEs, or columns are in scope or if our model will even compile, and more time doing deep, focused work.

No matter how far down into a project a model resides, the autocomplete and code analysis have column-level awareness of the models above it, giving a complete picture of what is in scope.

When one model changes, every other model is instantly aware of a compilation error that may have been introduced:

Tedious tasks like transposing long lists of column names are replaced with shortcuts that do the heavy lifting for us:

Beyond autocomplete

But the technology that underpins this opens up possibilities that go so far beyond just autocomplete.

Want to know where a column, model or CTE was defined, or even what's inside it? Just hover over it, peek at its definition, or quickly jump to its definition:

Finding errors faster

Today, running a model is conflating two things: it’s how we materialize a model, but it’s also an expensive and time-consuming way to validate the syntax of the model.

Deep Channel surfaces compilation errors as you type, saving you the time and warehouse credits of failed runs:

Where we're coming from

We built this out of our learnings from working inside dozens of dbt Core™ projects since 2016.

The analytics lifecycle is a complex orchestration of several moving parts. dbt Core™ sits at the heart of the analytics lifecycle, and functions as a critical protocol in this complex orchestration.

But to our current tools, our models are merely an unrecognizable blend of SQL and Python. We have to use lots of context clues to form a complete picture.

In Deep Channel, dbt Core™ and the data warehouse are fused together so closely that models become living extensions of the warehouse.

We've carefully designed an experience that looks and feels like our current tools, but with a degree of cohesion never seen before.

Where we're going

This fundamentally changes how we do our craft. But this is only the first step towards unifying the complex toolchain Analytics Engineers operate within today. We're excited to soon share features that expand further into your workflow.

We'd love for you to join us on this journey. You can download Deep Channel today.

Have questions, thoughts or feedback? We'd love to hear it - reach out to us at

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