Dbt Pros And Cons Worksheet

Why You Probably Need a Dbt Pros And Cons Worksheet Before Starting

We've all been there. Management asks you to evaluate dbt for the team, and suddenly you're scrambling to put together a one-pager that actually means something. A Dbt Pros And Cons Worksheet isn't just a checklist — it's a survival document. When I sat down to build ours at my last company, I expected to spend a couple hours. It took three days because people kept asking for more nuance. A good worksheet has three columns: the feature or consideration, the advantage, and the catch. That's it. Anything more complicated just becomes noise. The middle column is where most people fumble. They write vague positives like "easy to use" without backing it up with specifics. Don't do that. Every entry needs a concrete example or metric behind it. I once had someone list "great community" as a pro without specifying that the community support for their particular use case — incremental models on Snowflake with complex partitioning — was essentially nonexistent. The workaround involved reading through three years of GitHub issues and piecing together a solution from unrelated threads. Took about four hours that could have been saved with one line of real context.

How To Build Your Own Dbt Pros And Cons Worksheet

Start by mapping your actual workflow. Not the theoretical one. What does data modeling look like at your company right now? Who touches models after you hand them off? What breaks when something goes wrong at 2am? Then go through each major capability of dbt and rate it honestly. Version control integration. Testing framework. Documentation generation. Materialization strategies. Jinja templating power. Macro ecosystem. Deployment pipelines. Each one gets a pro, a con, and a severity rating. High, medium, low. If you can't fill out all three columns for a given item, you haven't thought about it enough yet. The section most teams skip is the deployment and maintenance column. People fall in love with the development experience and forget about the reality of running this at scale. Model count. Compilation times. CI/CD friction. Resource costs. These compound over time in ways that aren't obvious until you're already deep in.

Common Pitfalls Nobody Talks About

Here's what I wish someone had told me before building our first comprehensive evaluation. The biggest issue isn't any single feature — it's the learning curve collision. Your analysts who are comfortable in SQL will struggle with Jinja and Python macros. Your engineers who know Python well will push back on the abstraction layer. This isn't theoretical. We spent six weeks dealing with resistance from both sides before the workflow settled. Another thing: the testing story. dbt's built-in testing is solid for basic checks. Custom data quality rules? That's where things get messy. I found myself writing custom Python tests that ran outside of dbt entirely, then feeding results back into the dashboard. Not ideal, but workable. Most teams I know ended up with a hybrid approach because dbt alone doesn't cover everything their stakeholders need validated. Cost opacity is the third silent killer. Your first dbt project might run fine. By month six, compilation alone can eat into significant compute credits depending on model count and project structure. We saw our Snowflake costs increase by roughly forty percent after onboarding dbt, and about half of that was purely from dbt-related queries during compilation and materialization phases.

Where To Find A Dbt Pros And Cons Worksheet Template

There are several community templates floating around on GitHub and the dbt Slack workspace. The one I ended up using was adapted from a template shared by the dbt community, modified extensively to include our specific cloud infrastructure considerations. I'd recommend starting there rather than building from scratch, but don't treat any template as final. Each organization has different constraints around governance, team size, and existing tooling that will change what matters in your evaluation. For what it's worth, our final worksheet ran about two pages when properly formatted. Everything longer became unreadable. Everyone I know who wrote more than two pages ended up with a document nobody referenced after the initial meeting.

When To Walk Away

This deserves its own section because it gets overlooked. dbt isn't always the right answer. If your data stack is under fifty tables, if you have no dedicated data engineering resources, if your team consists entirely of business analysts who barely touch SQL — then you're probably adding complexity without getting proportional value. Simple SQL scripts in a shared notebook environment might serve you better. Or a managed transformation tool that abstracts away the version control and deployment pieces. The worksheet should help you reach this conclusion honestly, not just confirm your initial bias toward the popular tool. We nearly went down the dbt path at one company despite having twelve people on the entire data team and a modest analytics warehouse. The worksheet saved us from that mistake. The pros column was honest, and the cons column was longer and heavier than we expected.