dbt: Why It’s A Game Changer for Modern Data Teams
If you’ve spent more than five minutes around modern data stacks, you’ve probably come across dbt. At EdgeRed, we’ve used it with everyone from fintech startups to banks with decades of data – and it’s become a core part of how we help teams build trust in their data.
So if you’re wondering whether dbt is worth the hype – here’s our take.
What is dbt?
At its core, dbt (data build tool) helps you transform raw data in your warehouse into clean, analytics-ready tables using SQL. That’s it. It doesn’t move data. It doesn’t visualise it. It just does the transformation layer – and it does it really well.
Lately, dbt’s stepped things up by moving into the semantic layer—and honestly, it’s a bit of a game changer. You can now define key metrics like “profit” once and use them everywhere, so Finance and Marketing finally agree on the numbers (miracles do happen).
We’d love to think of it as the layer where business logic lives. And for most teams, that’s the layer that usually gets messy.
Why we love it
1. It uses SQL
Most of the analysts and engineers we work with already know SQL – so the learning curve is minimal. But behind the scenes, dbt treats everything like code: you get version control, reusable models, automated testing – all the good stuff that helps you scale.
2. Clear lineage, centralised definitions, no more black boxes
This one’s a big deal. dbt’s lineage graphs show exactly how models are connected. It means no more chasing down mystery joins or broken dashboards. Plus, it centralises all your metric definitions and business logic, so everyone’s singing from the same data sheet. When we roll this out with clients, the usual response is: “Wait, we can see that now?”
3. Testing and Documentation that just works
You can write tests directly in your model files (e.g. “this column should never be null”) and dbt flags any issues before things break downstream. Plus, the documentation is automatically generated – and stays up to date. No more digging through dusty wiki pages from 2021.
4. It scales with your team
From startups with one analyst to enterprise data teams with dozens of engineers, dbt supports both ends of the spectrum. Start simple, then scale up to multiple environments, CI/CD, and complex model hierarchies.
Where dbt fits in the stack
dbt works best with cloud warehouses like BigQuery, Snowflake, or Redshift. Here’s a typical flow:
- Raw data lands in your warehouse via tools like Fivetran or Airbyte
- dbt transforms that data into clean, analytics-ready tables as well as semantic layers (where we define key metrics)
- Dashboards (e.g. Power BI, Looker, Tableau) sit on top of dbt models
- Stakeholders get trusted insights – fast
We often describe dbt as “the bridge” between ingestion and insight. It’s where business rules get applied and logic gets standardised – and that’s what makes reporting actually useful.
What to watch out for
Like any tool, dbt isn’t magic. A few tips to get the most out of it:
- Model naming conventions matter – otherwise things get messy quickly
- Watch your dependencies – long chains of models can slow down builds
- Plan your tests – a few key checks will save you hours down the line
And remember – dbt is a tool, not a strategy. You still need strong governance, ownership, and alignment with business needs.
“dbt is like a playground for the entire data team—from engineers to analysts. It’s scalable, easy to integrate, and valued for its transparency and simplicity. Tip: start small, stay consistent, and expand gradually to keep momentum.”
– Prishilla, dbt specialist at EdgeRed
The bottom line
We’ve rolled out dbt for startups trying to escape spreadsheet chaos and for enterprise clients with sprawling legacy systems – and every time, it’s helped teams work faster and trust their data more.
If your current data workflow feels brittle, or if you’ve got analysts spending half their time untangling SQL scripts, dbt is 100% worth a look.
We’ve got a few dbt-certified engineers on the team who live and breathe this stuff – so if you’re keen to explore how it could work in your stack (without overengineering the solution), we’d be happy to chat. Whether you’re just starting out or scaling up, let’s chat about what makes sense for your team.
This blog was written by Prishilla, assisted by E.R.I.C.A.
About EdgeRed
EdgeRed is an Australian boutique consultancy with expert data analytics and AI consultants in Sydney and Melbourne. We help businesses turn data into insights, driving faster and smarter decisions. Our team specialises in the modern data stack, using tools like Snowflake, dbt, Databricks, and Power BI to deliver scalable, seamless solutions. Whether you need augmented resources or full-scale execution, we’re here to support your team and unlock real business value.
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