We Tried Dataform So You Don’t Have To — Here’s Why We Now Recommend It to Our Clients

At a certain stage in every data team’s growth, SQL logic starts to become a liability. Queries pile up. Business users pull conflicting numbers from different dashboards. Analysts are stuck rerunning the same transformations manually, while engineers are pulled in to stabilize reporting pipelines that were never built to scale.

As a consulting team helping organizations mature their analytics workflows, we see this daily. One of the most consistent pain points across industries and team sizes is the lack of structure in the data transformation layer — the crucial middle step between raw data and decision-making.

That’s why we decided to test Dataform, Google Cloud’s managed SQL orchestration tool for BigQuery. And after implementing it ourselves the way many of our clients would — from scratch, without DevOps — we now confidently recommend it to a wide range of teams looking to scale smarter.

What Is Dataform?

Dataform is a fully managed, cloud-native platform that helps teams automate and manage SQL workflows in BigQuery. It enables you to define, test, schedule, and document data transformations using SQL — all inside the Google Cloud Console.

Unlike traditional methods where analysts manage SQL scripts manually or rely on engineers to deploy transformations, Dataform brings structure to the chaos by offering:

• A browser-based IDE with Git integration
• Built-in scheduling and dependency orchestration
• Reusable SQL logic with optional JavaScript
• A visual DAG (Directed Acyclic Graph) for understanding lineage
• Integrated testing to validate outputs before they’re queried downstream

It’s powerful enough for engineers, accessible enough for analysts, and lean enough for growing companies trying to modernize without overcommitting.

Dataform vs dbt: Which Should You Choose?

Both Dataform and dbt aim to turn SQL workflows into well-governed, production-ready pipelines. But they take different routes — and the tradeoffs matter depending on your stack, team composition, and budget.

In our experience, Dataform is ideal for BigQuery-first teams that want the ease of dbt Cloud — without the license fees or the DevOps overhead. It’s also perfect for organizations who want analysts to contribute more directly to the data stack without needing to master command-line tools or YAML syntax.

What It’s Like to Use Dataform

We approached Dataform as many of our consulting clients would: no pre-built pipelines, no engineering shortcuts, no CI/CD configuration. Just a clean Google Cloud project, a dataset, and the need for a working, automated SQL pipeline.

The setup took under 30 minutes:

1. We created a Dataform repository inside the Google Cloud Console.
2. We opened a development workspace — like a Git branch for SQL models.
3. We defined a simple pipeline using SQL and a public BigQuery dataset.
4. We ran the pipeline manually, validated the results, and scheduled it to run every 5 minutes.

From the first click to the first production run, the experience was intuitive and frictionless. More importantly, it was visible — every table, view, and transformation was mapped in the DAG and versioned in Git.

Why We Recommend Dataform to Our Consulting Clients

As a data analytics consulting firm, we don’t just test tools for features — we evaluate them for how well they solve recurring problems across diverse teams and industries. Dataform stands out because it addresses the most common pain points in transformation-layer workflows — and does so without requiring additional infrastructure, hiring, or training.

Here’s what it enables:

1. Faster Time-to-Insight

Automated pipelines reduce the turnaround time between raw data and usable insights. Analysts don’t need to rely on engineers for deployment, and teams don’t have to wait days to validate metrics.

2. Built-In Data Governance

With Git integration, built-in testing, and a visual DAG, every transformation is traceable. KPIs can be audited back to their source logic. Changes are versioned, documented, and reviewable.

3. Collaboration Without Bottlenecks

Engineers can review logic and enforce standards, while analysts can build models independently — without handoffs or Jira tickets. Everyone works in one place.

4. Cost-Efficiency That Scales

Unlike dbt Cloud, Dataform doesn’t charge per user or project. It’s included as part of Google Cloud, meaning you only pay for BigQuery compute. For many clients, this reduces costs without sacrificing capability.

5. Sustainable Scaling Without Engineering Burnout

Dataform empowers lean data teams to operate like mature ones. Instead of asking your engineers to build custom orchestration from scratch, you get a fully managed system — ready to use, ready to grow.

When Should a Company Use Dataform?

We recommend considering Dataform when:

• You already use BigQuery as your data warehouse
• Your SQL logic lives across multiple tools or dashboards
• You need to version, test, and schedule transformations — but don’t want to set up dbt or Airflow
• You want to reduce the dependency on engineering for everyday reporting logic
• You’re looking for a lightweight, scalable foundation for analytics automation

Whether you’re building your first production-grade data model or replacing a tangle of legacy scripts, Dataform provides the structure and transparency modern data workflows demand.

Final Thoughts: A Quiet Power Tool Worth Your Attention

We didn’t expect Dataform to be a core part of our modern data stack playbook — but it earned its place. In our view, it’s one of the most accessible, scalable, and cost-effective ways to operationalize SQL workflows in BigQuery today.

It lets analysts ship reliable data models. It gives engineers peace of mind. And it helps business stakeholders trust the numbers they rely on every day.

We tried Dataform so you don’t have to.
But if you’re serious about scaling your analytics workflows, you probably should.

Interested in Implementing Dataform?

We help clients transition to modern analytics workflows — from messy SQL scripts to structured, scalable pipelines.

Our Dataform consulting services include:

• Initial setup and repository design
• Workflow modeling and optimization
• Testing and governance frameworks
• Team onboarding and documentation
• dbt → Dataform migration consulting

Reach out to us to learn how we can help turn your data chaos into a governed, automated system — faster than you think.