Implementing common data transformation techniques with dbt

High-quality data is the foundation for every data-driven decision. But clean, trusted, and analysis-ready data doesn’t happen on its own—it requires deliberate transformation.

Data transformation is the process of cleaning, verifying, and reshaping raw data into a format stakeholders can actually use. While writing individual transformations in SQL or Python might be simple, managing them at scale is not. Designing, testing, documenting, and deploying code across your entire data estate requires structure.

That’s where a data control plane comes in. In this article, we’ll explore common data transformation techniques—and how dbt helps teams implement them in a scalable, governed, and cost-efficient way.

Data transformation: core concepts

Data transformation is the process of cleaning, restructuring, and optimizing raw data so it’s usable for analysis. It takes data from multiple sources and turns it into a consistent format that business users and systems can trust.

Think of it like translating between languages. Raw data arrives in different formats, schemas, and structures—each its own “dialect.” Transformation turns that into a shared, standardized language that analytics tools and teams can understand.

Two common pipeline architectures

You can build transformation pipelines in different ways depending on your team’s needs. Here are two typical approaches:

Approach 1: Sequential processing pipeline

1. Extract raw customer data from a CRM
2. Clean and validate email addresses and phone numbers
3. Aggregate purchase history by customer ID
4. Enrich with demographic data from external sources
5. Load the final dataset into your analytics warehouse

Approach 2: Parallel processing pipeline

1. Extract raw customer data from a CRM
2. Split into three parallel streams:
   • A: Clean contact information
   • B: Aggregate purchase metrics
   • C: Enrich with external demographics
3. Merge all streams back together
4. Apply final validation checks
5. Load into your analytics warehouse

Both methods deliver the same end result. Sequential pipelines are easier to debug and maintain. Parallel pipelines offer faster performance, but require more complex orchestration—something that tools like dbt can help simplify and standardize.

Types of data transformation

Transforming data usually means applying one of a fixed set of operations to change data to a more usable format. Sometimes, this means addressing errors or inconsistencies in your data. ‌It also involves changing data into a format that's more readily usable for your use cases.

Data transformation pipelines save time and money by bringing consistency to data. Without data transformation pipelines, everyone—analysts, data engineers, and business users—would be slicing and dicing data their own way, wasting time and introducing data inconsistencies.

Most of the time, you'll be applying one of the following transformations to your data:

• Cleaning. ‌Removing errors in inconsistencies from your data—missing fields, inaccurate entries, duplicated data, etc.
• Aggregation. ‌Rolling up critical values for faster access—for example, sales data for a given customer or time period.
• Generalization. ‌Breaking up a single data unit into a hierarchy, such as an address.
• Discretization. Transforming continuous data, such as ages, into a set of ranges (e.g., ages 18-29) to make it easier to drive initiatives such as targeted marketing.
• Normalization. ‌Enforcing standards for the format of certain fields and rationalizing data types and identifiers. Example: converting currency data into a single standard currency, such as USD.
• Validation. Ensuring that data is in the correct format. One example is verifying that phone numbers have the correct number of digits, that they have a valid country code, etc.
• Enrichment. Also called attribute construction, enrichment adds additional data to enable enhanced decision-making — e.g., adding weather data to scheduled shipment information to warn customers about potential delays.
• Integration. Bringing in data from multiple sources to create a single, consistent data set that doesn’t require complex joins or high-latency connections across different databases.

When done well, transformation turns chaotic inputs into structured, trusted assets—and dbt helps you manage and scale these workflows like code.

dbt: A control plane for data transformations

Data teams can write transformations in many ways—from ad hoc SQL to custom Python scripts. But managing these workflows at scale requires more than just code—it needs structure, visibility, and repeatability.

That’s where dbt comes in.

As a transformation control plane, dbt helps teams manage the entire lifecycle of analytics code: development, testing, documentation, and deployment. Here’s how:

• Treats analytics like software. ‌dbt lets you write transformations in SQL or Python, version them in Git, and manage them like code—so changes can be tracked, reviewed, reused, and rolled back with confidence.
• Works across warehouses. dbt provides a consistent, vendor-agnostic framework that supports all major data platforms. With support for both SQL and Python, contributors from across the team can build and maintain transformations without learning new tools.
• Enables built-in testing. ‌Testing transformations isn't an afterthought. With dbt, you can write tests alongside your data transformations that are run automatically at various points to ensure the code is correct before it touches production data.
• Generates documentation and lineage. ‌dbt builds documentation as part of your workflow and visualizes lineage across your models. This makes it easier for stakeholders to trust and understand the data—without needing to ask engineers how it works.

With dbt, your transformation workflows are structured, tested, documented, and discoverable—by default.

Avoiding common pitfalls in data transformation with dbt

Writing transformation code is just the beginning. Teams also need safe, repeatable ways to deploy changes—and ensure data consumers can easily find and trust what’s been built.

dbt helps organizations avoid common transformation challenges like:

• Corrupted values from bugs
• Unreliable deployment processes
• No rollback strategy
• Low data discoverability

Let’s look at each of these issues in detail and how dbt addresses them.

Corrupted values from bugs

Unchecked bugs can break reports, mislead stakeholders, and create costly cleanup. dbt addresses this with multiple layers of quality control:

• All transformations live in version-controlled code (SQL or Python)
• Developers work in isolated branches, then open pull requests (PRs)
• PRs trigger automated tests and peer reviews before merging to production
• dbt supports reusable code modules, reducing the chance of duplicating flawed logic

With dbt’s acquisition of SDF, developers can now emulate popular data warehouses locally—enabling early, fast feedback before pushing any code to Git. This shifts testing left and cuts down PR churn.

Unreliable deployment processes

Historically, deploying data changes often meant running manual scripts in production—a risky and opaque process.

dbt brings the rigor of DevOps to data workflows. You can implement CI/CD pipelines to validate, test, and promote code changes through dev, staging, and prod environments. This approach improves security, increases confidence, and reduces bottlenecks.

No rollback strategy

Even with robust testing, some issues only show up in production. With dbt, every transformation is versioned and tracked—making it simple to revert to a known good state while your team troubleshoots and fixes the issue.

Lack of data discoverability

The best models are useless if no one knows they exist. dbt solves this with built-in documentation and dbt Catalog.

Data stakeholders can search, explore, and adopt trusted models—complete with lineage and descriptions—without needing engineering help. This drives adoption, increases data trust, and supports true self-service analytics.

Get started with better data transformation today

You can write transformation logic in SQL or Python—but managing it at scale requires more than code. A control plane powered by dbt brings consistency, quality, and reusability to your analytics workflows.

With dbt, data teams gain a standardized, cost-effective way to build, test, and deploy models—while business users get governed, self-serve access to the data they need.

Start for free and see how dbt can power your modern data transformation workflows.

FAQs about data transformation