If you're diving into data engineering or analytics engineering, you've likely heard about DBT (Data Build Tool). It's a powerful tool that allows analysts and engineers to transform raw data into trusted datasets using only SQL. But one of the first questions people ask is: What databases and warehouses are supported by DBT?
Understanding which
platforms DBT integrates with is crucial for building scalable data
pipelines—and for advancing your career in the modern data stack. In this post,
we'll break it all down in clear, simple terms. DBT Training
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What Is DBT
and Why Does It Matter?
Before diving into
the specifics, let’s quickly revisit what DBT does. DBT enables data teams to
write modular SQL code, test it, document it, and deploy it—all in one
streamlined process.
It’s not a database
itself; instead, it connects to a data platform (like a warehouse or
database), transforms data in place, and gives you version control and
repeatability. That’s why knowing the DBT supported platforms is
essential.
Core DBT
Supported Platforms
DBT supports a wide
range of databases and data warehouses. These platforms are grouped into two
categories: DBT Core-supported and DBT Cloud-supported. Here's an
overview of the most popular ones:
1.
Snowflake
One of the most
widely used platforms with DBT, Snowflake is a cloud-based data
warehouse known for scalability and performance. DBT works seamlessly with
Snowflake, making it a go-to for many data teams. DBT
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2. BigQuery
Google’s BigQuery
is another major player. If your organization is in the Google Cloud ecosystem,
BigQuery plus DBT is a powerful combination for fast and scalable data
transformation.
3. Redshift
Amazon’s Redshift
is a cloud data warehouse tailored for big data processing. DBT supports
Redshift natively, helping teams manage transformations efficiently with SQL.
4.
Databricks
Databricks combines
data engineering, science, and analytics. DBT can connect to Databricks using
the Spark adapter. This makes it ideal for companies dealing with both
structured and unstructured data.
5.
PostgreSQL
PostgreSQL is an
open-source relational database. It’s supported by DBT Core and often used for
smaller-scale analytics or development environments. It’s great for learning
and experimenting. Data
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6. Snowpark
for Snowflake
Snowpark enables
developers to write code in Python, Java, or Scala for execution in Snowflake.
DBT recently added support for this, allowing teams to blend DBT with
programmatic data processing.
The Growing List of DBT Supported Databases
The DBT
ecosystem is constantly evolving. Alongside the main databases mentioned above,
DBT also supports:
·
Apache Spark – For
large-scale distributed data processing.
·
Trino/Presto – Useful for
querying data across multiple sources.
·
Firebolt – A newer,
high-performance cloud warehouse.
·
MotherDuck
(DuckDB) – Great for lightweight, fast analytics.
DBT’s
compatibility keeps expanding as the data engineering world moves toward more
flexible and diverse tooling.
How to Choose the Right Database for DBT
When picking
from the DBT supported databases, think about:
1. Your data size – Large datasets benefit from scalable cloud warehouses.
2. Budget – Some platforms are pay-as-you-go, others have fixed licensing
fees.
3. Performance needs – Do you need near-instant queries or is batch processing fine?
4. Ecosystem fit – If your company is heavily invested in AWS, Azure, or GCP,
choosing their native warehouse often makes sense.
5. Team skill set – Pick something your team is comfortable managing and querying.
Other
Notable DBT Supported Platforms
DBT continues to
expand its ecosystem. Additional platforms include:
- Azure Synapse Analytics
- Apache Spark
- SQL Server
- Presto/Trino
- Exasol
- Oracle (via community adapters)
Some of these
require installing third-party or community-developed adapters. Always check
compatibility and stability before using them in production.
How to
Choose the Right Platform
Choosing among the
many DBT
supported platforms depends on your team’s tech stack, budget, and
goals. Here are a few tips:
- Cloud-first? Go
with Snowflake, BigQuery, or Redshift.
- Open source fans? PostgreSQL or DuckDB are great starters.
- Large-scale machine learning? Databricks may be your best bet.
- Cost-conscious? PostgreSQL or a hybrid solution can help manage costs while
learning DBT.
Regardless of the
platform, DBT brings standardization and reliability to your transformations.
Why This
Matters for Your Career
Knowing which DBT
supported platforms are in demand can give you a serious edge. Here’s how:
- Expand your skillset by working across multiple platforms.
- Impress employers by understanding the full data pipeline.
- Increase job opportunities by mastering both DBT and high-demand warehouses.
Learning DBT along
with tools like Snowflake or BigQuery makes you incredibly valuable in today’s
data job market. Data
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FAQ:
1. What are the most popular databases supported by
DBT?
Snowflake, BigQuery, Redshift, Databricks, and PostgreSQL are the most commonly
used with DBT.
2. Can I use DBT with traditional SQL databases?
Yes, platforms like PostgreSQL, MySQL (community adapter), and SQL Server are
supported, though some may need extra setup.
3. Does DBT work with real-time databases?
DBT is optimized for batch transformations. It doesn’t currently support
real-time or streaming data natively.
4. What’s the difference between DBT Core and DBT
Cloud in terms of platform support?
DBT Core supports many platforms, but DBT Cloud offers additional integrations,
easier setup, and collaboration tools.
5. How do I know if a platform is fully supported
by DBT?
Check the official DBT documentation or GitHub for a list of officially and
community-supported adapters.
Final
Thoughts
DBT is transforming
the way we handle data transformations—and it’s only as powerful as the
platforms it runs on. Whether you're using cloud data warehouses like Snowflake
or open-source tools like PostgreSQL, understanding the landscape of DBT
supported platforms helps you build better workflows and grow your data
career.
As the DBT
ecosystem evolves, more integrations are on the horizon. Stay curious, keep
experimenting, and you’ll stay ahead in the world of data.
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