dlt vs dbt: What is the Difference?

📘 Introduction

When you start learning modern data pipelines, you will quickly hear about both dlt and dbt. The names look similar, and both tools are used in data engineering workflows. But they solve different problems.

In this beginner-friendly guide, we will explain the difference between dlt and dbt, where each tool fits in a pipeline, and how they can work together.

💡 Quick answer

dlt is mainly used to load data.

dbt is mainly used to transform data.

That is the most important difference.

In a typical modern data stack, dlt helps you move data from sources such as APIs, databases, files, or Python objects into a destination like DuckDB, Snowflake, BigQuery, PostgreSQL, or a data lake.

After the data is loaded, dbt helps you clean, model, test, document, and organize that data inside the warehouse or lakehouse.

📥 What is dlt?

dlt stands for data load tool. It is an open-source Python library for building data loading pipelines.

With dlt, you can extract data from sources such as REST APIs, SQL databases, files, cloud storage, or Python objects. Then dlt loads that data into a destination.

During this process, it can infer schemas, normalize nested data, manage pipeline state, and support incremental loading.

Think of dlt as the tool that helps bring raw data into your data platform.

If you’d like to dive deeper into dbt (data build tool), our book Building Modern Data Pipelines with dbt: From Raw Data to Gold Standard with the Medallion Architecture provides a hands-on guide to designing modern data pipelines. It covers dbt’s core concepts and best practices, including building Bronze, Silver, and Gold layers with the Medallion Architecture. It also serves as a hands-on study guide for the dbt Analytics Engineering Certification.

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