What is dbt?
Dbt—which stands for data build tool—is a collaborative, open-source command line tool that allows users to transform data more effectively. Using dbt, data analysts can write simple SQL that reflects core business logic to transform their data.
Data build tool is an ETL (Extract, Transform, Load) tool, but it does not extract or load data. Rather, its specialty is transforming the data that is already loaded into your data warehouse. It also has other useful functionalities such as compiling code and providing documentation for version control, and it offers simple ways to test data for data integrity.
You can use dbt to help solve data quality issues, organize metrics and business attributes, and expedite your data transformation process. Within the analytics engineering workflow, data teams work directly within the data warehouse using dbt to make trusted datasets for reporting, ML data modeling, and operational workflows. Use dbt to develop models, compile your project, and build tables and views.
Why use dbt and what challenges does it help solve?
For simpler, faster data warehouse transformation
- Data build tool allows you to accomplish complex transformation work on your data warehouses without having to write complex code. It utilizes simple select statements that transform the data. This eliminates the need for complex and boilerplate code to create tables or views. Dbt is also optimal for those who want to transform data but are unfamiliar with programming languages.
As a code compiler
- At its core, dbt is a compiling tool. Users can write and run their dbt code from their command line.
- Data build tool has built-in features for providing data governance for all your dbt models.
- This includes capabilities to write custom documentation as well as version control, which you can share for your dbt models. Descriptions can be written in plain text or markdown for each model and field. This documentation, along with additional information, such as model lineage or field data types, is easily shared with your team via different configurable methods—such as a generated website that can be shared with large teams.
- Additionally, dbt offers different types of built-in testing methods for your data. Dbt provides out-of-the-box tests that you can easily run to improve the integrity of your models. You can test a specified column in a model for non-null values, unique values, relational values among corresponding models, and check for a value from specified lists. Any assertion you can make about your model in the form of a select statement can be turned into a test.
- Dbt also provides the ability for logging and alerting as well. With every task dbt performs, it generates events. These events are stored in the debug log file, which can be referenced at any time to enable detailed debugging of errors when they occur. You can also enable JSON log formatting, which will output rich, structured log information which can be piped into monitoring tools for analysis or used to power applications with dbt metadata. You can also set up alerts to get notified if a job run fails, succeeds, or is canceled.
How does dbt work?
You can arrange data transformations in dbt through data models. Every model has only one select query, and this will define the resulting data set. For each model that is created via a select statement, dbt allows you to configure a materialization. A materialization represents a build strategy for the select statement you create. Data build tool models can be materialized into the following:
- View: This default model is built as a view in the database.
- Table: This model is built as a table in the database.
- Incremental: This model is initialized as a table, but in subsequent runs, dbt inserts new rows and updates changed rows in the table.
- Ephemeral: This model is not built directly in the database, but is instead pulled into dependent models as common table expressions.
- Custom materializations can also be built if required.
How do I use dbt?
In order to use dbt, you will need to have a database with raw, populated data. dbt does not help with extracting or loading data, but rather with the “T” in ETL (Extract, Load, Transform) processes.
Data build tool connects to your data warehouse in order to run data transformation, and natively supports connections to many big-name data warehouses such as Snowflake, BigQuery, Redshift, and Postgres, among others. When connecting to dbt, you will be able to specify what schema that dbt should target to create models. You can use dbt from a local source or on its cloud for more scalable solutions.
Dbt transformations can be attributed to a project. All dbt projects are composed of a directory containing these required files:
- A project .yml file: This will contain the configurations of the dbt project.
- A Model .sql file: This contains the SQL query that will define the model. This select statement either transforms the raw data into a dataset ready for analytics or an intermediate step.
Projects also typically contain a number of other file types and resources as well, which can include tests, snapshots, documentation, and seed files.
In order to run dbt against your database, the command line will need to be prompted and executed using dbt commands. When you issue a dbt command, dbt will:
- Determine the order to execute the models of your project.
- Generate the DDL required to build the model, according to the model’s defined materialization.
- Execute the compiled queries against your data warehouse.
Executing these queries will create relations in the target schema in your data warehouse. These relations will contain the transformed data, ready for analysis.
Who should use dbt?
Dbt is a great tool for anyone who interacts with or handles data warehouses. This includes but is not limited to data engineers, data analysts, data scientists, or anyone that is familiar with writing select queries in SQL. Knowledge of programming basics is also helpful to anyone looking to make full use of this tool.
Data build tool has quickly become one of the top transformation tools for modern data teams because it eliminates many problem areas that data analysts face while also providing a reliable platform for swift data transformation and collaboration. All of these factors are a large reason why over 9,000 companies are currently using dbt and its features.
What are the benefits of dbt?
- Quick, clean, transformed data
- Low learning curve
- No need for boilerplate or complex code—dbt handles that for you
- Apply software engineering practices to analytics code: Anyone with a little SQL knowledge can utilize dbt. You only have to utilize simple select statements to build complete data models and get the most out of this tool.
- Keep track of documentation: Dbt provides a mechanism to write, version-control, and share documentation for your dbt models
- Improve the integrity of the SQL in each model: SQL can be difficult to test, since the underlying data is frequently changing. Dbt provides a way to improve integrity with out-of-the-box testing methods like non-null values, unique values, relational values, etc.
- Data refreshes
How can Search Discovery help?
Overall, dbt implementation alongside your data warehouse can help to optimize and improve your data warehouse processes and can enhance the results of your data analytics capabilities. Search Discovery can help to introduce dbt onto your data warehouse platform and showcase its powerful potential. Utilizing your core business logic and goals, we can automate a dbt integration that will make your data transformation faster and more simple.
(A note on capitalization within this post: dbt from dbt Labs is a specific tool, not a class of tools like CDP, so we retained the branded, lowercase appearance and capitalized at the beginning of sentences as necessary.)