dbt: how to make data transformation easier

Before loading data into a data warehouse (DW), the information must go through an entire cleaning, consistency checking and combination process.

Translated, data needs to be transformed – the “T” of ELT (extract, load and transform). This makes the information valuable and reliable for companies.

New ways to perform this function are increasingly emerging, but dbt , from dbt Labs , stands out!

Indicium is an official dbt partner, and in this article we will show you better what this tool is, its advantages, how it impacts companies that use data to generate value and its role in the modern approach to data.

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What is dbt?

dbt is a data transformation tool that allows analytics engineering professionals to operate information using SQL , in addition to facilitating and organizing the DW construction environment .

What does this mean in practice?

This instrument makes the workflow of data teams even faster and more efficient , bringing organization and agility by using a relatively simple query language .

Flexibility is another strong point of dbt , being possible to connect it to different databases , such as BigQuery, Redshift, Databricks and others. In fact, you can check all compatible fonts in the official documentation .

As it is an open source project , dbt grows and improves every day with the support and contributions of a very engaged community. And, as you will see below, this is one of its main advantages .

What are the advantages of dbt?

The strong presence of the community makes dbt a tool made by and for analytics professionals , meeting the main demands of those who work in the area.

The low learning curve and native testing support make dbt a very competitive choice, especially if your data project does not rely solely on analytics engineering professionals . This way, everyone on the team can contribute to data transformation.

The main advantages that dbt offers are:

• easy to use for those who are not data science professionals
• extremely flexible data modeling
• Easy version control application
• open source and customizable
• integrated data quality testing
• low learning curve
• integrated documentation

DBT is facilitating the transformation of data for countless companies, but its use can be even better leveraged with the right infrastructure. We will talk more about it below.

dbt and the modern approach to data

The Modern Data Stack (MDS) is the new combination of best practices and tools for creating a robust, complex and highly efficient data infrastructure.

In this approach, dbt plays the fundamental role of not only transforming the raw data within a DW, but also of keeping it safe and organized .

In this way, resource and risk management in each project, in addition to compliance with regulatory standards, is guaranteed. Using dbt within this infrastructure increases pipeline flexibility and allows analysts to define business rules for their analyses.

The governance offered by this tool eliminates all doubts that the data may generate, and dbt does this very well with version control, testing and alerts for the sake of information security.

The main functions of dbt

The main objective of dbt is to transform data into a DW , but the tool offers extra functions that optimize this process:

Tests

• DBT tests data quality , integration and code performance
• It is possible to create test programs that check information and its values ​​within specific columns
• There are automated tests that implement necessary changes

Deploy

• dbt has a built-in package manager that allows analysts to publish public and private repositories
• Analysts and analytics engineering professionals can be referenced by other users

Documentation is another important function present in dbt. Let's talk more about this later.

Autonomy and documentation in dbt

Similar to what a data management platform does, dbt houses information in a single location ( DW ). And for a data warehouse to work well, your team needs to be able to understand it.

Therefore, if there is no set of documentation to explain the tables and their uses, your project can easily end up getting stuck. Therefore, documents are automatically generated in dbt , making it possible to send them in deploy .

As most organizations have complex business logic behind their data reports , relying on dbt means having all changes already made recorded and easily traceable if repairs or updates are needed.

The tool also creates visual representations in the form of maps to show the company's data flow through each table in the ETL process .

Make your team's work easier

Indicium is a specialist in dbt and has professionals who have expertise in the tool due to the official partnership with dbt Labs .

Power your data transformation with dbt !

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