DBT

In the increasingly complex landscape of data analytics, organizations are facing countless challenges in achieving effective management of their data. Often distributed on heterogeneous systems and with a high effort regarding integration and the consequent extraction of insights.

With DBT, companies can overcome the complexities of ETL (Extract, Transform, Load) and embrace the ELT (Extract, Load, Transform) paradigm, making it easier to create scalable data pipelines and generate strategic insights in an efficient and reproducible manner , separating Data Movement processes from actual transformation processes.

Contatti di riferimento

Giorgio Tavecchia

Data Architect

Andrea Gioia

Chief Technology Officer

Overview

DBT, acronym for Data Build Tool, is an increasingly used tool in the Data Engineering panorama. Designed to simplify and automate the process of transforming data in a data warehouse, DBT offers an efficient and effective solution for managing the flow of data within an organization.

With its SQL code-based approach, DBT allows analysts and data engineers to easily define the necessary transformations on raw data, ensuring consistency, reproducibility and maintainability of the process according to the Data as Code principle. The use of the SQL language, in addition, it allows developers to minimize adoption time by being able to leverage existing knowledge to write data pipelines with agility.

DBT stands out for its flexibility in integrating with a wide range of data warehouse technologies and systems and operating according to ELT approach, making it a popular choice for companies looking to optimize data management and accelerate pipeline development time of analysis. In detail, DBT can be traced back to the letter T in ELT, therefore focusing on the Data Processing component and ensuring decoupling from the technological solution chosen to move the data into the target system.

Capabilities

DBT offers a series of distinctive features that position it as an essential tool within modern technology stacks, thus contributing to its success:

Automation of data transformations and materializations

DBT simplifies the data transformation process, allowing users to easily define desired transformations using SQL, specifying the desired materialization mode, and natively supporting incremental and snapshot processing capabilities.

Dependency management

DBT automatically detects and manages dependencies between different models and projects, ensuring that transformations are applied respecting their logical order, also providing the possibility of carrying out partial re-executions in case of errors.

Automatic documentation

DBT automatically generates documentation for all data models and transformations implemented, helping users better understand transformation structures and logic. The data pipeline itself becomes a source of documentation, providing the possibility to define additional metadata that facilitates understanding.

The documentation thus generated can be enriched and made available to users via a user friendly web interface, thus removing the need to have additional solutions to maintain.

Integrated testing

It offers built-in testing capabilities that allow users to verify the correctness of data transformations and pipelines. It is therefore possible to easily implement Quality Gateway which guarantees consumers always access to certified and quality data through the use of testing modules made available out of the box or which can be developed at the same time as development and with the same tool.

Advanced customization

DBT is not limited to the SQL language for defining data transformations but, to enable the development of customized and complex logic that is difficult to maintain in SQL, supports the use of Python code. Python and SQL transformations can be alternated without any constraints, ensuring maximum flexibility for developers.

Integration with Jinja

DBT allows the integration of the Jinja templating language into SQL, enabling features not normally permitted in pure SQL: among these we find the introduction of control structures such as conditional and loop statements, management of environment variables and definition of reusable macros both in models and in tests.

DBT Core & DBT Cloud

DBT is available in several versions that offer a variety of features to adapt to the specific needs of users and organizations. The open source version of DBT, known as DBT Core, provides core data transformation capabilities, including dependency management, automatic documentation, and integrated testing. This version is widely used by the community and offers a solid foundation to start using DBT.

For those who need advanced features and professional support, DBT Cloud is also available, a managed platform that offers an intuitive user interface, planning and pipeline monitoring capabilities, integration with collaboration tools like Slack and simplified integration with developer services. main Cloud Providers. DBT Cloud is designed to further simplify the use of DBT, allowing users to focus on building powerful, reliable data pipelines without having to worry about underlying infrastructure or resource management.

Partnership

Our DBT consultancy offers comprehensive, personalized support to help organizations maximize the value of their implementation.

We have several projects under our belt in which DBT is used in a production environment for the management of complex data models according to the Data Vault paradigm and with a Data Product centric approach.

Our consultancy is not limited to the initial implementation phase, but continues over time to ensure that DBT-based solutions are always aligned with the evolving needs of the organization. With a practical and results-oriented approach, we are committed to providing tangible value and ensuring the long-term success of our clients.

Use Cases

Data Infrastructure

DBT