dbt - Data Build Tool | Transformation and control from data warehouse

Unlock the full potential of your data with dbt

Modern businesses generate large amounts of information every day. But without proper transformation and organization, raw data remains an untapped asset. dbt (Data Build Tool) empowers organizations to transform data efficiently, ensuring that business can make informed decisions and utilize AI (Artificial Intelligence) based on accurate, clean, and well-structured data.

dbt is an open-source based analytics engineering tool that helps data teams transform and manage data in their warehouses. It allows users to write, test, and document SQL-based transformations in a modular and scalable way.

Utilizing dbt with modern cloud data warehouses

dbt leverages modern cloud data warehouses (Snowflake, BigQuery, Fabric, Redshift, Databricks) to process data efficiently with modular, scalable and version-controlled data transformations. It enhances collaboration, data quality, and analytics efficiency, making it a core tool for modern data teams.

Reliable and unified data for BI tools

Different BI tools (Power BI, Looker, Qlik Sense, Tableau) work best when they are provided with well-structured and transformed data. In order to keep BI dashboards fast and reliable, dbt is used to ensure that the data is modeled correctly. Built-in testing and validation ensures data accuracy and consistency, while minimizing risk of manual errors and costly mistakes in business reporting. It is important that different business units and teams rely on the same definitions and calculations – dbt ensures this by promoting a single source of truth.

Key Features of dbt:

Cloud-native approach: Supports Snowflake, BigQuery, Redshift, Databricks, and other SQL-based data warehouses.
Extensibility & Scalability: Can integrate with multiple orchestration and data quality tools ensuring that your data infrastructure is agile, and ready for AI-driven analytics.
Modern ELT Workflows: Unlike traditional ETL tools, dbt uses SQL for transformations inside the warehouse leveraging the scalable performance of the modern cloud data warehouse.
SQL-based Transformations: dbt enables users to write transformations using SQL and automates the execution of these transformations.
Developer Friendly: Works well with Git, allowing collaboration and tracking of changes. Encourages modular and reusable code, making development more efficient. No proprietary tool or UI knowledge required, just SQL.
Automated Testing & Documentation: Provides built-in testing for data quality. Generates interactive documentation and lineage, visualizing the dependency tree of transformations.
Available in different versions: dbt Core that is open-source, CLI-based and self-managed, and dbt Cloud which is managed service with UI, scheduling and collaboration features.
Designed for batch processing: works well in large-scale environments with even thousands of tables.

dbt in one picture

Future proof data platform architecture

Data control plane

Data users in different organisations often face challenges in their data quality and ability to build trustworthy data products. Lack of data literacy, like semantics, catalogs and other metadata, make utilization and governance of your data assets challenging.

Data control plane is a cross-platform solution that promotes collaboration between different teams by centralising metadata across your business. This gives you an universal view of your data assets regardless of what platforms you utilise. This makes it possible to ensure that all the stakeholders have a common understanding of how business metrics are defined. With an integrated data control plane of the dbt Cloud you can govern and document your data on the go, while you build data pipelines and transformations for the business.

De facto standard for data transformations

As a widely adopted open source tool at its core, dbt has quickly become a de facto standard for data transformations. It is not just a tool or a technology. dbt is a framework for the analytics workflow: Have everyone working on the same page, same style. The community is large and the amount of open knowledge out there is extensive.

By using dbt, companies across industries have:

Reduced data processing costs
Improved reporting accuracy, eliminating data discrepancies.
Successfully scaled Data Engineer teams when required know-how is widely available
Accelerated time-to-insight from weeks to hours, driving faster decision-making.

Getting started with dbt

In the digital economy, data is your most valuable asset. Without a streamlined way to transform and manage data, companies risk inefficiencies, poor decisions, and lost revenue. dbt provides a powerful yet cost-effective solution that enhances data reliability, speeds up insights, and enables smarter business decisions.

Beginners

Start with defining a use case for available data. This means that data sources, data warehouse and integration tools will be selected and the case will be driven by providing the business with refined insights into the data.

Experienced data users

You already have some type of data platform in use and want to modernize the existing solution, migrate to a different platform or get more people involved with your data. Transformation to dbt enables you to manage your growing data ecosystem efficiently.

Advanced data users

You want scalability and ML/AI powered solutions into your repertoire. This means that processes and workflows behind the data need to be robust and flexible. Integrations to different orchestration, automation and Artificial Intelligence (like LLMs) tools are crucial.

Stay ahead of the game

Ready to unlock the full potential of your business data? As experienced tech agnostics with a strong emphasis on delivering business value, at Recordly we have some of the Finland's leading dbt experts in our team. Learn today how dbt can help you accelerate growth, reduce costs, and improve data quality.

Practical guide: migrating to dbt

Modern data teams need scalable, cost-efficient tools to manage data transformations effectively.

Our Practical Guide: Migrating to dbt walks you through the key drivers for migration, a comparison of dbt vs. legacy tools, and a step-by-step process to ensure a seamless transition.

Learn best practices, explore real-world case studies, and discover how dbt can enhance collaboration, automation, and data reliability.

Ready to modernize your data workflows? Download the guide and start your migration today!