How dbt (data build tool) can transform your data stack
If you’ve never heard of dbt before, you’re missing out on one of the most exciting developments in data technology. dbt offers a workflow that is compatible with SQL. This can help you organise your analytics code into modular chunks and keep your data team within the bounds of standard software engineering processes.
Over 15,000 companies have, as of this moment, incorporated dbt models into their data stack improvement efforts.
At re_data, we realised creating a central data stack could assist a larger community while gaining insights using dbt data models. So we created our all-in-one data monitoring package to help gather your data in one place. Now, you can join top analytics engineers who use dbt to convert data in Bigquery, Snowflake, and other top cloud data platforms to make informed corporate decisions.
The main goal of dbt (Data Build Tool) is to simplify and accelerate data transformation. Here, we describe how dbt can transform your data stack and help your organisation’s data curation process.
What is dbt?
dbt is a tool that transforms data. It combines modular SQL, software engineering practices, and the features of a developmental framework to correct data with speed and efficiency. dbt also helps automate data tasks, such as testing and validating your data and managing your data stacks. dbt helps to keep a record of all changes made while building data pipelines. This makes it easy to trace back data and fix the data stack; this is very useful because of the complexity of the organisation’s logic behind reporting data.
With dbt, you only need basic SQL skills to solve problems in your data stack. However, it has some complementary tools like apache airflow. The major function of dbt is to take your code, compile it to SQL, and then run it against your database. dbt supports multiple databases such as RedShift, BigQuery, Snowflake, Databricks and others.
dbt (data build tool) is written in python and is installed using the python package installer. It is open-source, which means it allows customisation. You can choose from the pool of great data packages already existing to extend the functionality of dbt (like re_data open-source) or create and upload your own package!
dbt has a command line interface, which runs transformations, and performs data tests. Additionally, it allows users to generate data documentation which is very useful for exploring your data.
How can the Data Build Tool (DBT) help my data stack?
Building and testing the data models are dbt’s two primary functions. It is flexible enough to hook up with any cutting-edge data stack and works seamlessly with numerous data warehouses and data lakes.
Data analysts can encourage a data-driven culture inside the company by taking control of the whole analytics engineering workflow using dbt. You can create data transformation code for deployment and documentation, which will facilitate the data stack in the following ways:
Provide clean, converted data that is available for analysis
dbt allows data analysts to write easy SQL select statements, which allows them to drive complicated data transformations without the requirement for boilerplate code or knowledge of other computer languages.
dbt can also create data models easily standardized for future use. A data model with different business-specific contextual layers that ensure optimal modularity ultimately yields an optimal value from data for specific business operations and processes.
Use good practices from software engineering
In transforming your data stack, dbt enables CI/CD software development practices.
It helps to perform modular coding, version control, and data validation while automating CI/CD to dimensions. This allows you to test and integrate all your code changes into production.
Additionally, the dbt cloud is integrated with GitHub. This means continuous integration can be automated, and there is no version to manage.
Data testing ensures dbt’s ability to adhere to dynamic software development methodologies. It provides full data integrity and checks for each specific data model. As a result, snapshot tables track and record changes to your data model whenever new changes are introduced by continuous integration.
Build reusable and modular code using dbt data modeling
dbt modular technique allows you to begin from the factor as much as others have contributed to the record modelling task. This component-based, fully modular technique saves analysts time and effort because they don’t have to start the modelling venture from scratch.
The dbt framework allows for the one-at-a-time sharing and reuse of additive changes made to the data models. This results in equally valuable improvements to the work of data scientists and analysts working on different projects.
dbt gives lively assistance in reusing the often repeated code, permitting the reuse of the SQL common sense in extraordinary layers of the code in a context-pushed manner.
Maintain data documentation and definitions within dbt while creating and developing extraction diagrams
dbt (data creation tool) automatically generates documentation for descriptions, model dependencies, model SQL, sources, and tests. It creates a lineage diagram of your data pipelines, providing transparency and visibility into what your data represents, how it was created, and how it maps to your business logic.
Automated testing is achieved
dbt is a great tool for automating tests of data models because of its component-based modular design, which eliminates the limitations and challenges of manual testing.
Schema and data tests are two different types of tests that dbt does. Additionally, it enables combining both types of testing for data quality assessments on various models. These tests can run continuously while the model is updated without relying on a different testing framework.
Conclusion
Among many other benefits of how dbt may transform your data stack, dbt enables unlimited freedom to alter data using straightforward SQL select statements, manages dependencies, provides native data test support, and has a simple learning curve.
At re_data, we have built a one-stop shop to observe your dbt data, set alerts, detect anomalies and track the root cause of your data issues in one place. Let us know if we need to include anything. We are very responsive on our Slack.
