What is Data Build Tool (dbt)?

dbt, or Data Build Tool, revolutionizes data transformation by simplifying the process through SQL select statements. dbt empowers data teams to leverage software engineering principles for transforming data. With dbt, these statements effortlessly translate into tables and views, empowering users to seamlessly shape their data. Acting as the transformative force in ELT (Extract, Load, Transform), dbt focuses on optimizing and refining data already loaded into the database, making it a crucial component in the data pipeline. It's important to note that while dbt excels in transformation, it does not handle the Extract and Load processes.

dbt stands out as a contemporary addition to the data stack toolkit, facilitating data analysis and the discovery of fresh insights while enhancing operational efficiency. Its primary role involves coding, compiling to SQL, and executing operations directly on your data warehouse. Notably, dbt conducts calculations at the database level rather than in memory, resulting in accelerated transformation processes, heightened security, and simplified maintenance.

How dbt work?

1. Version Control & CI/CD: Deploy safely using dev environments. Git-enabled version control enables collaboration and a return to previous states.
2. Test & Document: Test every model before production, and share dynamically generated documentation with all data stakeholders.
3. Develop: Write modular data transformations in .sql or .py files - dbt handles the chore of dependency management.

Analytics Engineer: Owns the transformation of Raw data up to the BI layer.

The modern data team consists of:

1. Data Engineer
2. Analytics Engineer
3. Data Analysts

ETL vs ELT

1. The ETL process involves extracting data from various sources, transforming it either locally or on a third-party machine, and ultimately loading the transformed data into a data warehouse, thereby creating new database objects.
2. ELT represents a modern approach to creating database objects, wherein raw data is initially extracted and loaded into a data warehouse, followed by direct transformation within the warehouse to produce the desired outcomes.
3. Cloud-based data warehouse technologies have facilitated the evolution of the ELT process, enabling seamless extraction, loading, and transformation of data within the warehouse itself.

dbt, data platforms, and version control

There are effectively two ways in which to use dbt: dbt CLI and dbt Cloud.

• dbt Cloud is a hosted version that streamlines development with an online Integrated Development Environment (IDE) and an interface to run dbt on a schedule.
• dbt Core is a command line tool that can be run locally.

Data Platforms: dbt specializes in managing the transformation aspect of the data platform's 'extract-load-transform' framework. It establishes a connection with the data platform and executes SQL code within the warehouse to perform data transformations.

Modeling: Shaping of the data from raw data through to your final transformed data.

Models in dbt: models are SQL select statements, each model has a one-to-one relationship with a table or view in the data warehouse.

Sources: represents the raw data that is loaded into the data warehouse.

Testing: Used in software engineering to make sure that the code does what we expect it to.

run tests in the development environment while you coding, and run tests in the production environment with alerts.

01. Singular Tests - Specific queries that you run against your models. These are run on the entire model.

02. Generic Tests - Written in YAML and return the number of records that do not meet your assertions. These are run on specific columns in a model.

• unique
• not_null
• accepted_values
• relationships

Documentation: Effective documentation plays a crucial role in optimizing the productivity and efficiency of an analytics team. Robust documentation empowers team members to address data-related inquiries independently and facilitates smooth onboarding processes for new team members. Documenting your project happens while you build your models - not in separate spaces.

References:

1. https://courses.getdbt.com/collections
2. https://www.udemy.com/course/dbt-data-build-tool/?couponCode=LETSLEARNNOW