Data Build Tool, commonly known as dbt, has gained recently significant popularity in the realm of data pipelines and Intrigued by its popularity. The Data Build Tool (DBT) is an open-source command-line tool for transforming and managing data within a data warehouse. dbt is primarily focused on the transformation layer of the data pipeline and is not involved in the extraction and loading of data. DBT is commonly used in conjunction with SQL-based databases and data warehouses such as Snowflake, Big Query, and Redshift. In this we can build reusable SQL code and define dependencies between different transformations. This approach greatly enhances data consistency, maintainability, and scalability, making it an asset in the data engineering toolkit.
Advantages of DBT: –
- Open Source: It is freely available and accessible to DBT Core users.
- Modularity: DBT promotes modular and reusable code, enabling the creation of consistent, maintainable, and scalable data transformations.
- Incremental Builds: It supports incremental data builds, allowing you to process only the data that has changed, reducing processing time and resource usage.
- Version Control: DBT can be integrated with version control systems like Git, facilitating collaboration and version tracking for your data transformation code.
- Testing Framework: DBT provides a robust testing framework to verify the quality of your data transformations, catching issues early in the pipeline.
Components of DBT: –
We have two components in DBT. They are:
- DBT Core
- DBT Cloud
DBT Core:
With DBT Core, you can define and manage data transformations using SQL-based models, run tests to ensure data quality, and document your work. It operates through a command-line interface (CLI), making it easy for data professionals to develop, test, and deploy data transformations while following industry-standard practices. DBT Core is widely used in the data engineering and analytics community to streamline data transformation processes and ensure data consistency and reliability.
Key Features of DBT Core:
- SQL Based Transformation
- Incremental Builds
- Tests and Documentation
DBT Cloud:
DBT Cloud is a cloud-based platform that offers a speedy and dependable method for deploying code. It features a unified web-based user interface for scheduling tasks and investigating data models.
The DBT Cloud application comprises two types of components: static and dynamic. Static components are consistently operational, ensuring the availability of critical DBT Cloud functions like the DBT Cloud web application. In contrast, dynamic components are generated on-the-fly to manage tasks such as background jobs or handling requests to use the integrated development environment (IDE).
Key Features of DBT Cloud: –
- Scheduling Automation
- Monitoring
- Alerting
Differences Between DBT Core and DBT Cloud: –
| Feature | DBT Core | DBT Cloud |
| Deployment Environment | DBT Core is typically used in a local development environment. You install and run it on your local machine or on your organization’s infrastructure. | DBT Cloud is a cloud-based platform and is hosted in the cloud. It provides a managed environment for running DBT projects without the need to manage infrastructure. |
| Scheduling | DBT Core does not natively provide scheduling capabilities. You would need to use external tools or scripts to schedule DBT runs if needed. | DBT Cloud includes built-in scheduling features, allowing you to automate the execution of DBT models and transformations on a defined schedule. |
| Monitoring and Alerts | DBT Core may require third-party tools for monitoring and alerting on data transformation issues. | DBT Cloud includes monitoring tools and alerts to notify you of problems in your data transformation pipelines. |
| Security and Compliance | Security features in DBT Core depend on how it is configured and secured within your own infrastructure. | DBT Cloud provides security features to protect your data and ensure compliance with data privacy regulations. |
| Scalability | DBT Core can be used for both small-scale and large-scale data transformation tasks, but you need to manage the scaling yourself. | DBT Cloud is designed to scale easily, making it well-suited for larger teams and more complex data operations. |
| Orchestration | DBT Core does not include built-in orchestration capabilities. You need to manage the execution order of models and transformations manually | DBT Cloud provides orchestration features to define and automate the sequence of data transformations, ensuring they run in the correct order. |
Built-in Folders in DBT: –
- analyses
- dbt_packages
- logs
- macros
- models
- seeds
- snapshots
- target
- tests
- dbt_project.yml
dbt_project.yml: –
The dbt_project.yml file is a central configuration file that defines various settings and properties for a DBT project. This file is located in the root directory of your DBT project and is used to customize how DBT runs, connects to your data warehouse, and organizes your project.
This file is crucial for configuring your DBT project and defining how it interacts with your data sources and the target data warehouse. It helps ensure that your data transformation processes are well-organized, maintainable, and can be easily integrated into your data pipeline.
Analyses: –
Analyses refer to SQL scripts or queries that are used for ad-hoc analysis, data exploration, or documentation purposes. Analyses are a way to write SQL code in a structured and version-controlled manner, making it easier to collaborate with other team members and ensuring that your SQL code is managed alongside your data transformations. Analyses help you organize and document your SQL code for data exploration, reporting, and quality validation within your DBT project.
dbt_packages.yml: –
The dbt_packages.yml file is a configuration file used to specify external packages that you want to include in your DBT project. These packages can be thought of as collections of DBT code and assets that are developed and maintained separately from your project but can be easily integrated. It is used to help manage and share reusable DBT code, macros, models, and other assets across different DBT projects.
When we want to use more packages in our project, we need to create a new file with the name of packages.yml in our project and mention the package name and version like below. When you run dbt deps, DBT will resolve and fetch the specified packages (including their models, macros, and other assets) and integrate them into your project. This allows you to reuse and share code and best practices across different DBT projects, making it easier to collaborate and maintain consistency.
Logs: –
These logs provide detailed information about the tasks performed, including data transformations, tests, and analysis runs, making them essential for monitoring and troubleshooting your DBT projects.
Overall, logs in DBT play a crucial role in helping you monitor the health and performance of your data transformation processes and in diagnosing and troubleshooting issues that may arise during the development and execution of your DBT projects.
Macros: –
Macros are reusable pieces of SQL code that you can use to perform various tasks, such as custom transformations, calculations, and data validation. These are analogous to “functions” in other programming languages and are extremely useful if you find yourself repeating code across multiple models. Macros are defined in .sql files, typically in your macro’s directory.
Benefits and key components of Macros: –
- Code Reusability
- Maintainability
- Code consistency and modularity
- Abstraction
Models: –
Models are the basic building block of our business logic. In models we can create tables and views to transform the raw data into structured data by writing SQL Queries. It promotes Code modularity and reusability of the SQL Code. It follows dependency management. i.e. To execute the models. It supports incremental data builds and updates only updated data.
Materialization: –
There are four built-in materializations too, how your models can be stored and managed in Datawarehouse. They are:
- Views
- Tables
- Incremental
- Ephemeral
View Materialization: –
When using the view materialization, your model is rebuilt as a view on each run.
- Pros: No additional data is stored, views on top of source data will always have the latest records in them.
- Cons: Views that perform a significant transformation, or are stacked on top of other views, are slow to query.
Table materialization: –
When using the table materialization, your model is rebuilt as a table on each run.
- Pros: Tables are fast to query.
- Cons: New records of underlying source data are not automatically added to the table.
Incremental Materialization: –
Models allow dbt to insert or update records into a table since the last time that dbt was run.
- Pros: You can significantly reduce the build time by just transforming new records.
- Cons: Incremental models require extra configuration and are an advanced usage of dbt.
Ephemeral: –
It is Very much virtual materialization. Ephemeral models are not directly built into the Datawarehouse. It hides the view/table when we use dbt run command.
- Pros: Can help your Datawarehouse by reducing clutter.
- Cons: For the first time you need to drop view/table manually from the snowflake.
Seeds: –
Seeds are CSV files in your dbt project (typically in seeds directory). Seeds are local files that you load into your Datawarehouse using dbt seed. Seeds can be referenced in downstream models the same way as referencing models – by using ref function. Seeds are best suited to static data which changes infrequently.
Command to Upload csv file in seed:
- curl file:/// path of file -o seeds/filename.csv
Note: csv files must and should have header or else the first record it considers as header.
Sources: –
Sources is an abstract layer on the top of your input tables (raw tables) and the data is more structured. Sources make it possible to name and describe the data loaded into your warehouse by your Extract and Load tools.
Note: select from source tables in your models using the {{source() }} function, helping define the lineage of your data.
Snapshots: –
In dbt (Data Build Tool), “snapshots” are a powerful feature used to capture historical versions of your data in a data warehouse They are particularly useful when you need to track changes to your data over time, such as historical records of customer information, product prices. It implements the scd type 2.
Advantages of Snapshots: –
1.See the Past: Imagine you have data, like prices or customer info. Snapshots let you look back in time and see how that data looked on a specific date. It’s like looking at a history book for your data.
2.Spot Changes: You can easily spot when something changes in your data. For example, you can see when a product’s price went up or when a customer’s address was updated.
3.Fix Mistakes: If there’s a problem with your data, you can use snapshots to figure out when the problem started and how to fix it.
4.Stay Compliant: For some businesses, keeping old data is a legal requirement. Snapshots done this.
Strategies of Snapshots: –
- Timestamp
- Check
Timestamp: –
The timestamp strategy uses an updated_at field to determine if a row has changed. If the configured updated_at column for a row is more recent than the last time the snapshot ran, then dbt will invalidate the old record and record the new one. If the timestamps are unchanged, then dbt will not take any action.
Check: –
The check strategy is useful for tables which do not have a reliable updated_at column. This strategy works by comparing a list of columns between their current and historical values. If any of these columns have changed, then dbt will invalidate the old record and record the new one. If the column values are identical, then dbt will not take any action.
Disadvantages of Snapshots: –
- Storage cost become significant
- Performance Impact
- Limit the transformation amount
Tests: –
Tests are a critical component of ensuring the quality, correctness, and reliability of data transformations and play a crucial role in catching data issues and discrepancies. DBT allows you to define and run tests that validate the output of your SQL transformations against expected conditions.
Benefits of Tests: –
- Data Quality
- Collaboration and Maintenance
- Documentation
Types of Tests: –
- Singular Tests
- Generic Tests
Singular Tests: –
- Singular tests are very focused, written as typical SQL statements, and stored in SQL files typically in tests directory.
- We can use Jinja templates (ref, source) in tests and it returns failed records.
- In Singular Tests we must go through negative testing.
Generic Tests: –
Generic tests are written and stored in YML files, with parameterized queries that can be used across different dbt models. And they can be used over again and again. The main Components of generic tests:
- Unique
- Not null
- Relationship
- Accepted Values
Target: –
The “Target” folder is a directory where DBT stores the compiled SQL code and materialized views (tables or other objects) that result from running the DBT transformations. The exact structure and contents of the target directory may vary depending on your data warehouse and the DBT project’s configuration. The location of the target directory is typically specified in your dbt_project.yml configuration file using the target-path setting. By default, it’s located in the root directory of your DBT project.
Hooks in DBT: –
There are some repeatable actions that we want to take either at start or end of our run or before and after at each step, so for this process Dbt introduces the hooks process. Hooks are snippets of SQL that are executed at different times.
Types of Hooks:
Pre-hooks:
Pre-hooks are executed before specific DBT commands or tasks. For example, you can define a pre-hook to run custom code before executing a DBT model.
Post-hooks:
Post-hooks are executed after specific DBT commands or tasks. You can use post-hooks to perform actions after a model is built or a DBT run is completed.
on-run-start Hook:
The on-run-start hook is executed at the beginning of a DBT run, before any models are processed.
on-run-end Hook:
The on-run-end hook is executed at the end of a DBT run, after all models have been processed, tests have been run, and other run tasks are completed.
All the above are the built in files and features in our DBT project to perform transformation of the data. After completing transformation, we can be able to generate the documentation. And this Documentation which contains explanations, and descriptions to your DBT project, models, columns, tests, and other components. It helps make your data transformation code more understandable, shareable, and self-documenting, making it easier for your team to work with your project.
Overall, the Documentation in DBT is a valuable feature for enhancing the maintainability and collaborative aspects of your data transformation projects. It ensures that the business logic and data meanings are well-documented and accessible to everyone involved in the project.