In this blog, covers every step of the process, from setting up the necessary tools to deploying the project to production.
A robust data transformation pipeline is crucial for analytics and reporting. As data volumes grow and data sources proliferate, building and maintaining ETL processes becomes increasingly complex. dbt (data build tool) is an open-source tool that enables analysts and engineers to more easily transform data and build scalable pipelines.
In this post, we’ll walk through how to build a robust dbt project from start to finish. We’ll use a common example of analyzing customer data from a mobile app. By the end, you’ll understand:
To follow along, you’ll need:
Once the prerequisites are met, we’re ready to start building!
We’ll build out an analytics model to understand mobile customer behavior. Our raw data comes from two sources:
With dbt, we can model this disparate data into an analytics schema that’s easy to understand.
First, we’ll create a customers model by selecting key attributes from the directory:
This materializes a customers table we can join to. Next we’ll build a page_views model to prepare the event data:
With these modular data models, we’ve now built a flexible analytics layer while abstracting away the underlying complexity!
Transforming Data at Scale
As data volume grows, care must be taken that transformations can scale. Here are some best practices for handling large datasets with dbt:
1. Materialize Where Possible - Materializing models pre-aggregates data which allows much faster queries. In dbt, configuring materialized='table' materializes the results.
2. Partition Tables - For extremely large tables, partitioning splits data into smaller pieces for more efficient querying. dbt natively supports table partitioning schemes.
3. Use Incremental Models - Incremental models only process new or updated records since they were last run. This saves compute resources.
4. Structure for Modularity - Break down models into reusable pieces. Single-purpose models can handle data at scale vs. a monolithic transformation.
By applying these patterns, we can develop dbt pipelines to handle ever-growing volumes of data.
With ongoing data transformations occurring, how do we ensure output quality? dbt allows configurable test criteria so models can be automatically validated.
Some examples of useful tests include:
Unique ID Validations - Ensure a model's primary key is unique and not null
Row Count Thresholds - Validate number of records meet expectations
Referential Integrity - Check consistency between related tables
By building test cases into models, data quality safeguards get automated at runtime. We prevent nasty surprises down the line!
Self-documenting models are invaluable as an analytics project evolves. dbt has powerful features that auto-generate documentation of models:
Include a markdown block detailing a model’s purpose:
This model creates a cleaned customer list for analytics, providing key attributes like location, lifetime value, and a unique customer ID.
Auto-generate a data dictionary defining all columns:
Unique ID for each customer generated from the mobile app
With these tools, understanding models is simplified for engineers and stakeholders alike.
Once we’ve built, tested, and documented our project locally, we're ready to deploy to production! Here's a reliable workflow:
And we're done! By following these steps, we can reliably deploy dbt projects as code. The pipeline will systematically transform data and make it available for analytics.
In this post we walked through architecting an end to end dbt project - from modeling schemas to testing data to deploying code. Key takeaways included:
Adopting these patterns leads to more scalable, reliable, and sustainable data transformation. With dbt's flexibility, you're empowered to build robust pipelines tailored to your needs!
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