dbt with Databricks Data Pipeline

 

1. The Databricks Lakehouse Platform (The Engine)

This is the "Data Plane" and "Compute Plane" at the bottom left.

• Data Plane: Raw data lands as Delta Tables. The key internal here is the ACID Transaction Log (_delta_log), which ensures that even if a dbt run fails halfway through, your data stays consistent.

• Intelligence Plane (Unity Catalog): This is the "brain" that manages the Metastore, security, and audit logs. It allows dbt to know exactly where the tables live and who has permission to change them.

• Compute Plane: When you execute a dbt command, it triggers Photon Engines or SQL Warehouses. Databricks does the actual "crunching" of the numbers; dbt just sends the instructions.

2. The dbt Core/Cloud Project (The Logic)

This is the "Logical Plane" at the top, representing your code repository (Git).

• profiles.yml: The "handshake" file that tells dbt how to log into Databricks using a Personal Access Token (PAT).

• Models (Staging & Marts): Your SQL files. Staging models handle the "Silver" cleaning (renaming, casting), while Marts models create the "Gold" business-ready data.

• Snapshots & Macros: Internals that handle Slowly Changing Dimensions (SCD Type 2) to track history and reusable code snippets to keep the project "DRY" (Don't Repeat Yourself).

• dbt Docs: Automatically generated documentation that shows the Lineage (the map of how data flows from A to Z).

3. The Transformation Lifecycle (The Refinery)

The center of the image shows the flow from raw to refined:

• Bronze (Raw): Untouched data.

• Silver (Filtered): dbt applies filters, deduplication, and standardized naming conventions.

• Gold (Standardized): The final, high-quality data. Here, dbt Tests (unique, not_null) act as "quality gates" to ensure no bad data reaches the end users.

4. Downstream Sharing (The Value)

The right side shows how different teams "check out" the finished product from the Gold layer:

• BI Teams: Use SQL Warehouses to power dashboards.

• Data Science: Accesses the same tables for Feature Stores (ML inputs).

• AI/LLM Teams: Pulls cleaned text into Vector Search indexes for RAG (Retrieval-Augmented Generation).

• External Partners: Use Delta Sharing to access the data securely without needing to copy it to another system.

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Setting up dbt (data build tool) with Databricks is a powerful way to implement a "Medallion Architecture" (Bronze, Silver, Gold). Since Databricks handles the compute and dbt handles the transformation logic via SQL, you get a highly scalable, version-controlled data pipeline.

1. Establishing the Connection

Before writing SQL, you need to bridge the two platforms.

• Databricks Side: Go to SQL Warehouses, select your warehouse, and navigate to Connection Details. You will need the Server Hostname, HTTP Path, and a Personal Access Token (PAT).

• dbt Side: If using dbt Cloud, use the Databricks adapter. If using dbt Core, install dbt-databricks.

• Profile Configuration: Your profiles.yml (or Cloud setup) should look like this:

• Type: databricks

• Method: token

• Catalog: main (Unity Catalog is highly recommended for sharing).

2. Data Ingestion (The "Bronze" Layer)

dbt is primarily a transformation tool, not an ingestion tool. However, you have two main paths:

• External Ingestion: Use tools like Fivetran, Airbyte, or Azure Data Factory to land raw data into Delta tables.

• dbt-as-Ingestion: Use dbt Seeds for small, static CSVs (like mapping tables) or define Sources that point to tables created by Databricks Auto Loader.

3. Data Cleansing (The "Silver" Layer)

This is where dbt shines. In this layer, you transform raw data into "clean" entities.

• Filtering & Deduplication: Use SELECT DISTINCT or window functions to remove duplicates.

• Casting: Ensure dates are DATE types and strings are trimmed.

• Renaming: Use dbt models to change cryptic source column names (e.g., src_usr_id) to readable ones (user_id).

• Implementation: Create a model in models/silver/ and use the ref() function to pull from your Bronze sources.

4. Maintaining Data Standardization

To keep your data "Golden," you must enforce quality and consistency across the organization.

• dbt Tests: Define unique, not_null, and accepted_values in your schema.yml.

• Macros: Use dbt macros for reusable logic. For example, if you have a specific way to calculate "Fiscal Year," write a macro once and call it in every model to ensure everyone uses the same math.

• Packages: Utilize dbt_utils for complex cross-database testing and transformations.

• Documentation: Run dbt docs generate to create a searchable data catalog so users know exactly what a column represents.

5. Data Sharing to Downstream Teams

Once your "Gold" layer is ready, you need to deliver it. This is where Unity Catalog on Databricks becomes your best friend.

For Analytics & BI

• Materialization: Set your Gold models to materialized='table' or incremental.

• BI Tools: Tools like Tableau, Power BI, or Databricks SQL Dashboards connect directly to these Gold tables.

For Data Science & ML

• Feature Store: DS teams can use your dbt-cleansed tables as the foundation for the Databricks Feature Store.

• Python Integration: ML engineers can pull dbt models directly into notebooks using: spark.table("main.gold.customer_lifetime_value")

For AI & LLMs (RAG)

• Vector Search: You can sync your dbt-processed text data into Databricks Vector Search indexes to power AI chatbots with clean, "truthful" organizational data.

External Sharing

• Delta Sharing: Use Databricks Delta Sharing to provide read-only access to your dbt models for partners or vendors outside your Databricks environment without moving the data.