End-to-End Data Engineering with Azure, Databricks and dbt: A Deep Dive

This project demonstrates a complete data engineering pipeline leveraging Azure Databricks, Data Build Tool (DBT), and Azure cloud services. The project adheres to a Medalion Architecture, a data management paradigm that promotes a structured and iterative approach to data processing, ensuring data quality and reliability throughout the lifecycle.

1. Data Ingestion (Bronze Layer)

• Objective: Extract raw data from the source system and land it in a raw, unprocessed format.
• Source: Azure SQL Database (sqldb-adventureworks-dev) containing Adventure Works data (sale transactions, customers, products, address, ...).
• Destination: ADLS Gen2 bronze container.
• Process:
  1. Data Factory Orchestration:
     • Create an Azure Data Factory pipeline to automate the data extraction process.
     • Utilize a Lookup activity to dynamically retrieve a list of all tables within the specified schema (e.g., SalesLT) in the SQL Database.
     • Employ a For Each loop to iterate through each table identified by the Lookup activity.
     • Within the loop, execute a Copy Data activity to extract data from each SQL table and transfer it to the bronze container in ADLS Gen2.
     • Store data in Parquet format for efficient data processing and storage.
  2. Databricks Integration:
     • Integrate Databricks with the Data Factory pipeline to perform initial data validation and preparation.
     • Create a Databricks notebook that executes after the Copy Data activity within the Data Factory pipeline.
     • Within the notebook:
       • Create a dedicated database in Databricks to organize ingested data.
       • Create external tables in Databricks pointing to the Parquet files located in the bronze container. This enables querying and basic data exploration within the Databricks environment.

2. Data Transformation (Silver Layer)

• Objective: Transform raw data from the bronze layer into a more structured and consistent format suitable for analysis.
• Source: Parquet files in the ADLS Gen2 bronze container.
• Destination: ADLS Gen2 silver container.
• Process:
  1. DBT for Data Transformation:
     • Utilize DBT to manage and orchestrate data transformations.
     • Create snapshot files within the dbt/snapshots directory for each dimension table.
       • Define snapshot configurations, including:
         • File format (Delta Lake for efficient versioning and time travel)
         • Location within the silver container
         • Unique key for identifying records
         • Invalidation strategy (e.g., check to compare with the source)
     • Create a bronze.yml file to define source tables within the DBT project, referencing the external tables created in Databricks.
     • Execute dbt snapshot to create snapshot tables in the silver container. This process captures the current state of the data and enables tracking changes over time (SCD type 2).

3. Data Mart Creation (Gold Layer)

• Objective: Create refined, business-oriented data marts for analytical purposes.
• Source: Transformed data from the silver layer.
• Destination: ADLS Gen2 gold container.
• Process:
  1. DBT for Data Mart Creation:
     • Create directories within the models folder to organize data marts (e.g., models/marts/customer, models/marts/product).
     • Define DBT models using YAML files (e.g., dim_customer.yml) to:
       • Specify the target table (e.g., dim_customer)
       • Configure table properties (e.g., file format, location, partitioning)
       • Define data quality checks (e.g., constraints, checks)
     • Create SQL files (e.g., dim_customer.sql) to implement data transformations:
       • Join tables from the silver layer.
       • Apply business logic (e.g., calculations, aggregations).
       • Handle data inconsistencies or missing values.
     • Execute dbt run to create the specified data marts in the gold container.

4. Data Quality & Testing

• DBT Tests:
  • Leverage DBT's testing framework to perform data quality checks:
    • Unique key constraints
    • Not null constraints
    • Check constraints for specific business rules
    • Data type validations
    • Referential integrity checks
  • Execute dbt test to run the defined tests and identify any data quality issues.
• Documentation:
  • Generate and serve interactive data documentation using dbt docs generate and dbt docs serve. This provides valuable insights into the data model, lineage, and data quality.

Project Setup

• Prerequisites:
  • Active Azure subscription
  • Databricks CLI, DBT CLI
• Azure Resources:
  • Create a Resource Group, ADLS Gen2 storage account, Data Factory, Key Vault, and SQL Database.
• Databricks Setup:
  • Create a Databricks workspace, configure secret scope, and mount ADLS Gen2 containers.
• DBT Setup:
  • Initialize a DBT project, configure DBT with Databricks workspace details.

Key Considerations:**

• Data Lineage: Implement data lineage tracking to understand the origin and transformations of data throughout the pipeline.
• Data Profiling: Conduct regular data profiling to identify data quality issues, understand data distributions, and inform data modeling decisions.
• Scalability and Performance: Optimize data pipelines and transformations for performance and scalability to handle growing data volumes.
• Security: Implement robust security measures to protect sensitive data throughout the pipeline.