Passenger Data Processing Pipeline using Databricks Genie
We leveraged Databricks Genie to accelerate the development of an end-to-end data engineering pipeline for processing passenger-related operational data received from the business team.
The business uploads multiple passenger data files to SharePoint, including:
Watchlist Passenger
Stranded Passenger
High Risk Stations
Security on Arrival
INADS
Databricks Genie significantly simplified the pipeline development by assisting with the creation of data ingestion, transformation, and reporting workflows through natural language-driven code generation and automation. Instead of manually building every component, Genie helped generate Spark-based code, SQL queries, and data transformation logic, reducing development effort and improving productivity.
The pipeline created with Genie performs the following activities:
Data Ingestion
Connects securely to SharePoint.
Downloads the latest passenger files automatically.
Stores the raw files in Azure Data Lake Storage (ADLS Gen2) as the landing zone.
Data Transformation
Uses Apache Spark to clean, validate, and standardize the incoming datasets.
Handles data quality checks, schema alignment, and business rule implementation.
Integrates multiple passenger datasets to create a consistent analytical model.
Data Processing and Aggregation
Processes large volumes of passenger records efficiently using Spark's distributed computing capabilities.
Generates business-specific aggregated datasets required for reporting and operational analysis.
Stores the processed data in curated layers within ADLS Gen2.
Data Serving
Creates structured tables in the Databricks SQL Warehouse from the curated datasets.
Optimizes the data model for fast and efficient analytical queries.
Business Reporting
Connects Power BI to the Databricks SQL Warehouse.
Builds interactive dashboards and reports that provide insights into passenger watchlists, stranded passengers, high-risk stations, security-on-arrival cases, and INADS data.Overall, Genie enabled faster implementation of a scalable and maintainable end-to-end data engineering solution, allowing the team to focus more on business logic and analytics rather than repetitive coding tasks.
