Data Migration & Transformation Using Databricks
Intuz Development & Consulting
- Requirement Analysis & Planning
- Environment & Infrastructure Setup
- Data Pipeline Development
- Machine Learning & AI Integration
- Performance Optimization
- Deployment & Integration
- Security & Compliance
- Monitoring & Scaling
About the Project
Our client, a leading US based digital marketing company, relied heavily on data analytics to optimize advertising campaigns and customer engagement. They wanted to understand the customer behaviour, what has engaged users the most, and what has made them to convert.
The project involved data migration from one PostgreSQL database instance to another PostgreSQL instance, ensuring smooth and efficient batch and incremental data migration. The goal was to transfer data accurately while applying necessary transformations and normalization for analytical and data science purposes.
Challenges Addressed
• Migrating large volumes of historical data efficiently.
• Ensuring real-time incremental data updates using CDC tools.
• Defining and implementing complex data transformations.
• Normalization of data for data scientists.
• Storing transformed data for analytics and reporting.
Databricks makes it easy for businesses to build and scale data-driven solutions. With a powerful platform for data engineering, AI, and real-time analytics, it helps teams collaborate faster and turn raw data into valuable insights effortlessly.
System Architecture Overview
Data Mapping and Transformation Definition
A mapping file was created between the source and destination database. This mapping file served as a blueprint for:
• Column name changes to maintain consistency.
• Datatype conversions to match destination schema requirements.
• Data formatting rules, ensuring correct representations (e.g., decimal precision, datetime formats, etc.).
• Normalization of data to support machine learning and data science applications.
• Additional custom transformations as per business and analytical needs.
Historical Data Migration
• Databricks pipelines were developed to execute data migration scripts.
• The mapping file was utilized to apply pre-defined transformations.
• The transformed data was loaded into the destination PostgreSQL instance.
Incremental Data Migration
• Change Data Capture (CDC) mechanism was implemented using a Pub/Sub-like service in AWS.
• The CDC tool detected real-time changes and streamed them into Databricks pipelines.
• Databricks pipelines applied necessary transformations using the mapping file.
• The final transformed data was stored in the destination database for further processing.
Data Utilization
• The migrated and transformed data was leveraged for analytical dashboards and reports.
• Business stakeholders could make data-driven decisions based on the insights.
• Data scientists accessed the normalized data for building AI/ML models.
Business Impact
• Seamless migration with minimal downtime.
• Improved data quality and consistency through structured transformations.
• Real-time insights from incremental data loads.
• Enhanced decision-making powered by accurate data analytics.
• Scalability to handle future data expansion and business needs
Technical Specifications
Databricks
AWS
Explore More Work
We changed the way they do business, and they have no complaints
Marengo Load Board
Logistic Aggregator Mobile and Web App connecting Shippers, Carriers and Truck Drivers Targeting African Region
Bubba Booking
Intuitive Travel Application for Booking Last-Minute Tours & Adventure Deals
Let’s Talk
Let us know if there’s an opportunity for us to build something awesome together.