A Databricks-Powered Data Migration and Transformation Solution for a US Digital Marketing Client
A US-based digital marketing client needed better visibility into customer behaviour and conversions – but their data was fragmented and not ready for analysis. Intuz migrated and transformed their data between PostgreSQL databases using Databricks – handling historical migration, real-time incremental updates, and complex transformations to produce clean, structured data ready for dashboards and AI/ML models.
WHAT WE BUILT
From Fragmented Data to a Clean,
Structured Migration Built on Databricks
Data Mapping and Transformation
We created a mapping file that linked the source and destination databases – defining column name updates, data type changes, data formatting rules, normalization steps, and custom transformations. This mapping file became the foundation for every migration step that followed, ensuring the data that arrived in the destination was consistent, accurate, and ready for analysis.
Historical Data Migration
We developed Databricks pipelines to execute the historical data migration scripts. The mapping file was applied at each step to enforce pre-defined transformations, and the processed data was loaded into the destination PostgreSQL instance – handling large volumes of historical data accurately.
Incremental Data Migration via CDC
We built a Change Data Capture system on AWS to capture real-time data changes and stream them into Databricks pipelines. The CDC system handles insert, update, and delete operations – applying the same mapping-based transformations before storing the final processed output in the destination database.
Data Utilization for Dashboards and AI/ML
Once the data was migrated and structured, we built dashboards and reports that gave business teams actionable visibility. The clean, organised data also served as the foundation for the client’s data scientists to build and improve their AI and ML models – turning the migration output into a live analytical asset.
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THE SOLUTION
What We Delivered Across Migration,
Analytics, and Real-Time Data Infrastructure
Campaign and Advertisement Analytics
We built dashboards covering campaign performance, advertisement management, account tracking, and strategy monitoring. The analytics layer was built directly on the migrated and transformed data – giving the client’s teams a structured view of campaign metrics and ad performance across channels.
Dashboard Reporting and AI/ML Model Support
We built dashboards and reports from the migrated and transformed data to give business teams actionable visibility. The clean, structured data also served as the input for the client’s data scientists to build and improve their AI and ML models – replacing fragmented raw sources with reliable, organised data.
Change Data Capture System on AWS
We built the CDC system on AWS to handle ongoing data changes after the initial historical migration was complete. Regular and real-time data changes – inserts, updates, and deletes – are captured and streamed into Databricks pipelines where they are transformed and loaded into the destination database, keeping both systems in sync on an ongoing basis.
Ad Spend and Revenue Tracking
We built spend tracking across ad campaigns – covering daily and total spending alongside remaining budget per campaign. Revenue and profit reporting was structured by platform – giving the client a consolidated view of their marketing spend and returns.
Performance Optimization
We optimised the data pipelines and infrastructure as part of the delivery. The solution was built to handle the client’s data volumes efficiently across both the migration layer and the analytical dashboards built on top.
Security and Compliance
We built security and compliance measures into the migration and pipeline infrastructure. Data handling across the full migration process was designed to meet the necessary security requirements for a client working with customer behaviour and conversion data.
Monitoring and Scaling
We built monitoring and scaling into the solution from the start. The system is designed to support future data growth – handling increasing data volumes and evolving business requirements as the client scales.
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TOOLS & TECHNOLOGIES
The Tools and Technologies Behind DMDigital
We used Databricks and AWS to build a scalable, reliable data migration and transformation pipeline – with PostgreSQL on AWS RDS as the destination database for clean, structured data.
