Data Migration & Transformation Using Databricks

Seamlessly migrated and transform data with Databricks, ensuring scalability, efficiency, and real-time analytics. Discover how our solution optimized ETL workflows, enhances data quality, and accelerates business insights.

Our US-based digital marketing client needed better insights into customer behavior and conversions. We migrated their data from one PostgreSQL database to another with accurate batch and incremental transfers. The work included handling large historical data, real-time updates, complex transformations, normalization, and storing clean data for analytics and reporting

Intuz Development & Consulting

Requirement Analysis & Planning

Environment & Infrastructure Setup

Data Pipeline Development

Machine Learning & AI Integration

Performance Optimization

Deployment & Integration

Security & Compliance

Monitoring & Scaling

Data Mapping and Transformation Definition

We created a mapping file linking the source and destination databases. It outlined column name updates, datatype changes, data formatting rules, and normalization steps. It also included custom transformations to ensure the migrated data was consistent, accurate, and ready for analysis.

Historical Data Migration

Databricks pipelines were developed to execute data migration scripts. The mapping file was utilized to apply pre-defined transformation and the transformed data was loaded into the destination PostgreSQL instance.

Data Mapping and Transformation Definition

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.