Replacing a decade-old Oracle Enterprise Data Warehouse with a cloud-native Databricks platform built for scale.
A leading U.S. manufacturer of windows and doors relied on a decade-old Oracle Enterprise Data Warehouse and Informatica-based workflows to power its Market Share reporting. As the business scaled, interconnected pipelines required manual handling at multiple stages, slowing insight delivery and limiting the team's ability to support more complex analytics.
Systech used its DBShift™ accelerator to migrate the environment to a cloud-native Databricks platform, cutting end-to-end pipeline runtime from ~10 hours to ~5 minutes.
Addressing this called for a modern foundation built around five core needs:
Migrating off Oracle Enterprise Data Warehouse onto a Databricks-native platform built for scale.
Replacing legacy Informatica workflows and Java-based processing with maintainable PySpark scripts.
Eliminating repetitive file handling and manual tasks that slowed teams down.
Speeding up Market Share pipeline execution for timely, decision-ready insights.
Establishing a flexible cloud-native foundation to support future analytics needs.
While DBShift™ automated the majority of the migration, some data type mismatches between Oracle and Databricks tables required targeted manual adjustments to ensure accuracy in Market Share reporting.
Systech used DBShift™ to convert the legacy pipeline into a Databricks-native architecture, end to end.
Used DBShift™ to rapidly and accurately convert legacy Informatica workflows into Databricks pipelines, replacing Java-based staging with PySpark.
Shifted from a monolithic structure to a medallion-based approach for better scalability and data flow.
Leveraged Azure services to extract source data and load it directly into Azure Data Lake Storage (ADLS), eliminating manual file handling.
Databricks Jobs managed pipeline execution reliably across all pipeline stages.
Leveraged Databricks clusters to improve pipeline efficiency and overall performance.
Performed full and incremental load validations using an automated utility for end-to-end data comparison, with data type corrections applied.
Configured customer-specific naming conventions and coding standards within DBShift™ for consistent, maintainable pipelines.
Parameterized key elements to enable seamless deployment across environments and minimize rework.
Delivered standardized, deployment-ready code aligned with best practices, minimizing post-migration rework.
Near-zero customer involvement, with minimal dependency on functional and technical knowledge transfer.
Automated ingestion, transformation, and orchestration reduce manual effort.
Full and incremental load validations ensure Market Share data is accurate and actionable.
Cloud-native Databricks architecture ready to support future growth and more advanced analytics.
Connect with the Systech team to see how DBShift™ can accelerate your next migration.
marketing@systechusa.com