Business Continuity Plan | Streamlined Data Migration and Mapping for Cloud Success

Business Continuity Plan | Streamlined Data Migration and Mapping for Cloud Success

Objectives

The primary objective of this initiative was to implement a Business Continuity Plan (BCP) that would simplify and modernize the process of data mapping and migration. The effort aimed to move critical legacy datasets from traditional on-premises systems to a more agile, cloud-based infrastructure using Azure Data Factory. By employing a Python-based automation script, the project also sought to consolidate over a hundred reports into a unified, streamlined output. Beyond automation, the plan focused on ensuring seamless integration, reliable data performance, and strict data quality assurance. A key part of the initiative involved addressing technical challenges such as mapping intricate resource data and configuring aggregate tables to support scalable, cloud-first reporting solutions

Solution

To bring this initiative to life, a Fortune 500 company partnered with a specialized data services team in April 2020 to execute the Business Continuity Plan. Utilizing Azure Data Factory, the team managed the entire data pipeline—from ingesting Parquet-formatted source files to loading structured data into Azure Synapse tables. A custom Python-based automation tool was developed to consolidate 135 legacy reports into a single, streamlined document, while also handling complex query analysis and reporting needs. The migration process included extensive data quality checks to ensure accuracy and reliability throughout.
One of the major technical undertakings involved mapping intricate resource and aggregate tables to align with the new cloud architecture. As a result, seventy data tables were successfully loaded and now power forty-eight reports actively used by end users. To maintain data freshness, incremental loads are performed daily. In the event of a missing input file, an automated alert system triggers an email notification, prompting the Extract Data Load team to immediately step in and reload the data for that specific day.