Data Migration to Azure Synapse
Data Migration to Azure Synapse
OVERVIEW
Assurant is a 131+ year old insurance provider with a global presence across 21 countries.
With a customer base of 300M+ customers and 13,600+ employees around the world, they provide wide range of business services including insurance (extended service contracts, warranties, and bundles), protection products, service and repair, risk management and logistic services.
Assurant approached People Tech to migrate their databases to Azure Synapse. The project scope included:
- Migration of existing on-premises data warehouse databases and ETL Applications to Azure Cloud to build modern data platform as foundation for Data Science, Enterprise Business Intelligence Reporting and Analytics Applications.
- Establish DR capabilities and fail-over within the agreed upon SLAs in case of failures.
- Modernization of the on-premises DW consisting of 100+ databases, ETL data processing and BI applications to the Azure cloud in a Modern Data Lake Platform Architecture using Databricks and Synapse.
- Utilize PaaS services for scaling during seasonal peaks.
SOLUTION PROVIDED BY PEOPLE TECH GROUP
Solution provided by People Tech covered the following aspects:
- Creation of infrastructure as code (IaC) pipelines using Enterprise TFE modules.
- Migration of Staging Database Tables to ADLS Gen2 Data Lake on Azure using Databricks Delta Table formats and SPARK for data processing.
- Migration of Data Mart Tables into Azure Synapse Dedicated SQL Pool and repointing of OLAP cubes, BI Reports onto Synapse.
- QA testing of the migration process which included object migration and data completeness testing.
- Support Downstream/ Consumption team during Integration testing.
BENEFITS
- Achieved minimum downtime in case of any hardware failure.
- Improved the resiliency of the critical DW application with fail-over capabilities.
- Business teams drove operational efficiency and automation by using AI and ML solutions and utilized the power of Data Lake.
- Data lake brings the ability to power more real-time use cases and support semi-structured and unstructured data processing needs.
- Unified customer view across various products and business lines and reducing siloed data analytics.
