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About the customer – Customer is one of the top 3 pet retailers in the world
Customer Challenge
- Lack of centralization, standardization and self-service of the enterprise data leading to higher time to market, costs & redundancies
- Projects based development as opposed to “Platform / Products” thinking leading to significant cost & redundancy.
NucleusTeq Solution Approach
- Defined & Created core platform components on the Azure environment to support Scheduling, Ingestion, Metadata Management, Data quality etc.
- Created end-to-end monitoring & operations management platform to ensure optimal use of the platform
- Created Continuous Integration, Continuous Deployment, Automated testing capabilities for all federated applications. Extensive use of enterprise Jenkins.
- Created data consumption layer through microservices architecture using Azure Kubernetes Services.
Outcomes Delivered
- Unified single version of truth data lake for the enterprise
- 5X faster time to market for new reports & data capabilities
- 40% reduction in operating expenses through cloud adoption & management.
About the customer – Customer is one of the top federal housing finance company
Customer Challenge
- Enterprise Data Warehouse (EDW) on Netezza to be migrated to AWS Cloud.
- Design and deliver a comprehensive solution implementing best practice recommendations in line with the enterprise data lake goals.
- Replicate the current Netezza snapshotting process in the EDW environment into the AWS environment
- Provide best practice recommendations in the areas of metadata capture, lineage & audit
NucleusTeq Solution Approach
- Migrated the snapshotting process from Netezza to AWS based environment
- Migrated base tables in 3NF from IDS (Integrated Data Store) to S3 and update them on an ongoing basis with incremental updates
- Implemented identified data migration patterns in a way that it can be reused for tables conforming to similar patterns
- Identified and migrated major percentage of the EDW users and their associated queries that qualify as long-running workloads to S3
- Comprehensive architecture on AWS addressing the enterprise data lake goals with best practice recommendations in the areas of metadata capture, lineage, storage & audit
Outcomes Delivered
- Offload of the major percentage of Netezza based EDW (data and workloads) to AWS based environment
- Free the EDW from snapshotting process by migrating the process to AWS
- Use of cloud offering elasticity to the process for future scaling