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SUCCESS STORIES

https://nucleusteq.com/wp-content/uploads/2020/03/cloud-azure.jpg

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.
https://nucleusteq.com/wp-content/uploads/2020/03/finance-housing.jpg

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

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