Tech company’s data warehouse migration to the cloud results in 50% improvement in on-time data availability
Summary
Our client is a Fortune 500 intelligent data infrastructure company that provides unified data storage, integrated data services and cloud operations solutions. They turned to HCLTech to modernize their on-prem data warehouse to a cloud-based solution. HCLTech redesigned the data model and optimized ETL processes to enable a high-performance, scalable data analytics platform.
The Challenge
Outdated platform and disparate data sources
The client's ten-year-old on-prem analytics platform had accumulated huge technical debt. In addition, poorly designed batch jobs delayed data availability, and disparate data sources and multiple sources of truth threatened data accuracy. Other challenges included the company's inability to produce real-time analytics or seamlessly scale the system.
The Objective
End-to-end platform modernization
The client tapped HCLTech to transform data collection and integration. Our goals were to modernize their end-to-end enterprise analytics platform by migrating it from on-prem to cloud, consolidating the data source footprint and supporting strategic insights and decision-making. The flexible, agile and scalable platform would also improve on-time data availability and deliver end-user reporting.
The Solution
Cloud, automation and process optimization
HCLTech got to work: We implemented the Snowflake data cloud to migrate the client’s on-prem data footprint to the cloud and migrated the existing solution to next-gen cloud architecture. The HCLTech ADMigrate framework and product components reduced migration risks. HCLTech Gatekeeper enabled automated testing.
We employed a mix of Snowflake data cloud and Informatica Intelligent Cloud Services to provide a scalable infrastructure for all ETL and ELT processes. Optimizing 2,400 ETL jobs to 700 redesigned jobs would ensure faster refresh and improved performance.
We created consumption-aligned data sets and virtualization, automated data reconciliation and improved flexibility through consumption-based billing.
The Impact
Efficiency, accuracy and cost savings
Eliminating deprecated platforms and consolidating database, data integration, security and other tools on a single set of cloud-based platforms simplified the landscape and reduced costs. The positive outcomes speak for themselves:
- 30% reduction in time to add new data sources to the data warehouse; business analysts can securely access structured and unstructured data from all SaaS and on-prem applications
- The data refresh rate was reduced from 12 hours to 4 hours, enabling rapid adoption of native AI capabilities for near real-time insights and reporting
- 50% improvement in on-time data availability
- A reduction in storage from 1 PB to 200 TB and transition to subscription solutions running in a public cloud reduced ongoing costs by 20%
- Data integration and unification significantly reduced the number of data hops, improving efficiency, accuracy and quality.
The client now has a thoroughly modernized and future-ready platform.
