A leading cloud computing organization, operating as a multi-cloud solutions provider with a global presence, needed an optimal cloud solution to transform its legacy enterprise data warehouse and enable genuinely data-driven decision-making. The problem was that innovation itself had slowed to a crawl. System enhancements were taking too long, held back by a scattered view of business processes and data models across source systems that made even basic changes feel harder than they should. Effective documentation was another persistent hurdle, and data quality checks simply weren’t in place, leaving the business without the confidence it needed in its own data. The company wanted to deliver better, faster actionable insights through a modernized data platform, one that could drive faster innovation cycles and grow the business through data sharing people could actually trust.
Rather than starting with the technology, Ascendion reverse engineered the process by starting with the business outcomes the client actually needed and working backward from there, building a new enterprise data lake primarily on Google Cloud Platform to deliver an AI-driven, cloud-based technology solution built for speedy enhancements.
A Test Data Framework was leveraged with the CDO to validate straight loads to GCP BigQuery, ensuring quality was built in rather than checked for after the fact. Ascendion used Aloomna to integrate data from DBMS, stream data from Kafka, and onboard huge batch files, while the BigQuery Data Transfer Service was applied for smooth data integration from SaaS platforms and AWS Kinesis, bringing what had been a scattered data landscape into one coherent system.
Tech Stack:
Cut cloud operation costs by 35%, giving the business a leaner, more efficient foundation to run its multi-cloud operations on.
Achieved 55% faster migration, moving data into the new enterprise data lake far more quickly than the legacy warehouse approach ever allowed.
Reduced deployment time by 93%, turning what used to be a slow, multi-step rollout process into something the team could execute with real speed and confidence.
Improved release cycles by 60%, enabling the organization to ship enhancements and respond to business needs at a pace that matched its ambitions, rather than being held back by its own infrastructure.
