CHALLENGE

Overcoming Fragmentation and Data Latency

A Retail Company relied on multiple on-premises data systems with limited integration, creating a fragmented and hard-to-scale data landscape. Business-critical workloads were locked into legacy infrastructure, constraining agility and analytics capability.

Data ingestion and transformation processes were partially manual or based on rigid batch mechanisms hosted on-premises, resulting in delayed data availability, duplication of logic, and reduced reliability across consumption layers.

The inability to scale existing on-premises infrastructure in line with growing data volumes and new digital initiatives created an urgent need to migrate workloads to the cloud and re-establish data operations on a modern, managed platform.

OUR SOLUTION

A Cloud-Native Lakehouse Model

BIP xTech led the migration of on-premises data workloads to AWS, redesigning the platform as a cloud-native data lake to centralize ingestion, automate processing, and strengthen governance at enterprise scale.

First, on-premises data sources were assessed and migrated to AWS through a structured cloud migration approach grounded in the AWS Cloud Adoption Framework, establishing a multi-account foundation aligned with AWS Control Tower and AWS Organizations to separate production and non-production workloads.

Second, a layered lakehouse architecture (Landing, Bronze, Silver, Gold) was established on Amazon S3, with AWS Glue ETL and Glue Data Catalog enabling progressive data refinement, metadata consistency, and curated analytics-ready datasets.

Third, a managed and event-driven ingestion pattern combined AWS AppFlow, AWS DMS, AWS Lambda, EventBridge, and AWS Step Functions to integrate heterogeneous sources and orchestrate resilient pipelines with reduced manual intervention.

Finally, analytics and governance capabilities were strengthened through Amazon Athena, Power BI integration, Lake Formation access controls, CloudWatch/CloudTrail monitoring, and Terraform-based Infrastructure as Code integrated with CI/CD practices.

RESULTS

Faster Data Availability and Higher Operational Efficiency

The migration program delivered a production-grade AWS platform that replaced on-premises data workloads, supporting daily ingestion, transformation, and analytics across multiple business domains.

Data time-to-market improved significantly, reducing latency from over 24 hours to approximately 2-4 hours depending on data domain and processing complexity.

Operational productivity increased through end-to-end pipeline automation, with documented efficiency gains in the range of 20%-40% for teams involved in data operations and analytics.

Conclusion

The initiative successfully migrated a fragmented on-premises data ecosystem to a scalable and governed AWS-native platform.

By moving legacy workloads to the cloud and rebuilding data pipelines on managed, serverless AWS services, the program eliminated infrastructure constraints, reduced operational overhead, and enabled faster, more reliable analytics delivery.

The migrated platform now serves as the foundation for future advanced capabilities such as predictive analytics and machine learning.