Avanse Financial Services, a leading education loan provider in India, replaced its fragmented analytics environment with an integrated AWS lakehouse architecture using Amazon SageMaker Unified Studio. This migration eliminated costly data synchronization, improved cost efficiency, and streamlined developer workflows for financial analytics.
- Cut costs by eliminating off-platform analytics licensing and redundant data pipelines
- Improved data reliability and consistency with a unified lakehouse on Amazon S3
- Enhanced developer productivity through project-based isolation and integrated notebooks
Infrastructure signal
Avanse moved from a dual-application setup to a unified lakehouse architecture built on AWS, leveraging Amazon S3 for storage, Athena for SQL querying, and SageMaker Unified Studio for integrated analytics and AI workflows. This change eliminated data synchronization delays and reduced operational complexity by keeping all datasets, including those formerly on NFS, in a single governed environment. Storage optimization through S3 Intelligent-Tiering further balanced cost and performance based on usage patterns.
The architecture adheres to financial compliance by supporting ACID transactions on open data formats in Amazon S3, ensuring data integrity and auditability. Security is enhanced via IAM Identity Center for role-based access, while per-project isolation within SageMaker Unified Studio enables granular cost tracking and limits resource contention, fostering reliability and predictable cloud spend.
Developer impact
Developers now operate within isolated SageMaker projects, each equipped with JupyterLab notebooks and access to Athena workgroups, enabling SQL, Python, and PySpark workloads directly against the S3 data lake. Removing the need for local analytics client installations and manual data sync pipelines streamlines workflows and accelerates iteration cycles. Experimentation during a dedicated 72-hour workshop phase validated these new capabilities without impacting production environments.
Access to the environment is centralized through a single browser-based interface fortified by multi-factor authentication, simplifying onboarding and daily use. The integrated SageMaker Catalog empowers analysts to discover business-relevant datasets easily through semantic search, bridging the gap between technical and business vocabulary and reducing reliance on documentation or manual coordination.
What teams should watch
Teams managing financial or sensitive datasets should prioritize eliminating siloed storage locations and redundant external analytics tools to reduce cloud costs and improve data consistency. Adopting project-based governance within unified development platforms like SageMaker Unified Studio helps control access, enforce security policies, and allocate costs accurately across business units.
Observability and lineage tracking integrated natively with AWS identity and governance services will be key to meeting regulatory demands and operational reliability. Development, data engineering, and analytics teams need to collaborate early on phased migration plans incorporating validation workshops that test new tooling and data models before full rollout.