Services
A mid-size life insurance carrier.
Business Objective
The client sought to modernize and transform their enterprise Data Ecosystem by building a Cloud Data Platform that serves as a single source of truth for analytics, business intelligence, and predictive use cases across the enterprise. They needed a comprehensive data management strategy that standardizes data collection, storage, processing, and consumption with strong governance, security, and innovation to improve efficiency, quality, and scalable access to data products across the enterprise.
Mphasis addressed the client’s requirements by leveraging Databricks on AWS (managed service) as a collaborative, unified analytics workspace, combining data engineering, advanced analytics, machine learning, and visualization. The platform integrates Apache Spark’s performance with AWS’ scalability and reliability to process, analyze, and derive insights from large datasets in near real time, supporting data engineering, ML, and BI workflows.
Architecture Overview:
AWS products and services used include - Databricks, Aurora PostgreSQL, Lambda, S3, CloudWatch, KMS, IAM, S3 Data Lake, VPC, Grafana, CloudTrail, Route 53, AWS Trusted Advisor, RDS MySQL, GuardDuty.
Single source of truth with standardized, governed datasets for analytics, BI, and ML
Faster time-to-insight via reusable components, self-service access, and scalable Databricks compute
Improved data quality, lineage, and compliance through centralized governance and encryption
Cost and performance optimization using elastic compute and right-sized storage tiers
Foundation for predictive analytics and generative AI workloads across business domains