Business Objective / Goal
To reduce system stack costs, enhance business productivity, and prepare for AI/ML readiness by building a distributed, open-source-based enterprise data lake architecture on AWS capable of handling both structured and unstructured data.
Solutions & Implementation
- Conducted in-depth requirement analysis, due diligence, and cost modeling using AWS Pricing & TCO Calculators.
- Designed a TO-BE architecture blueprint and implemented it in three stages (Drop 1 to Drop 3).
- Migrated databases to PostgreSQL, replaced legacy systems, and integrated with DCS & SLOB data sources.
- Uploaded IoT and unstructured data to AWS Cloud and enabled connectivity via Presto, Athena, Python, and R.
- Migrated dashboards from Tableau to Superset, AWS QuickSight, and D3.js, and automated processes for scalability.
- Followed PMBOK and CRISP-DM frameworks for project governance and analytics delivery.
Major Technologies Used
- AWS S3, AWS RDS, AWS Lambda, AWS Glue, AWS Athena, AWS QuickSight – Core AWS stack
- PostgreSQL, DynamoDB, Amazon DMS, Amazon Kinesis – Data management
- Python, R – Analytics scripting and machine learning
- Superset, D3.js – Visualization platforms
- CloudWatch, CloudTrail – Monitoring and logging
Business Outcomes
- Reduced IT Costs and Improved ROI Migrated to AWS cloud with open-source components to reduce system stack costs across three project phases.
- Improved Productivity and Performance Enabled faster reporting, reduced manual effort, and improved data availability with self-service capabilities.
- Scalable Enterprise Architecture for AI/ML Readiness Built a future-proof foundation to support advanced analytics, IoT data, and machine learning models.
- Enterprise Search and Ad-Hoc Query Enablement Integrated AWS Athena and Presto for fast, flexible queries across structured and unstructured datasets.
