1. Source-to-Target Mapping (STTM) Design - Engineered STTM documents to define extraction, transformation, and loading specifications for migrating structured and semi-structured data from on-premise sources to Azure Cloud. - Established data lineage and schema mappings for cross-platform integration. 2. Legacy System Analysis and Documentation - Conducted deep-dive system analysis to deconstruct data flow patterns, dependency relationships using Microsoft SQL server 3. Data Ingestion and Processing Pipeline - Developed PySpark-driven Microsoft Fabric notebooks for structured batch ingestion and real-time streaming data capture. - Applied schema evolution techniques, ensuring adaptive data transformation and enrichment using Spark SQL and PySpark functions. 4. Dimensional Modeling and Data Transformation - Designed star schema and snowflake models optimized for analytical queries in Microsoft Fabric lakehouses. - Engineered ETL pipelines using Spark SQL for staging, cleansing, and hierarchical aggregation of transactional and reference datasets. 5.Data Quality and Validation Framework. - Developed Data Quality Notebooks to systematically compare source vs target datasets, ensuring referential integrity and completeness. PySpark dataframe APIs to flag anomalies using threshold-based variance checks. 6.Designed and implemented a scalable data pipeline leveraging CouchDB to manage high-volume, semi-structured data. Optimized data replication and synchronization to support distributed, fault-tolerant systems. Integrated CouchDB with ETL workflows to enable efficient real-time analytics and reporting. Focused on performance tuning, indexing strategies, and seamless RESTful data access.