 Designed a data lakehouse solution with Apache Hadoop 3.3 and HBase 2.4 for scalable storage and real-time processing, integrating various data sources to streamline access and cut retrieval times by 35%. Deployed Apache Spark 3.4 and Hive 3.1 to establish efficient ETL processes for large-scale data management and warehousing, enhancing data processing efficiency by 50% and improving overall system performance. Automated ETL pipelines using AWS Glue 4.0 and AWS Kinesis 2.0 for batch and real-time processing, accelerating data ingestion into AWS S3, which resulted in a 40% increase in data availability. Configured Google BigQuery and integrated with Power BI to enable high-performance querying and real-time visualizations, producing actionable dashboards and reports that improved decision-making processes by 30%. Enhanced query performance with AWS Redshift Spectrum (v2024) by querying data directly from AWS S3, utilizing columnar storage and compression encoding, which boosted query speed by 45% and reduced associated costs.

• Developed and executed a phased migration strategy from SQL Server 2012 to 2017, minimizing downtime by utilizing SQL Server DMA and Upgrade Advisor for thorough impact analysis and compatibility checks. • Ensured data integrity during migration by conducting pre-migration checks and extensive testing in a staging environment, validating data and application functionality using SSMS. • Automated key migration tasks, including backups and restores, with PowerShell and T-SQL scripts, reducing manual intervention by 40% and enhancing overall consistency and efficiency. • Improved database performance post-upgrade by 20% through optimized indexing strategies and query tuning, utilizing SSMS and DMVs to enhance system speed and responsiveness. • Documented the entire migration process, including issues and resolutions, generating detailed reports and integrating AWS CloudWatch for effective monitoring and logging of system performance.

• Directed the containerization of SQL Server 2019 with Docker, deploying over 30 instances using Kubernetes and Helm, resulting in a 40% faster deployment speed and 35% reduction in downtime through automated scaling and orchestration. • Automated SQL Server container management with PowerShell and AWS CloudFormation, cutting manual errors by 60% and ensuring consistent provisioning, significantly enhancing operational efficiency. • Implemented monitoring solutions with Prometheus and Grafana, creating custom dashboards and alerts that reduced issue detection and resolution times by 45% and improved performance tracking. • Designed and executed robust backup and recovery strategies for SQL Server containers, achieving a 99.9% backup success rate and reducing recovery time by 30% through the use of custom scripts. • Enhanced security by applying best practices, including RBAC and network policies in Kubernetes, reducing vulnerability risks and ensuring compliance with security norms, monitored through AWS Security Hub.