VAMSI KRISHNA REDDY GADI

Data Pipeline and Infrastructure Development:

• Initiated and led development of Kafka-based data streaming platform. This platform processes millions of events daily, resulting in 30% reduction in latency for real-time data availability.
• Integrated DynamoDB streams with AWS Lambda for real-time processing and PII data handling, leading to 25% improvement in operational efficiency and enhanced data quality for analytics.

Cloud Infrastructure as Code:

• Implemented Terraform to automate provisioning of AWS resources, yielding a 50% faster rollout for new environments and 20% reduction in cloud-related costs through efficient resource utilization.
• Enforced best practices for infrastructure management, resulting in zero downtime during large-scale deployments.

Cloud Data Services and ETL Processes:

• Architected and maintained scalable AWS Glue ETL processes with PySpark to handle complex transformations and data processing jobs doubling data processing capacity and aided advanced analytics initiatives.
• Improved analytics teams' data discovery by Streamlining metadata management in Glue Data Catalog.

Data Storage and Management:

• Formulated compliant S3-based data lake, incorporating Delta tables and Parquet, enhancing data query performance by 35% and supporting petabyte-scale data storage.

Data Warehousing and Querying:

• Crafted and deployed optimized Amazon Redshift data warehouse, leveraging star schema modeling, increasing query performance five-fold specifically complex analytical queries.
• Expanded the use of AWS Athena, facilitating ad-hoc querying capabilities that empowered business users to perform data exploration without IT intervention.

Workflow Automation:

• Orchestrated and automated data workflows using Apache Airflow, which enabled consistent execution of batch jobs reducing manual intervention by 90%.

Data Visualization and Reporting:

• Connected data pipelines to Tableau and AWS QuickSight, providing advanced reporting producing self- service dashboards and actionable insights to business stakeholders to enhance data-driven decision- making capabilities across the company.

Performance Optimization and Security:

• Applied PySpark's in-memory processing to tune the performance of data jobs, contributing to a 20% increase in overall system performance.
• Implemented comprehensive security measures, including encryption and IAM policies, ensuring full compliance with internal and external security standards.

• Led Merchant Commission Differential Pricing project, utilizing PySpark in tandem with Apache Spark for real-time data processing, cutting query response times by 40%.
• Enhanced Spark's data processing by using PySpark to facilitate seamless integration with NoSQL databases, managing over 1TB of daily transactional data, supporting high-volume data analytics.
• Scripted automation workflows using shell scripting cut down data processing times by 30% and increased data pipeline's reliability.
• Configured MongoDB for replication and sharding, ensuring near perfect data availability and scalability for growing e-commerce demands.
• Administered Elasticsearch clusters, boosting search and analytics performance and resulting in 20% faster data retrieval for e-commerce applications.
• Devised Kibana dashboards, which provided real-time operational insights, leading to improvement in decision-making efficiency.
• Integrated PySpark with AWS services like S3, Lambda, and Step Functions to double processing capabilities and streamline workflows, creating resilient and flexible data management process.
• Orchestrated strategic project planning, optimizing data capacity planning and reducing resource over- provisioning by 20%.
• Elevated Kroger's data processing framework, enhancing scalability and efficiency, which facilitated data-driven business growth and better strategic decision-making.

• Monitored and tuned Hadoop clusters for optimal performance using Cloudera Manager and Hortonworks Data Platform, resulting in 20% increase in processing efficiency.
• Troubleshot complex cluster issues, enhancing system stability and reducing downtime by 15%.
• Implemented Flume agents for efficient data ingestion, enabling processing of streaming log files and facilitating real-time analytics capabilities, which allowed for faster data flow into Hadoop ecosystem.
• Integrated Hive and Pig for advanced data analysis and script optimization, achieving 40% improvement in query execution time.
• Designed SQL-based data manipulation scripts to support complex ETL tasks, enhancing data analysis readiness.
• Expedited transitioning from traditional data warehouses to Hadoop-based platforms, minimizing user disruption and enhancing adoption rates
• Established high-availability Hadoop clusters using tools like ZooKeeper and Ambari, resulting in near perfect system availability.