Prakash Chowdary Email & Phone Number

Sunnyvale, CA, US

• Spearheaded Requirement Gathering, Business Analysis, and translation of business requirements into technical designs for Big Data Projects.
• Developed a profound understanding of Spark Architecture, covering Spark Core, Spark SQL, Data Frames, and Spark Streaming.
• Orchestrated the scheduling of diverse Snowflake jobs through NiFi and migrated 5TB of data to Snowflake, improving data accessibility and reducing data retrieval time by 40%.
• Enhanced data integration efficiency by 30% with Azure Synapse Analytics, utilizing Azure Data Factory for streamlined orchestration of data pipelines and Databricks for advanced data processing.
• Designed and implemented real-time data streaming solutions, leveraging Python, Spark, and Kafka technologies.
• Spearheaded the implementation of Apache Airflow for orchestrating complex data workflows, resulting in a 30% reduction in data processing time and a 25% improvement in pipeline reliability.

Austin, Texas, United States

• Enhanced and optimized enterprise data processing pipelines using Apache Spark and cloud technologies, substantially improving processing efficiency and system performance.
• Designed and implemented an Apache Spark-based data integration solution, reducing data transfer time by 40% and increasing efficiency by 30%.
• Designed and enhanced Data Warehouse and Data Lake architecture, enabling seamless handling of increased data volumes by 50% and reducing data storage costs by 20%.
• Employed Python for the automation of recurring reports and visualization on Business Intelligence (BI) platforms.
• Facilitated collaborative teamwork to redesign database architecture, resulting in a 30% increase in query performance and a 25% reduction in data retrieval time.
• Implemented Agile best practices across development teams, significantly improving project delivery timelines while enhancing cross-team collaboration and communication efficiency.

Bengaluru, Karnataka, India

• Optimized Big Data processing workflows through comprehensive analysis and tuning of Hadoop ecosystem components including Hive, HBase, and Sqoop, improving cluster performance significantly.
• Implemented automated data pipeline using Sqoop and Oozie workflows for RDBMS to HDFS ingestion, reducing data transfer time by 20% and eliminating manual processes.
• Developed over 15 MapReduce jobs in Python, resulting in a 25% reduction in data cleaning and preprocessing time, thereby enhancing overall efficiency of data pipelines.
• Implemented complex data migration from Teradata SQL to Snowflake, developing comprehensive validation scripts and ensuring high data integrity through meticulous mismatch analysis.
• Architected and implemented a comprehensive data processing pipeline on AWS, leveraging Apache Spark on EMR, Amazon Redshift, and S3, resulting in a 40% reduction in data processing time.
• Implemented efficient data transfer solutions using Apache Spark to process 1TB data from File System to S3, reducing transfer time by 25% while improving system reliability.