Chathurya T Email & Phone Number

Denton, Tx, Us

Dallas, Texas, United States

This project aimed to build an efficient, scalable data pipeline to analyze customer behavior and derive actionable insights for targeted marketing and risk assessment. Customer interaction data from Amazon EFS was ingested into AWS S3 using Informatica. The data was then processed using Spark on AWS EMR to uncover patterns and trends in customer behavior. Following this, ETL pipelines were implemented to load the transformed data into Snowflake for further analysis. Automating these processes using AWS Glue, Lambda, and DBT significantly improved data processing speed and operational efficiency. Tableau dashboards provided visual insights, enabling stakeholders to make informed business decisions.

Hyderabad, Telangana, India

This project aimed to develop a scalable, real-time data integration and processing solution for a retail company, capable of handling 10,000 transactions per hour during peak times. Kafka was integrated with Azure Data Factory for real-time ingestion, while Azure Databricks with PySpark improved data transformation efficiency. Data security was ensured using Azure Key Vault, and processed data was stored in Azure Blob Storage, handling over 1 TB of transactional data. Data insights were extracted using Azure Synapse Analytics, with automation through Azure Logic Apps and Functions. Power BI dashboards provided visual insights for improved decision-making.

Hyderabad, Telangana, India

This project focused on efficient batch processing and real-time analytics for large datasets. Data from Hadoop (HDFS) was extracted via Hive and integrated using Talend, then processed using AWS EMR and Spark to enhance speed and reliability. Processed data was stored in AWS S3 for scalability and later transferred to Amazon Redshift for advanced analytics. Airflow was used to monitor data workflows, and performance optimizations in Redshift improved query performance. Interactive Tableau dashboards were developed to visualize real-time data for decision-making, while Kubernetes clusters and NoSQL databases (Cassandra, MongoDB) supported the platform’s scalability and performance.