Rithik Reddy Email & Phone Number

Chicago, Il, Us

Rochester, Minnesota, United States

- Designing and architecting scalable big data solutions that accommodate Mayo Clinic's growing data needs, including storage, processing, and analysis of large volumes of healthcare data.- Extensively worked on Spark Streaming and Apache Kafka to fetch live stream data. - Developing and maintaining data models and schemas optimized for healthcare analytics and reporting purposes. Performing data transformations, cleansing, and enrichment to ensure data quality and consistency.

Evansville, Indiana, United States

- Designing and architecting scalable, reliable, and secure data solutions on AWS to meet business requirements and regulatory standards within the banking sector.- Setting up and managing database systems such as Amazon RDS (Relational Database Service), Amazon Redshift, or DynamoDB to store and organize transactional and analytical data effectively.- Monitoring and optimizing AWS resource usage and costs through services like AWS Cost Explorer.

Columbus, Ohio, United States

• Designed end to end scalable architecture to solve business problems using various Azure Components like HDInsight, Data Factory, Data Lake, Storage and Machine Learning Studio.• Developed JSON Scripts for deploying the Pipeline in Azure Data Factory (ADF) that process the data using the SQL Activity.

Hyderabad, Telangana, India

• Designed and implemented a scalable data lake architecture leveraging Apache Hadoop HDFS for storage and Apache Hive for data querying, enabling efficient data storage and retrieval for analytics purposes.• Utilized Docker for containerization and Kubernetes for container orchestration to deploy and manage big data applications and microservices, ensuring scalability and reliability in production environments.

Hyderabad, Telangana, India

• Designed and implemented robust and scalable big data architectures using technologies such as Hadoop, Spark, and Kafka, ensuring efficient data processing and storage.• Set up monitoring and alerting systems to proactively identify and address data processing issues, minimizing downtime and disruptions.• Utilized version control systems (e.g., Git) to manage code and configuration changes, ensuring traceability and collaboration with team members.