Akshay Adme

Global Leader Logistics Client: In this project, we work with a client to process their raw data using advanced machine learning techniques

• The data processing pipeline includes steps for data sanitization, transformation, and cleaning, ensuring the data is accurate, consistent, and ready for analysis
• The processed data is then used to build and train machine learning models to provide valuable insights and predictions that support the client's business objectives
• API Development for HDFS Connection and Processing: Establish HDFS Connection: Develop APIs to establish a connection with the Hadoop Distributed File System (HDFS) using Java libraries, such as Apache Hadoop.
• Data Processing Logic: Implement logic within the APIs to perform various data processing tasks on HDFS, such as reading, writing, and transforming data files
• Error Handling and Logging: Implement robust error handling and logging mechanisms to ensure smooth operation and easy troubleshooting of the HDFS interactions
• Performance Optimization: Optimize the API performance to handle large-scale data efficiently and ensure minimal latency.

Data Engineering Platform (DEP) streamlines data management and improves data quality by standardizing the processes of data intake, sharing, and storage.

• API Development for AWS and Database Operations: Design and Implement Database Schema: Collaborate with the data engineering team to design a database schema that meets application requirements.
• Create and manage database tables, relationships, and indexes in Amazon RDS to optimize performance.
• Develop RESTful APIs: Write RESTful APIs using Spring Boot to perform Create, Read, Update, and Delete (CRUD) operations on Amazon RDS databases.
• API Development for AWS S3 Connection and Processing: Establish AWS S3 Connection: Develop APIs that establish secure connections to Amazon S3 using AWS SDKs to manage file interactions, including uploads, downloads, and file deletion.
• Data Processing Logic: Implement logic to perform data processing tasks in Amazon S3, such as reading, writing, and transforming data files.
• Error Handling and Logging: Implement robust error handling and logging mechanisms with CloudWatch to ensure smooth operation and ease of troubleshooting, S3 file interactions.