CPR E/SE 419: Software Tools for Large-Scale Data Analysis

Use best practices for database management, web application design, development, and deployment surrounding RESTful services, coordinating effectively with 20+ researchers.☛Database management and Querying --Postgres database with more than 10M entries☛Ensure efficient project management in conducting spatial-time series data compression, indexing, interpolation, and analysis under a heavy workload.☛Construct unique machine learning and deep learning models, graph neural networks, and perform extensive semantic analysis to produce meaningful insights into development and procedures.☛Act as a trusted expert with Computational Geometry, training incoming project members.

SE 329 - Software Project Management

Cpre/SE 414 -Introduction to Software Systems for Big Data Analytics: Taught two lab groups and graded Assignments/Labs/Exams of best practices tools of Big Data Analytics.☛Taught Transaction SQL queries using MySQL Workbench, a visual database design tool that integrates SQL development, administration, database design, creation, and maintenance into a single integrated development environment for the MySQL database system.☛ Taught SSDT (SQL Server Data Tools) and SSMS (SQL Server Management Studio) to design multidimensional cubes and perform OLAP (Online Analytical Processing) using MDX (Multidimensional Data Expressions) on a large volume of data from the data warehouse.☛ Taught Neo4j, a NoSQL graph database management system.☛ Taught MongoDB, a NoSQL documented oriented database management system.☛ Taught QGIS, a geographic system application for viewing, editing, and analyzing geospatial data.

CprE-281-Digital Logic:☛Taught recitations in 3 lab groups of 24 students that allowed them to learn and review materials on number systems and representations, Boolean algebra and logic minimization, combinational and sequential logic design, and arithmetic and finite state machines.

Worked as an important member of a team of eight to help the company move its legacy database to Datawarehouse to minimize the complex queries and execution time by using AWS tools and creating a proof of concept (POC).☛Utilized software engineering skills to transfer legacy databases to an AWS Data Lake using an ETL pipeline.☛Pipeline: Aurora > DMS (Extract) > S3 > Glue (Transform) > S3 Data Lake (Load)> Athena > QuickSight.☛Transformed data using PySpark ETL operations inside AWS Glue, utilizing Python wheels to make modular code.☛Created an authorization model, user policies in AWS IAM and Lake Formation, and visualizations in Amazon QuickSight.

Led well-received projects, including South Asian Vehicle Tracking System, and acted as an instructor for workshops for senior design projects using superb communication skills.☛Incorporated new methodologies for posting and storing real-time data of a vehicle to the database using HTTP POST method and authored a handbook detailing common issues and lessons learned.