Tutorials and examples of how to deploy Presto and connect it to different data sources

This project offers a robust data pipeline solution designed to efficiently extract, transform, and load (ETL) Reddit data into a Redshift data warehouse. Leveraging a blend of industry-standard tools and services, the pipeline ensures seamless data processing and integration.

This project demonstrates how you can build downstream data pipeline using dbt in athena

This project focuses on real-time data streaming with Kinesis, using Flink for advanced processing and OpenSearch for analytics. This architecture has succinctly handled the complete lifecycle of data from ingestion to actionable insights, making it a comprehensive solution.

Data Engineering Specialization offered by Joe Reis in partnership with DeepLearning.AI through Coursera...

In this project I have used the Trending YouTube Video Statistics data from Kaggle to analyze and prepare it for usage.

This projects uses ETL (Extract, Transform and Load) pipeline to extract data from Spotify using its API and loads the data to a data source(AWS Athena). The entire pipeline will be built using Amazon Web Services (AWS).

🏛️ Build an AI-powered personal knowledge system to efficiently manage and access your information with ease.

Projects on Big Data Using Pyspark and AWS

This project showcases a data transformation pipeline utilizing AWS Glue and Amazon Athena to process Spotify data from CSV files. It involves loading, transforming, and storing data in an S3 datawarehouse, enabling seamless querying through Amazon Athena.

DeFtunes - a complete data ecosystem for a music streaming case study, work done as part of a Data Engineering learning & capstone project of DeepLearning.ai Certificate.

Incremental Data Load from S3 Bucket to Amazon Redshift Using AWS Glue

Power BI dashboard for Memphis public safety analytics, built on AWS Athena, Glue, and S3.

Built a cloud-native ETL pipeline using Python, AWS, and Snowflake to analyze public job listings. Automated data ingestion with AWS Lambda, transformation with AWS Glue (PySpark)Stored cleaned data in Amazon S3 and loaded it into Snowflake for advanced analytics. Visualized job trends, skill demand, and salaries using interactive Tableau dashboard

This project builds a pipeline to analyze Superstore sales data using the power of AWS. It transforms the data to make it ready for exploration. Querying the transformed data using SQL queries to uncover trends and patterns. Analyzing results and creates easy-to-understand visualizations, providing clear insights into Superstore sales performance.

Transformed YouTube’s raw JSON data to parquet & loaded it in an S3 bucket, used Glue Data Catalog for storing metadata & Athena to query the cleaned data. Developed an ETL process using a Lambda job that would be triggered when raw data is loaded into an S3 bucket, processed, and stored for analytical purposes in an S3 bucket.

I am dedicated to delivering innovative solutions that align with business objectives while ensuring optimal performance, reliability, and security. My strong analytical skills, attention to detail, and problem-solving abilities drive me to create effective and efficient solutions.

AWS big data pipeline project: S3 upload triggers Lambda to start a Glue ETL job that transforms CSV data and loads ~5.6M records into DynamoDB, with scripts to provision and tear down all infrastructure

This project demonstrates the use of Amazon Web Services (AWS) to analyze superstore sales data. The analysis was performed using AWS S3 for data storage, AWS Glue for data cataloging, AWS Athena for SQL-based serverless data querying, and AWS Quick Sight for visualization. The project’s objective was to provide actionable insights into sales trend