Big data and Cloud Deployment
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Updated
Jan 15, 2024 - Jupyter Notebook
Big data and Cloud Deployment
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.
This project demonstrates how you can build downstream data pipeline using dbt in athena
AWS S3 & Sentiment Analysis, Basic Plotting with Matplotlib, & Supervised Learning & Machine Learning with Sklearn.
Incremental Data Load from S3 Bucket to Amazon Redshift Using AWS Glue
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.
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.
An End-To-End data pipeline integration from Website Source to analytical dashboard in AWS using Python flask, ML models, DynamoDB and other AWS services.
Projects on Big Data Using Pyspark and AWS
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 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.
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).
In this project I have used the Trending YouTube Video Statistics data from Kaggle to analyze and prepare it for usage.
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