/covid19-data-engineering

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covid19-data-engineering

AWS Data Pipeline Implementation

Overview

This project entails the implementation of a robust data pipeline on AWS, leveraging various services to ingest, process, and analyze data for actionable insights. The pipeline facilitates the seamless integration of data stored in multiple CSV files into AWS services for agile querying, transformation, and reporting.

Features

  • AWS S3 Storage: The data pipeline stores multiple CSV files in an AWS S3 bucket, ensuring scalable and durable storage for large datasets.

  • AWS Glue Integration: AWS Glue crawlers are utilized to automatically extract schema and metadata from the stored data, enabling effortless integration with downstream services.

  • AWS Athena Integration: The extracted schema and metadata are leveraged for agile querying and quick insights using AWS Athena, a serverless interactive query service.

  • ETL Processing with AWS Glue: ETL (Extract, Transform, Load) tasks are executed using AWS Glue to transform the raw data and load it into Amazon Redshift, a fully managed data warehouse service.

  • Amazon Redshift Integration: Data loaded into Amazon Redshift facilitates further analysis and reporting capabilities, empowering data-driven decision-making processes.

Installation and Configuration

  1. AWS Account: Ensure you have an active AWS account with appropriate permissions to access AWS services like S3, Glue, Athena, and Redshift.

  2. AWS S3: Create an S3 bucket to store your data files.

  3. AWS Glue: Set up AWS Glue crawlers to extract schema and metadata from the stored data in S3.

  4. AWS Athena: Create a database and tables in Athena using the extracted schema.

  5. AWS Redshift: Provision an Amazon Redshift cluster and configure it to load data from AWS Glue.

  6. Data Pipeline Configuration: Configure AWS Glue ETL jobs to transform data and load it into Amazon Redshift.

Usage

  1. Data Ingestion: Upload your CSV files to the configured S3 bucket.

  2. Data Processing: Run AWS Glue crawlers to extract schema and metadata from the uploaded data.

  3. Querying with Athena: Utilize AWS Athena to query the extracted data for insights and analysis.

  4. ETL Processing: Trigger AWS Glue ETL jobs to transform and load the data into Amazon Redshift for further analysis and reporting.