REDDIT-AWS-DATA-PIPELINE

This project implements an end-to-end ETL (Extract, Transform, Load) pipeline using Apache Airflow, AWS services (Glue, Crawler, Athena, Redshift), and other supporting tools. The primary goal is to extract Reddit posts, transform the data, and load it into a data warehouse for analysis and dash boarding.

Table of Contents

• Project Overview
• Architecture
• Prerequisites
• Setup Instructions
• Potential Flaws and Solutions
• Future Add-ons

Project Overview

This ETL pipeline:

Extracts Reddit posts using the PRAW library.

Transforms the data to ensure consistency and readiness for analysis.

Loads the transformed data into AWS S3 and subsequently into AWS Redshift for data warehousing.

Utilizes AWS Glue for ETL operations and AWS Athena for querying.

Architecture

The architecture comprises:

Apache Airflow: Orchestrates the ETL pipeline.

AWS Glue: Handles the ETL jobs.

AWS Crawler: Creates a database from the S3 data.

AWS Athena: Enables querying the data.

AWS Redshift: Serves as the data warehouse for storing and analyzing data.

PostgreSQL: Used by Airflow for metadata storage.

Redis: Serves as the broker for CeleryExecutor.

Prerequisites

Docker and Docker Compose

AWS Account with necessary permissions for S3, Glue, Athena, and Redshift

Apache Airflow 2.7.1 with Python 3.9

Potential Flaws

Data Consistency Issues:

Extracted data may have inconsistencies or missing fields.

ETL Failures:

The ETL process might fail due to network issues, API rate limits, or incorrect data transformations.

Scalability:

The current setup may not scale well with increasing data volume.

Security:

Sensitive information (e.g., AWS credentials) might be exposed if not handled properly.

Solutions

Data Consistency:

Implement data validation and cleansing steps in the transformation process.

ETL Failures:

Add error handling and retry mechanisms in the ETL pipeline.

Monitor and log ETL jobs to quickly identify and resolve issues.

Scalability:

Use more scalable storage solutions like AWS Redshift Spectrum or partitioned S3 buckets.

Optimize Glue jobs and use parallel processing where possible.

Security:

Use AWS Secrets Manager or AWS Systems Manager Parameter Store to securely manage credentials.

Implement IAM roles and policies to limit access to sensitive resources.

Future Add-ons

Real-time Data Processing:

Integrate Apache Kafka or AWS Kinesis for real-time data streaming and processing.

Advanced Analytics:

Implement machine learning models using AWS SageMaker for advanced analytics on the data.

Enhanced Dashboarding:

Use tools like Tableau or Power BI for more advanced data visualization and dashboarding capabilities.

Automated Reporting:

Set up automated reporting using tools like IBM Cognos Analytics or Google Data Studio.