In this repo datailed theory of what can i use in this project ------------------------------------------------
🌟# AWS_ETL_project Theory of Services used 🚀🚀 Build a serverless ETL pipeline using AWS Glue to process and analyze CSV data stored in S3 with Athena.
- AWS S3 (Simple Storage Service) • What is it? o Object storage service that provides scalable, secure, and high-performance storage. • Key Features: o Stores data as objects in buckets. o Highly available, durable, and cost-effective. o Supports multiple storage classes (Standard, IA, Glacier, etc.). o Queryable with Athena (without moving data). o Integrated with AWS Glue for ETL processes. • Use Cases: o Data lake storage. o Backup and archival. o Hosting static websites.
- AWS Glue • What is it? o A fully managed ETL (Extract, Transform, Load) service that prepares data for analytics. • Key Components: o Glue Crawler: Automatically scans S3 and creates metadata in the Glue Data Catalog. o Glue Data Catalog: Stores metadata for structured/semi-structured data. o Glue Jobs: Runs transformations using PySpark or Python shell scripts. • Key Features: o Serverless (scales automatically). o Supports schema inference. o Can integrate with Athena, Redshift, and RDS. o Supports partitioning and cataloging for better performance. • Use Cases: o Cleaning and transforming raw data from S3. o Automating data pipelines for machine learning. o Migrating and preparing data for analytics.
- AWS Athena • What is it? o A serverless query service to analyze data directly from S3 using SQL. • Key Features: o Uses Presto (open-source SQL engine). o Queries data without loading it into a database. o Supports structured and semi-structured formats (CSV, JSON, Parquet, ORC). o Works with Glue Data Catalog for metadata management. o Pay-per-query pricing model (cost-efficient). • Use Cases: o Ad-hoc querying of large datasets in S3. o Log analysis and reporting. o Business intelligence and analytics.
How They Work Together
- Store raw data in S3 → (data lake storage).
- Use AWS Glue Crawler → (automatically infer schema & create metadata in Glue Data Catalog).
- Run AWS Glue Jobs → (clean and transform data if needed).
- Query data using Athena → (run SQL queries directly on S3 without moving data). This combination is widely used for big data analytics and serverless data pipelines. 🚀