ETL | AWS Glue | AWS S3 | Transformations | AWS Glue ETL Data Pipeline With Advanced Transformations

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Title: AWS Glue ETL Data Pipeline With Advanced Transformations
Introduction
Opening: The video starts with an introduction to AWS Glue, highlighting its capabilities as a serverless ETL (Extract, Transform, Load) service that simplifies the process of preparing and loading data for analytics.
Objective: The presenter outlines the goal of the video: to demonstrate how to build an advanced ETL data pipeline using AWS Glue, incorporating sophisticated data transformations.
Part 1: Overview of AWS Glue
Service Explanation: Brief overview of AWS Glue, including its components like Glue Data Catalog, Glue Crawlers, and Glue Jobs.
Use Cases: Examples of scenarios where AWS Glue can be effectively used, such as data warehousing, real-time analytics, and big data processing.
Part 2: Setting Up the Environment
AWS Account Setup: Instructions on setting up an AWS account and configuring necessary permissions.
IAM Roles: Explanation on creating and assigning IAM roles to Glue services for accessing data sources and destinations securely.
Part 3: Creating a Glue Crawler
Data Source Connection: Demonstrating how to connect to a data source (e.g., an S3 bucket) where raw data is stored.
Crawler Configuration: Step-by-step process to configure a Glue Crawler to scan the data source and populate the Glue Data Catalog with metadata.
Running the Crawler: Execution of the crawler and verification of the metadata in the Glue Data Catalog.
Part 4: Developing Glue ETL Jobs
Job Creation: How to create a new Glue ETL job using the AWS Management Console.
Script Editor: Introduction to the script editor within Glue, where ETL scripts are written in Python or Scala.
Job Configuration: Setting up job parameters, including input and output data locations, and specifying the script to use.
Part 5: Advanced Transformations
Transformations Overview: Explanation of various data transformations that can be performed within Glue, such as data filtering, mapping, and aggregation.
Part 6: Loading Transformed Data
Data Destination: Configuring the final destination for the transformed data, such as an S3 bucket, Amazon Redshift, or an RDS instance.
Loading Process: Steps to load the transformed data into the destination and verify its integrity.
Repo Link : github.com/RekhuGopal/PythonH...
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[@strangemate]

How are you able to get the selection of data files viz. covid.json file state.csv from within the Visual ETL section. Where did you place them initially?

[@cloudquicklabs]

Thank you for watching my videos. I shared those source data files, can be kept in your s3 bucket and then you would need to create crawler to Extract data from same and store it in data catalog table as shown in video.