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

=================================================================== 1. SUBSCRIBE FOR MORE LEARNING : https://www.seevid.ir/fa/result?ytch=UCv9MUffHWyo2GgLIDLVu0KQ =================================================================== 2. CLOUD QUICK LABS - CHANNEL MEMBERSHIP FOR MORE BENEFITS : =================================================================== 3. BUY ME A COFFEE AS A TOKEN OF APPRECIATION : https://www.buymeacoffee.com/cloudquicklabs =================================================================== 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 : https://github.com/RekhuGopal/PythonHacks/tree/main/AWS_Advanced_Data_Transformation_With_Glue #aws #etl #glue #cloudquicklabs #datatransformation #dataengineering #data #aws #awscloud #awsglue #glueetl #dataengineering #datapipeline #etl #cloudcomputing #bigdata #datascience #dataanalytics #serverless #awstutorial #cloudtutorial #awsetl #datatransformation #advancedetl #pythonetl #scalacode #clouddata #automation #datavalidation #dataquality #awscrawler #gluecrawler #gluejob #datawarehouse #amazonredshift #s3 #awsrds #dataintegration #datawrangling #dataprocessing #cloudetl #awstrigger #workflowautomation #cloudstorage #dataaggregation #datafiltering #datamapping #awssecurity #awspermissions #iamroles #datasource #datadestination #awsmanagementconsole #cloudservices #cloudsolutions #awssolutions #cloudarchitecture #cloudplatform #clouddataengineering #etlworkflow #datasynchronization #datapreparation #cloudintegration

• #science_technology
• #cloudquicklabs