aws glue

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AWS Glue Training is designed for data engineers, ETL developers & cloud professionals who want to master serverless data integration & transformation on AWS. This training covers ETL development, job orchestration & data lake integration using AWS Glue, from basic to advanced concepts. It is essential for those aiming to build scalable, automated & cost-effective data pipelines to support modern analytics & big data solutions in the AWS ecosystem.

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Overview

The Syllabus

Curriculum Designed by Experts

Module 1: Introduction to AWS Glue

• What is AWS Glue? Use Cases
• Glue Architecture & Components Overview
• Comparison with other ETL services (e.g., Data Pipeline, EMR)
• Glue vs Lambda vs Step Functions for ETL
Hands-on:
• Explore AWS Glue Console and understand regions & pricing

Module 2: Data Catalog

• Glue Data Catalog Overview
• Crawlers and their Configuration
• Classifiers and Custom Classifiers
• Table Metadata and Partitions
• Glue Data Catalog Integration with Athena, Redshift, EMR
Hands-on:
• Create Glue Crawlers to catalog S3 data
• Query data using Athena via Glue Catalog

Module 3: Glue Jobs – Python & Spark ETL

• Glue Job Types (Spark, Python Shell, Ray)
• Glue DynamicFrame vs Spark DataFrame
• Connection to RDS, Redshift, S3
• Reading, Writing & Transforming data
• Bookmarking and Job Parameters
• Glue Job Bookmarks and Checkpoints
Hands-on:
• Create a Glue Spark Job to transform JSON to Parquet
• Load data from S3 to Redshift using Glue Job

Module 4: Glue Studio (No-Code/Low-Code Development)

• Introduction to Glue Studio Interface
• Visual Job Creation
• Adding and Testing Transforms
• Monitoring Job Execution & Logs
Hands-on:
• Build an ETL pipeline using Glue Studio without writing code

Module 5: Job Orchestration & Triggers

• Creating Triggers (On-Demand, Scheduled, Conditional)
• Job Dependencies and Event Triggers
• Integration with Step Functions
Hands-on:
• Chain multiple Glue jobs using conditional triggers
• Set up a Step Function to run jobs in sequence

Module 6: Glue DataBrew (Data Wrangling without Code)

• What is AWS Glue DataBrew?
• Creating Projects and Recipes
• Visualizing and Cleaning Data
• Exporting Cleaned Data to S3/Redshift
Hands-on:
• Clean CSV file using Glue DataBrew (null removal, column cleanup)

Module 7: Glue Security & Access Control

• IAM Roles and Policies for Glue
• Encrypting data at rest and in transit
• VPC Endpoints and Glue Networking
• Data Classification for DLP
Hands-on:
• Assign IAM role for S3 access to Glue
• Configure KMS encryption for Glue jobs

Module 8: Monitoring, Logging & Optimization

• Job Metrics and Logs (CloudWatch)
• Debugging Errors in Jobs
• Optimizing Job Execution (Worker Types, DPUs, Retry Policies)
• Cost Optimization Strategies
Hands-on:
• Enable CloudWatch logs and alerts for failed jobs
• Tune Glue job for large file batch processing

Module 9: Advanced Use Cases & Integration

• Partitioning Strategies and Incremental Loads
• Handling Semi-structured Data (JSON, XML)
• Glue with AWS Lake Formation
• Glue with S3 Event Notification
• Glue + Redshift + Athena Pipelines

Capstone Project

Project Scenario:
Design a serverless ETL pipeline using AWS Glue that:
• Crawls and catalogs raw CSV logs from S3
• Cleans and transforms the data into Parquet
• Loads it into Redshift for reporting
• Triggers the ETL job daily via schedule
• Includes monitoring, logging, and failure notifications

Tools & Technologies Covered

• AWS Glue (Jobs, Catalog, Studio, DataBrew)
• S3, Redshift, Athena, RDS
• AWS IAM, CloudWatch, Step Functions
• Python (PySpark in Glue Jobs)

Optional Add-Ons

• Glue + Lake Formation Integration
• Glue + DMS for CDC (Change Data Capture)
• Build ETL using Glue + Apache Hudi or Iceberg

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aws glue Course Certification With Training In Kochi

Q: Capstone Project

Project Scenario: Design a serverless ETL pipeline using AWS Glue that: • Crawls and catalogs raw CSV logs from S3 • Cleans and transforms the data into Parquet • Loads it into Redshift for reporting • Triggers the ETL job daily via schedule • Includes monitoring, logging, and failure notifications

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