Data Engineering with AWS Glue Training Course

 Data Engineering with AWS Glue Training Course 

Introduction 

Data Engineering with AWS Glue Training Course is designed to equip participants with practical skills and advanced knowledge in cloud-based data engineering, serverless data integration, and big data processing using Amazon Web Services (AWS). The course explores how organizations can leverage AWS Glue to build scalable, automated, and cost-effective Extract, Transform, and Load (ETL) pipelines for managing complex data environments. Participants will gain expertise in data ingestion, data transformation, data cataloguing, schema discovery, workflow orchestration, and cloud data lake development. Emphasis is placed on modern data engineering practices, Apache Spark, Python, AWS Glue Studio, Amazon S3, and Amazon Athena to support real-time analytics, business intelligence, and data-driven decision-making. 

The training also examines enterprise data architecture, metadata management, data quality, security, and automated pipeline monitoring within modern cloud ecosystems. Through practical exercises and international case studies, participants will explore how organizations use AWS Glue to integrate structured and unstructured data from multiple sources, optimize data processing, and support machine learning and artificial intelligence applications. The course develops competencies in designing reliable serverless data pipelines, implementing data governance frameworks, improving data processing performance, and managing enterprise-scale data workloads. Participants will acquire industry-relevant capabilities for implementing cloud-native data engineering solutions across financial services, healthcare, telecommunications, logistics, e-commerce, and public sector organizations. 

Course Objectives 

By the end of this course, participants will be able to: 

1. Understand cloud-based data engineering and AWS Glue architecture.
2. Develop automated Extract, Transform, and Load (ETL) pipelines.
3. Configure AWS Glue Data Catalog and metadata management.
4. Implement scalable data lake architectures using Amazon S3.
5. Apply Python and Apache Spark for data transformation.
6. Perform data ingestion from multiple enterprise sources.
7. Implement schema discovery and automated data classification.
8. Design serverless data integration workflows.
9. Optimize data processing performance and resource utilization.
10. Apply data quality validation and monitoring techniques.
11. Implement cloud data security and access management.
12. Integrate AWS Glue with Amazon Athena and Amazon Redshift.
13. Develop production-ready data engineering solutions.

Organizational Benefits
 

1. Reduced operational costs through serverless data processing.
2. Improved enterprise data integration and accessibility.
3. Faster analytics and business intelligence delivery.
4. Enhanced data governance and regulatory compliance.
5. Increased automation of enterprise ETL processes.
6. Improved data quality and consistency.
7. Scalable cloud-based data infrastructure.
8. Better decision-making through integrated datasets.
9. Reduced manual data engineering workloads.
10. Improved organizational competitiveness through cloud innovation.

Target Audiences
 

1. Data engineers and cloud data architects.
2. Database administrators and developers.
3. Big data analysts and data scientists.
4. Business intelligence professionals.
5. Cloud computing and AWS administrators.
6. Information technology managers.
7. Enterprise data integration specialists.
8. Machine learning and analytics engineers.

Course Duration: 5 days

Course Modules

Module 1: Fundamentals of AWS Glue and Data Engineering
 

• Introduction to modern data engineering and cloud computing.
• AWS Glue architecture, components, and serverless capabilities.
• AWS Glue jobs, crawlers, triggers, and development endpoints.
• Data engineering lifecycle and enterprise integration.
• AWS global infrastructure and cloud data services.
• Case study: Netflix-style cloud data integration and large-scale data processing.

Module 2: AWS Glue Data Catalog and Metadata Management
 

• Configuring databases, tables, and metadata repositories.
• Creating and managing AWS Glue crawlers.
• Automated schema discovery and classification.
• Schema evolution and metadata synchronization.
• Integrating Data Catalog with Amazon Athena.
• Case study: Airbnb-style metadata management for distributed datasets.

Module 3: Data Ingestion and Amazon S3 Data Lakes
 

• Ingesting structured and unstructured enterprise data.
• Connecting Amazon S3 to AWS Glue.
• Data lake architecture and storage organization.
• Working with CSV, JSON, Parquet, and Avro formats.
• Incremental data ingestion and partition management.
• Case study: Amazon-style scalable data lake implementation.

Module 4: ETL Development Using Python and Apache Spark
 

• Designing automated ETL pipelines.
• Developing AWS Glue scripts using Python.
• Apache Spark DataFrames and DynamicFrames.
• Data cleansing, filtering, joining, and transformation.
• Handling complex datasets and schema inconsistencies.
• Case study: Spotify-style large-scale data transformation and analytics.

Module 5: AWS Glue Studio and Workflow Orchestration
 

• Developing visual ETL pipelines using AWS Glue Studio.
• Configuring jobs, triggers, and workflow dependencies.
• Automating data processing schedules.
• Integrating AWS Lambda and Amazon EventBridge.
• Managing pipeline failures and workflow recovery.
• Case study: Retail enterprise automation of inventory data workflows.

Module 6: Data Quality, Security, and Performance Optimization
 

• Implementing AWS Glue Data Quality rules.
• Identifying missing, duplicate, and inconsistent records.
• Applying AWS Identity and Access Management (IAM).
• Optimizing Spark jobs and partition strategies.
• Monitoring execution logs and resource consumption.
• Case study: Financial services data validation and secure processing.

Module 7: AWS Glue Integration and Advanced Analytics
 

• Integrating AWS Glue with Amazon Redshift.
• Querying data through Amazon Athena.
• Connecting enterprise databases and external data sources.
• Supporting machine learning data preparation.
• Implementing incremental processing and job bookmarks.
• Case study: Healthcare analytics integration using cloud data pipelines.

Module 8: Enterprise Data Engineering Projects and Deployment
 

• Designing end-to-end cloud data engineering architectures.
• Developing production-ready ETL workflows.
• Implementing pipeline testing and deployment practices.
• Applying infrastructure automation and version control.
• Establishing monitoring, documentation, and governance.
• Case study: Global e-commerce data lake and analytics implementation.

Training Methodology
 

• Instructor-led presentations and interactive technical discussions.
• Practical AWS Glue configuration and cloud laboratory exercises.
• Hands-on Python, Apache Spark, and ETL pipeline development.
• Real-world enterprise projects and international case studies.
• Group assignments, demonstrations, and collaborative problem-solving.
• Practical assessments, knowledge checks, and final project presentations.

Register as a group from 3 participants for a Discount

Send us an email: info@datastatresearch.org or call +254724527104

Certification

Upon successful completion of this training, participants will be issued with a globally- recognized certificate.

Tailor-Made Course

We also offer tailor-made courses based on your needs.

Key Notes

a. The participant must be conversant with English.
 
b. Upon completion of training the participant will be issued with an Authorized Training Certificate
 
c. Course duration is flexible and the contents can be modified to fit any number of days.
 
d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.
 
e. One-year post-training support Consultation and Coaching provided after the course.
 
f. Payment should be done at least a week before commence of the training, to DATASTAT CONSULTANCY LTD account, as indicated in the invoice so as to enable us prepare better for you.