AWS Data Engineering and Big Data Solutions Training Course

 AWS Data Engineering and Big Data Solutions Training Course 

Introduction 

AWS Data Engineering and Big Data Solutions Training Course is designed to equip participants with practical knowledge and technical expertise in designing, developing, deploying, and managing scalable cloud-based data engineering solutions using Amazon Web Services (AWS). The course explores modern data architecture, big data analytics, cloud data lakes, data warehousing, data integration, and real-time data processing. Participants will gain practical skills in leveraging AWS services such as Amazon S3, AWS Glue, Amazon Redshift, Amazon EMR, Amazon Kinesis, AWS Lambda, and Amazon Athena to build secure, reliable, and cost-effective data pipelines. Emphasis is placed on cloud-native data engineering, data governance, automation, distributed computing, and enterprise data management. 

As organizations increasingly adopt artificial intelligence, machine learning, and data-driven decision-making, the demand for professionals capable of managing large-scale cloud data environments continues to grow. This training provides hands-on exposure to modern big data technologies, ETL and ELT workflows, serverless computing, data lakehouse architecture, and advanced analytics. Participants will explore practical industry applications and global case studies involving financial services, healthcare, telecommunications, retail, and logistics. By the end of the course, participants will be equipped to implement scalable AWS data engineering solutions, optimize cloud data infrastructure, strengthen data security, and support digital transformation initiatives. 

Course Objectives 

1. Understand AWS cloud architecture and modern data engineering principles.
2. Design scalable big data processing and storage solutions.
3. Develop automated ETL and ELT data pipelines.
4. Implement AWS Glue for data integration and transformation.
5. Configure Amazon S3 data lakes and cloud storage.
6. Apply Amazon Redshift for enterprise data warehousing.
7. Utilize Amazon EMR for distributed big data processing.
8. Implement real-time data streaming using Amazon Kinesis.
9. Develop serverless data workflows using AWS Lambda.
10. Apply SQL and Amazon Athena for cloud data analytics.
11. Implement data governance, encryption, and access management.
12. Optimize cloud performance and infrastructure costs.
13. Integrate AWS analytics services with business intelligence and machine learning solutions.

Organizational Benefits
 

1. Improved enterprise data management and accessibility.
2. Reduced operational costs through cloud optimization.
3. Faster business intelligence and reporting processes.
4. Enhanced data security and regulatory compliance.
5. Scalable infrastructure for growing data volumes.
6. Improved real-time analytics and decision-making.
7. Automated data integration and processing workflows.
8. Increased organizational innovation and competitiveness.
9. Better data quality and operational efficiency.
10. Stronger digital transformation and cloud adoption capabilities.

Target Audiences
 

1. Data engineers and big data professionals.
2. Cloud architects and AWS administrators.
3. Database administrators and developers.
4. Business intelligence and analytics specialists.
5. IT managers and digital transformation leaders.
6. Machine learning and artificial intelligence professionals.
7. Data scientists and research analysts.
8. Software engineers and cloud computing consultants.

Course Duration: 5 days

Course Modules

Module 1: AWS Cloud Computing and Data Engineering Fundamentals
 

• AWS architecture, cloud infrastructure, and data engineering principles.
• AWS Identity and Access Management and cloud security fundamentals.
• AWS compute, storage, networking, and database services.
• Cloud-native data engineering architecture and deployment.
• Data engineering lifecycle and distributed data environments.
• Case Study: Netflix's use of AWS cloud infrastructure for large-scale data processing.

Module 2: AWS Data Lakes and Cloud Storage Solutions
 

• Amazon S3 buckets, storage classes, and lifecycle management.
• Data lake architecture and centralized data repositories.
• Structured, semi-structured, and unstructured data storage.
• Data partitioning, cataloguing, and metadata management.
• Data lake security, encryption, and access controls.
• Case Study: Capital One's adoption of cloud-based data platforms.

Module 3: ETL, ELT and AWS Data Integration
 

• ETL and ELT architecture and implementation strategies.
• AWS Glue crawlers, Data Catalog, and data transformation.
• Data ingestion, cleansing, validation, and transformation.
• Apache Spark-based data processing workflows.
• Automated pipeline orchestration and monitoring.
• Case Study: Airbnb's data integration and analytics ecosystem.

Module 4: Big Data Processing with Amazon EMR
 

• Distributed computing and Hadoop ecosystem fundamentals.
• Apache Spark and Apache Hive implementation.
• Amazon EMR cluster configuration and management.
• Large-scale batch processing and workload optimization.
• Resource allocation, fault tolerance, and performance tuning.
• Case Study: Yelp's application of big data technologies for analytics.

Module 5: AWS Data Warehousing and Advanced Analytics
 

• Amazon Redshift architecture and data warehouse design.
• SQL querying, data modelling, and optimization.
• Amazon Athena for serverless data analytics.
• Data visualization and business intelligence integration.
• Query performance, workload management, and cost optimization.
• Case Study: Nasdaq's use of cloud data solutions for financial analytics.

Module 6: Real-Time Data Streaming and Serverless Processing
 

• Amazon Kinesis Data Streams and data ingestion.
• Real-time analytics and event-driven architectures.
• AWS Lambda for serverless data processing.
• Stream processing, event monitoring, and automation.
• Integration of streaming data with AWS storage services.
• Case Study: Formula 1's use of AWS for real-time racing data analytics.

Module 7: Data Governance, Security and Quality Management
 

• AWS data governance frameworks and compliance.
• AWS Lake Formation and centralized data permissions.
• Data quality monitoring and validation techniques.
• Encryption, identity management, and audit logging.
• Data lineage, metadata management, and risk mitigation.
• Case Study: Financial institutions implementing secure cloud data governance.

Module 8: AWS Data Engineering Projects and Optimization
 

• Designing end-to-end AWS data engineering pipelines.
• Integrating storage, processing, and analytics services.
• Infrastructure automation and deployment strategies.
• Cloud monitoring, troubleshooting, and cost optimization.
• Performance evaluation and enterprise solution development.
• Case Study: Spotify's data-driven analytics and scalable data infrastructure.

Training Methodology
 

• Instructor-led presentations and interactive technical discussions.
• Practical AWS cloud laboratory exercises and demonstrations.
• Hands-on development of ETL and ELT data pipelines.
• Group assignments involving real-world big data scenarios.
• International industry case studies and best-practice analysis.
• Practical projects, technical assessments, and participant 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.