AWS Analytics and Data Pipeline Engineering Training Course

 AWS Analytics and Data Pipeline Engineering Training Course 

Introduction 

AWS Analytics and Data Pipeline Engineering Training Course is designed to equip participants with practical knowledge and technical skills for building, managing, and optimizing modern cloud-based data analytics infrastructures using Amazon Web Services (AWS). The course explores advanced data engineering concepts, including data ingestion, data integration, ETL and ELT processing, data warehousing, data lakes, real-time data streaming, big data analytics, and cloud data pipeline automation. Participants will gain hands-on experience with essential AWS analytics services such as Amazon S3, AWS Glue, Amazon Redshift, Amazon Kinesis, Amazon Athena, AWS Lambda, and Amazon QuickSight to develop scalable, secure, and cost-effective data solutions. 

The training also focuses on modern data architecture, serverless data engineering, data governance, metadata management, data quality, and enterprise analytics. Through practical exercises and global industry case studies, participants will learn how to design reliable data pipelines, implement automated workflows, optimize analytical queries, and integrate diverse data sources for business intelligence and data-driven decision-making. The course incorporates real-world applications from financial services, healthcare, e-commerce, telecommunications, and logistics, preparing professionals to implement enterprise-grade AWS analytics solutions and support digital transformation initiatives. 

Course Objectives 

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

1. Design scalable AWS cloud analytics and modern data engineering architectures.
2. Develop automated ETL and ELT data processing pipelines.
3. Implement cloud-based data ingestion and integration solutions.
4. Configure Amazon S3 data lakes and enterprise storage environments.
5. Apply AWS Glue for serverless data transformation and cataloguing.
6. Build real-time data streaming solutions using Amazon Kinesis.
7. Develop data warehouses using Amazon Redshift.
8. Perform serverless SQL analytics using Amazon Athena.
9. Implement data orchestration and workflow automation.
10. Apply data governance, security, and compliance frameworks.
11. Optimize cloud data pipeline performance and operational costs.
12. Integrate business intelligence and data visualization solutions.
13. Implement monitoring, troubleshooting, and data quality management.

Organizational Benefits
 

1. Improved enterprise data integration and accessibility.
2. Reduced operational costs through cloud automation.
3. Enhanced real-time business intelligence capabilities.
4. Improved data quality and consistency.
5. Faster and more informed business decision-making.
6. Scalable enterprise analytics infrastructure.
7. Strengthened data security and governance.
8. Increased operational efficiency and productivity.
9. Improved forecasting and predictive analytics.
10. Accelerated digital transformation initiatives.

Target Audiences
 

1. Data engineers and cloud engineers.
2. Data scientists and analytics professionals.
3. Business intelligence developers and analysts.
4. Database administrators and data architects.
5. AWS cloud solutions architects.
6. IT managers and enterprise technology professionals.
7. Software developers and DevOps engineers.
8. Big data specialists and business intelligence consultants.

Course Duration: 5 days

Course Modules

Module 1: AWS Analytics Architecture and Data Engineering Fundamentals
 

• Introduction to AWS analytics and cloud data engineering.
• Modern data architectures, data lakes, and data warehouses.
• AWS analytics services and infrastructure integration.
• Designing scalable enterprise data pipeline architectures.
• Cloud security, availability, and performance principles.
• Case study: Netflix's cloud-based data analytics architecture.

Module 2: Data Ingestion and Storage Using Amazon S3
 

• Data ingestion techniques and source connectivity.
• Amazon S3 buckets, storage classes, and lifecycle policies.
• Structured, semi-structured, and unstructured data management.
• Data partitioning and storage optimization.
• Implementing secure data lake environments.
• Case study: Airbnb's data storage and analytics infrastructure.

Module 3: ETL and ELT Engineering with AWS Glue
 

• Introduction to ETL and ELT pipeline development.
• AWS Glue crawlers, jobs, and Data Catalog.
• Data cleansing, transformation, and validation.
• Apache Spark-based distributed data processing.
• Automated data integration and pipeline scheduling.
• Case study: Financial services data integration using AWS Glue.

Module 4: Data Warehousing with Amazon Redshift
 

• Amazon Redshift architecture and configuration.
• Data warehouse modelling and schema design.
• SQL analytics and complex query development.
• Query optimization and workload management.
• Data warehouse security and performance monitoring.
• Case study: Nasdaq's cloud data warehousing and analytics applications.

Module 5: Real-Time Data Streaming with Amazon Kinesis
 

• Introduction to real-time data streaming architecture.
• Amazon Kinesis Data Streams and Firehose.
• Event-driven data ingestion and processing.
• Streaming analytics and real-time data transformation.
• Monitoring streaming applications and pipeline reliability.
• Case study: Real-time financial transaction monitoring.

Module 6: Serverless Analytics and Data Orchestration
 

• Serverless analytics using Amazon Athena.
• AWS Lambda for event-driven data processing.
• Workflow automation using AWS Step Functions.
• Pipeline orchestration, scheduling, and dependencies.
• Implementing automated error handling and recovery.
• Case study: E-commerce order processing and analytics automation.

Module 7: Data Governance, Security, and Quality Management
 

• AWS Identity and Access Management (IAM) and access controls.
• Data encryption and enterprise security practices.
• Metadata management and data cataloguing.
• Data quality validation and anomaly detection.
• Governance, compliance, and audit management.
• Case study: Healthcare data governance and secure analytics.

Module 8: Business Intelligence, Monitoring, and Optimization
 

• Data visualization using Amazon QuickSight.
• Developing interactive dashboards and analytical reports.
• Pipeline monitoring using Amazon CloudWatch.
• Performance optimization and cloud cost management.
• Implementing enterprise analytics best practices.
• Case study: Retail analytics and customer intelligence using AWS.

Training Methodology
 

• Instructor-led presentations and technical demonstrations.
• Practical AWS laboratory exercises and guided projects.
• Real-world enterprise data engineering case studies.
• Group discussions and collaborative problem-solving.
• Hands-on ETL, ELT, and data pipeline development.
• Practical assessments and knowledge evaluation.

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.