Enterprise Data Platforms on AWS Training Course

 Enterprise Data Platforms on AWS Training Course 

Introduction 

Enterprise Data Platforms on AWS Training Course is designed to equip professionals with advanced knowledge and practical skills for designing, developing, deploying, and managing scalable enterprise data platforms using Amazon Web Services (AWS). As organizations increasingly adopt cloud computing, artificial intelligence, machine learning, big data analytics, and data-driven decision-making, enterprise data platforms have become essential for digital transformation and business competitiveness. This course explores modern cloud data architecture, enterprise data integration, data lakes, data warehouses, data pipelines, real-time data processing, data governance, and cloud-native analytics using AWS services. Participants will gain practical insights into building secure, scalable, resilient, and cost-effective enterprise data ecosystems that support business intelligence, predictive analytics, and advanced data management. 

The training also examines enterprise data strategy, metadata management, data quality, information security, regulatory compliance, and automated data engineering within AWS environments. Through practical exercises and international case studies, participants will explore technologies such as Amazon S3, AWS Glue, Amazon Redshift, Amazon Athena, Amazon Kinesis, AWS Lake Formation, and Amazon EMR. The course emphasizes modern data architecture, serverless data engineering, data orchestration, enterprise interoperability, and artificial intelligence readiness. Participants will develop the competencies required to modernize legacy data infrastructure, optimize enterprise analytics, implement cloud data governance frameworks, and establish reliable data platforms that deliver measurable organizational value. 

Course Objectives 

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

1. Design scalable enterprise data platforms using AWS cloud architecture.
2. Develop modern data lake and enterprise data warehouse solutions.
3. Implement cloud-native data integration and engineering pipelines.
4. Configure AWS data storage, processing, and analytics services.
5. Apply enterprise data governance and metadata management frameworks.
6. Implement real-time data streaming and event-driven architectures.
7. Optimize enterprise data processing and query performance.
8. Apply advanced data security, encryption, and access management.
9. Integrate artificial intelligence and machine learning into enterprise data platforms.
10. Implement automated data orchestration and workflow management.
11. Apply data quality monitoring and master data management practices.
12. Develop cost-effective, highly available, and resilient data architectures.
13. Evaluate enterprise data modernization and digital transformation strategies.

Organizational Benefits
 

1. Improved enterprise data management and operational efficiency.
2. Reduced infrastructure and data processing costs.
3. Enhanced business intelligence and predictive analytics.
4. Improved data security and regulatory compliance.
5. Faster enterprise digital transformation initiatives.
6. Better integration of distributed organizational data.
7. Increased scalability and reliability of data infrastructure.
8. Improved data quality and organizational decision-making.
9. Greater readiness for artificial intelligence and machine learning adoption.
10. Strengthened enterprise competitiveness through data-driven innovation.

Target Audiences
 

1. Enterprise data architects and cloud solutions architects.
2. Data engineers and big data professionals.
3. Database administrators and data warehouse specialists.
4. Business intelligence and analytics professionals.
5. Cloud infrastructure and DevOps engineers.
6. Chief information officers and IT managers.
7. Data governance, security, and compliance professionals.
8. Digital transformation consultants and technology project managers.

Course Duration: 5 days

Course Modules

Module 1: Enterprise Data Platform Architecture on AWS
 

• Enterprise cloud data architecture principles and frameworks.
• AWS infrastructure, scalability, and availability.
• Enterprise data platform components and integration.
• Multi-account environments and architectural best practices.
• Data platform modernization strategies.
• Case Study: Netflix's cloud-based data infrastructure and analytics ecosystem.

Module 2: Enterprise Data Lakes and Data Warehousing
 

• Designing enterprise data lakes using Amazon S3.
• Enterprise data warehouse architecture with Amazon Redshift.
• Data lakehouse concepts and implementation.
• Data cataloguing and metadata management.
• Structured, semi-structured, and unstructured data storage.
• Case Study: FINRA's use of cloud data infrastructure for large-scale financial data analytics.

Module 3: Data Integration and Engineering Pipelines
 

• Enterprise data ingestion and integration strategies.
• Building ETL and ELT pipelines using AWS Glue.
• Data transformation and preparation techniques.
• Automating enterprise data workflows.
• Integrating relational and non-relational databases.
• Case Study: Capital One's cloud transformation and enterprise data engineering.

Module 4: Real-Time Data Processing and Streaming
 

• Real-time data architecture and streaming fundamentals.
• Implementing data streams using Amazon Kinesis.
• Event-driven data processing and integration.
• Stream analytics and real-time monitoring.
• Managing high-volume enterprise data streams.
• Case Study: Formula 1's real-time data processing and analytics infrastructure.

Module 5: Enterprise Data Governance and Security
 

• Enterprise data governance frameworks and policies.
• Data access management using AWS IAM.
• Data discovery and governance using AWS Lake Formation.
• Encryption, privacy, and regulatory compliance.
• Data lineage, auditing, and metadata management.
• Case Study: Nasdaq's cloud data management and financial information governance.

Module 6: Enterprise Analytics and Performance Optimization
 

• Enterprise analytics using Amazon Athena.
• Distributed processing with Amazon EMR.
• Query optimization and workload management.
• Integrating business intelligence platforms.
• Monitoring and optimizing enterprise data workloads.
• Case Study: Airbnb's data analytics infrastructure and large-scale business intelligence.

Module 7: Automation, AI and Machine Learning Integration
 

• Data pipeline automation and orchestration.
• Integrating Amazon SageMaker with enterprise data platforms.
• Machine learning data preparation and feature engineering.
• Predictive analytics and intelligent data processing.
• Infrastructure automation and continuous integration.
• Case Study: Siemens' industrial data analytics and artificial intelligence applications.

Module 8: Enterprise Data Platform Operations and Modernization
 

• Enterprise data platform monitoring and maintenance.
• High availability, backup, and disaster recovery.
• AWS cost optimization and resource management.
• Legacy data platform migration strategies.
• Enterprise architecture assessment and future readiness.
• Case Study: General Electric's industrial digital transformation and cloud-based data modernization.

Training Methodology
 

• Instructor-led presentations and interactive technical discussions.
• Practical AWS cloud configuration and architecture exercises.
• Hands-on enterprise data engineering and integration projects.
• International case study analysis and group assignments.
• Demonstrations of AWS data management and analytics services.
• Collaborative problem-solving and enterprise architecture simulations.
• Practical assessments and end-of-course 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.