Big Data Platforms and Enterprise Architecture Training Course

 Big Data Platforms and Enterprise Architecture Training Course 

Introduction 

Big Data Platforms and Enterprise Architecture Training Course is designed to equip professionals with advanced knowledge and practical skills in big data management, enterprise architecture development, data integration, and digital transformation. As organizations increasingly adopt data-driven business models, cloud computing, artificial intelligence, machine learning, and advanced analytics, the ability to manage complex data ecosystems has become essential for operational efficiency and strategic decision-making. This course explores modern big data platforms, enterprise data architecture, distributed computing, data governance, data warehousing, data lakes, cloud-native technologies, and scalable information management systems. Participants will gain practical competencies in designing secure, interoperable, and scalable enterprise architecture frameworks that support business intelligence, predictive analytics, digital innovation, and organizational growth. 

The training also examines emerging enterprise technology trends, including hybrid cloud architecture, data mesh, data fabric, real-time analytics, Internet of Things (IoT), and artificial intelligence-powered data management. Through practical exercises and international case studies, participants will learn how to align information technology infrastructure with organizational objectives, optimize data pipelines, implement enterprise integration strategies, and establish effective data governance frameworks. The course emphasizes industry best practices, enterprise architecture frameworks, technology modernization, cybersecurity, and digital transformation strategies used by leading global organizations. Participants will develop the capabilities required to plan, implement, and manage enterprise-scale big data solutions while improving business agility, innovation, regulatory compliance, and long-term technological sustainability. 

Course Objectives 

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

1. Understand big data ecosystems, emerging technologies, and enterprise architecture principles.
2. Design scalable big data platforms using distributed computing technologies.
3. Apply enterprise architecture frameworks to digital transformation initiatives.
4. Develop cloud-based and hybrid enterprise data architecture solutions.
5. Implement data governance, data quality, and master data management frameworks.
6. Design enterprise data lakes, data warehouses, and lakehouse architectures.
7. Apply real-time data processing and advanced analytics technologies.
8. Integrate artificial intelligence and machine learning into enterprise data systems.
9. Implement secure data management and enterprise cybersecurity strategies.
10. Evaluate data integration, interoperability, and application modernization approaches.
11. Develop enterprise architecture roadmaps aligned with business objectives.
12. Apply data mesh, data fabric, and metadata management principles.
13. Monitor enterprise data infrastructure performance and optimize technology investments.

Organizational Benefits
 

1. Improved organizational data management and operational efficiency.
2. Enhanced data-driven decision-making and business intelligence.
3. Reduced infrastructure costs through optimized cloud architecture.
4. Improved enterprise-wide data integration and interoperability.
5. Strengthened cybersecurity and regulatory compliance.
6. Increased scalability and flexibility of information systems.
7. Better collaboration between business and information technology departments.
8. Enhanced innovation through artificial intelligence and advanced analytics.
9. Improved data quality, accessibility, and governance.
10. Stronger digital transformation and competitive advantage.

Target Audiences
 

1. Chief Information Officers and Chief Technology Officers.
2. Enterprise architects and solution architects.
3. Big data engineers and data architects.
4. Data scientists and business intelligence analysts.
5. Information technology managers and systems administrators.
6. Cloud architects and infrastructure engineers.
7. Digital transformation consultants and project managers.
8. Database administrators and data governance professionals.

Course Duration: 5 days

Course Modules

Module 1: Introduction to Big Data Platforms and Enterprise Architecture
 

• Big data concepts, characteristics, ecosystems, and emerging industry trends.
• Enterprise architecture principles, components, and organizational applications.
• Big data architecture layers and distributed computing environments.
• Relationship between enterprise architecture and digital transformation.
• Overview of Hadoop, Apache Spark, and modern data platforms.
• Case study: Amazon's big data ecosystem and enterprise-scale data processing.

Module 2: Big Data Infrastructure and Platform Technologies
 

• Distributed storage systems and parallel processing architectures.
• Hadoop Distributed File System (HDFS) and resource management.
• Apache Spark, Apache Kafka, and Apache Flink applications.
• NoSQL databases, document stores, and distributed databases.
• Containerization, Kubernetes, and microservices infrastructure.
• Case study: Netflix's use of distributed cloud infrastructure and data platforms.

Module 3: Enterprise Architecture Frameworks and Governance
 

• Enterprise architecture development and implementation principles.
• The Open Group Architecture Framework (TOGAF) and Architecture Development Method.
• Business, data, application, and technology architecture domains.
• Enterprise architecture governance and stakeholder management.
• Architecture documentation, capability mapping, and compliance.
• Case study: United States federal enterprise architecture modernization initiatives.

Module 4: Cloud Computing and Enterprise Data Architecture
 

• Cloud-native architecture and enterprise cloud adoption strategies.
• Public, private, hybrid, and multi-cloud infrastructure.
• Amazon Web Services, Microsoft Azure, and Google Cloud Platform.
• Cloud data storage, processing, and enterprise integration.
• Cloud security, scalability, disaster recovery, and cost optimization.
• Case study: Capital One's cloud transformation and enterprise technology modernization.

Module 5: Data Warehousing, Data Lakes, and Data Integration
 

• Enterprise data warehouse design and dimensional modeling.
• Data lakes, lakehouse architecture, and modern storage solutions.
• Extract, Transform, Load (ETL) and Extract, Load, Transform (ELT) pipelines.
• Data integration, interoperability, and application programming interfaces.
• Metadata management, data catalogues, and data lineage.
• Case study: Walmart's enterprise data infrastructure and large-scale retail analytics.

Module 6: Data Governance, Security, and Enterprise Compliance
 

• Enterprise data governance frameworks and management policies.
• Data quality assessment, validation, and lifecycle management.
• Data privacy, access controls, encryption, and identity management.
• Regulatory compliance, audit trails, and risk management.
• Master data management and enterprise metadata standards.
• Case study: European financial institutions implementing General Data Protection Regulation (GDPR) compliance.

Module 7: Advanced Analytics, Artificial Intelligence, and Real-Time Processing
 

• Real-time analytics and streaming data processing architectures.
• Artificial intelligence and machine learning integration.
• Predictive analytics, business intelligence, and data visualization.
• Internet of Things data management and intelligent automation.
• Data mesh, data fabric, and emerging analytical architectures.
• Case study: Singapore's Smart Nation initiative and data-driven urban services.

Module 8: Enterprise Architecture Implementation and Optimization
 

• Enterprise architecture planning and implementation roadmaps.
• Technology modernization and legacy system integration.
• Enterprise architecture performance monitoring and evaluation.
• Business-IT alignment and strategic technology investment.
• Innovation management, scalability, and continuous improvement.
• Case study: DBS Bank's digital transformation and enterprise technology modernization.

Training Methodology
 

• Interactive presentations and expert-led technical discussions.
• Practical demonstrations of big data platforms and enterprise architecture tools.
• Hands-on exercises involving data integration and distributed processing.
• Group assignments on enterprise architecture development.
• International case study analysis and industry benchmarking.
• Practical simulations, knowledge assessments, and group 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.