Introduction

In today’s fast-paced business environment, organizations are faced with rapidly growing volumes of data that are distributed across diverse systems, including on-premises, cloud, and hybrid environments. Data Fabric is an emerging architecture that provides a unified data platform, simplifying data integration, accessibility, and governance, enabling organizations to leverage their data more efficiently and securely. This course will equip professionals with the skills and knowledge needed to design, implement, and manage a Data Fabric solution for optimal enterprise data management.

Objectives

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

• Understand the concept of Data Fabric and its role in modern enterprise data management.
• Design and implement a Data Fabric architecture to integrate, govern, and access data from multiple sources.
• Optimize data workflows across hybrid, multi-cloud, and on-premises environments.
• Implement automated data discovery, data quality, and data security within a Data Fabric framework.
• Leverage AI and machine learning for intelligent data management and insights.
• Overcome common challenges in implementing Data Fabric and ensure long-term success.

Who Should Attend?

This course is ideal for:

• Data architects and engineers
• IT managers overseeing data infrastructure
• Chief Data Officers (CDOs) and data governance professionals
• Enterprise architects and cloud architects
• Data analysts and business intelligence specialists
• Professionals involved in enterprise data integration and optimization

Course Outline

Day 1: Introduction to Data Fabric and Enterprise Data Management

• What is Data Fabric?
• Key Principles of Data Fabric Architecture: Unified Data Access, Integration, and Governance
• Traditional Data Management vs. Data Fabric Approach
• The Role of Data Fabric in Multi-Cloud, Hybrid, and On-Premises Environments
• Benefits of Data Fabric for Scalability, Agility, and Operational Efficiency
• Hands-on Activity: Mapping Data Sources and Systems to a Data Fabric Framework

Day 2: Designing and Implementing Data Fabric Architecture

• Key Components of Data Fabric: Data Integration, Data Virtualization, Metadata Management
• Data Access Layer: Ensuring Seamless Connectivity Across Data Sources
• Data Integration Strategies: Batch vs. Real-Time, ETL vs. ELT
• Data Virtualization for Simplified Data Access and Reduced Redundancy
• Implementing Data Fabric Using Cloud Platforms and Hybrid Environments
• Workshop: Designing a Scalable Data Fabric Architecture for an Enterprise

Day 3: Data Governance, Quality, and Security in Data Fabric

• Federated Data Governance in Data Fabric: Shared Policies and Centralized Control
• Ensuring Data Quality and Consistency Across Distributed Systems
• Data Lineage and Provenance: Tracking Data Across Sources and Storing Metadata
• Data Privacy and Security in Data Fabric: Protecting Sensitive Data
• Compliance Considerations: GDPR, CCPA, HIPAA, and Other Regulations
• Hands-on Session: Implementing Data Governance and Quality Frameworks in Data Fabric

Day 4: Leveraging AI, Automation, and Intelligent Data Management in Data Fabric

• The Role of AI and Machine Learning in Data Fabric for Smart Data Management
• Automated Data Discovery: Identifying, Classifying, and Tagging Data Automatically
• Predictive Data Analytics and Intelligent Data Enrichment
• Automation in Data Processing and Workflow Management
• Real-Time Data Processing and Insights with Data Fabric
• Case Study: AI-Powered Data Fabric in Action
• Workshop: Implementing AI and Automation for Data Management Optimization

Day 5: Best Practices, Tools, and Future Trends in Data Fabric Implementation

• Key Tools and Technologies for Building and Managing Data Fabric (Apache Kafka, Snowflake, etc.)
• Best Practices for Data Fabric Implementation and Scaling Across the Enterprise
• Overcoming Common Challenges: Data Silos, Complex Infrastructure, Resistance to Change
• Managing Data Fabric for Continuous Integration and Long-Term Success
• Future Trends in Data Fabric: Edge Computing, IoT, and Emerging Technologies
• Final Project: Developing an Implementation Roadmap for Data Fabric in an Organization