DFAB1: Data Fabric - Smart Data Engineering, Operations, and Orchestration Course Overview

DFAB1: Data Fabric - Smart Data Engineering, Operations, and Orchestration Course Overview

The DFAB1: Data Fabric - Smart Data Engineering, Operations, and Orchestration course is an in-depth program designed to educate learners on the complexities of building and managing a modern data fabric architecture. It covers a broad range of topics including the design, deployment, and management of data across various storage, processing, and analytics platforms. Through a comprehensive curriculum spanning 12 modules, students will gain insights into data fabric benefits, architecture, security, and governance, as well as learn about deployment strategies, data management, orchestration, and integration with cloud platforms.

Learners will also explore best practices for data fabric storage, analytics, automation, performance tuning, and monitoring. This course is ideal for professionals looking to enhance their skills in handling big data environments and ensure seamless data availability and quality across an organization. By the end of the course, participants will be equipped with the knowledge to optimize data fabric systems for improved data interoperability and compliance, ensuring that data-driven decisions are made efficiently and securely.

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  • Live Online Training (Duration : 16 Hours)
  • Per Participant
  • Guaranteed-to-Run (GTR)
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♱ Excluding VAT/GST

Classroom Training price is on request

  • Live Online Training (Duration : 16 Hours)
  • Per Participant

♱ Excluding VAT/GST

Classroom Training price is on request

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Course Prerequisites

The DFAB1: Data Fabric - Smart Data Engineering, Operations, and Orchestration course is designed to provide comprehensive knowledge and skills for managing and operating data fabric solutions. To ensure that participants can fully benefit from the training and are prepared for the course content, the following prerequisites are recommended:


  • Basic understanding of data management concepts, including data storage, data processing, and data analysis.
  • Familiarity with database systems and concepts, such as SQL or NoSQL databases.
  • Knowledge of data integration and ETL (Extract, Transform, Load) processes.
  • Experience with cloud computing platforms (e.g., AWS, Azure, or Google Cloud) is beneficial but not mandatory.
  • Awareness of security principles related to IT systems and data protection.
  • An understanding of basic networking concepts and distributed systems architecture can be helpful.
  • Some experience with scripting or programming languages (e.g., Python, Java, or Bash) to follow automation and orchestration lessons.

These prerequisites are meant to provide a foundation upon which the course will build. They are not intended to be barriers to entry but rather to set the stage for a productive and enriching learning experience. Individuals with a genuine interest in data fabric and a willingness to learn will find this course accessible and valuable for their professional development.


Target Audience for DFAB1: Data Fabric - Smart Data Engineering, Operations, and Orchestration

The DFAB1 course offers in-depth knowledge on data fabric design, security, and management, ideal for IT professionals in data-centric roles.


  • Data Engineers
  • Data Architects
  • Database Administrators
  • IT Security Specialists
  • Cloud Solutions Architects
  • Data Governance Officers
  • IT Operations Managers
  • Big Data Analysts
  • Machine Learning Engineers
  • Data Science Professionals
  • Business Intelligence Analysts
  • System Integrators
  • Data Storage Specialists
  • Cloud Platform Administrators
  • IT Compliance and Risk Officers
  • Network and Systems Administrators looking to specialize in data solutions
  • IT Consultants focusing on enterprise data strategies and solutions
  • Technical Project Managers overseeing data management projects
  • DevOps Engineers working with data pipelines and orchestration
  • Enterprise Architects planning and designing data-driven architectures


Learning Objectives - What you will Learn in this DFAB1: Data Fabric - Smart Data Engineering, Operations, and Orchestration?

Introduction to DFAB1: Data Fabric - Smart Data Engineering, Operations, and Orchestration Course Learning Outcomes:

This comprehensive course offers a deep dive into the intricacies of data fabric, covering aspects from architecture to analytics, security to storage, and integration to automation, equipping students with the expertise to manage and optimize data ecosystems effectively.

Learning Objectives and Outcomes:

  • Understand the concept and benefits of a Data Fabric and how it facilitates data integration, management, and accessibility across an organization.
  • Gain knowledge of Data Fabric architecture, including its components and design principles, to design robust and scalable data systems.
  • Learn about the security measures and governance models necessary to protect data and comply with regulations within a Data Fabric environment.
  • Develop skills to deploy, manage, and orchestrate Data Fabric solutions, ensuring high availability, disaster recovery, and efficient data pipelines.
  • Master the process of data quality management, monitoring, and analytics to drive insights and value from disparate data sources.
  • Explore various storage strategies, understanding how to optimize performance, ensure data protection, and implement best practices in Data Fabric storage solutions.
  • Acquire the ability to automate tasks within a Data Fabric, including deployment, monitoring, and maintenance, to increase efficiency and reduce human error.
  • Discover how to incorporate cloud platforms with Data Fabric solutions to enhance scalability, performance, and cost-effectiveness.
  • Learn to troubleshoot and maintain Data Fabric systems, ensuring they remain performant, secure, and aligned with business objectives.
  • Engage with real-world scenarios and use cases that illustrate the application of Data Fabric technologies in different industry contexts.