Overview

This course focuses on the enterprise aspects of managing and optimizing big data systems. Learners will implement governance frameworks, configure security controls, and apply data protection strategies. You’ll also master performance optimization, scaling strategies, and cost management practices to ensure enterprise-grade deployments. By the end of this course, you will be able to: - Implement governance, cataloging, and lineage tracking - Configure security and compliance for big data systems - Optimize queries, caching, and workload performance - Apply scaling and cost optimization strategies in Azure Tools & Software: Microsoft Purview, Azure Active Directory, Azure Key Vault, Azure Monitor, Azure Cost Management Skills: Data governance, Security, Performance optimization, Cost management, Cloud scaling

Syllabus

Data Governance Frameworks for Big Data

Data Governance Frameworks for Big Data provides comprehensive training on establishing and implementing data governance frameworks tailored to big data environments. Students will learn to develop governance policies, implement metadata management strategies using Microsoft Purview, and configure automated data cataloging and discovery systems. The module covers data quality frameworks, lineage tracking, and compliance management, equipping learners with practical skills to manage distributed data assets effectively while maintaining regulatory compliance and operational excellence across enterprise big data systems.

Security Implementation for Big Data Systems

Security Implementation for Big Data Systems focuses on implementing comprehensive security controls for big data environments, including defense-in-depth strategies, authentication and authorization mechanisms, and data protection techniques. Students will learn to design multilayered security architectures, configure identity and access management systems, and implement encryption and privacy controls that meet regulatory requirements while maintaining system performance.

Performance Optimization Techniques

Performance Optimization Techniques focuses on optimizing the performance of big data systems. Students will learn how to identify and resolve performance bottlenecks, implement caching strategies, apply indexing and partitioning techniques, and optimize query performance in distributed environments. The module prepares learners to ensure efficient processing, fast response times, and robust observability in large-scale data systems, enabling issues to be detected and addressed before they impact users.

Scaling Big Data Systems

Scaling Big Data Systems focuses on designing horizontally and vertically scalable big data architectures, implementing automated scaling policies for varying workloads, applying distribution strategies to achieve balanced performance, and managing system growth through effective planning and implementation.

Enterprise Big Data Best Practices and Case Studies

Enterprise Big Data Best Practices and Case Studies covers enterprise-grade practices for big data operations, cost optimization strategies for large-scale deployments, and real-world case studies from successful big data implementations across industries. Students will learn operational excellence frameworks, comprehensive cost management approaches, and proven implementation patterns that can be applied to different organizational contexts.