Overview
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Unlock the power of data with the Microsoft Big Data Management and Analytics Professional Certificate. Organizations today rely on experts who can design, manage, and optimize big data systems to drive innovation and insight. This program prepares you for roles such as Big Data Engineer, Data Analyst, Data Scientist, or Cloud Solutions Architect, equipping you with skills that are in high demand across industries.
Throughout five hands-on courses, you will gain both foundational knowledge and applied experience with Microsoft’s leading big data tools and platforms. You will:
Understand big data fundamentals and cloud-based architectures Design and implement data lakes and data warehouses with Azure Data Lake and Synapse Analytics Build ETL/ELT pipelines and streaming solutions using Azure Data Factory, Event Hubs, and Stream Analytics Process and analyze data with Apache Spark, PySpark, and Azure Databricks Create visualizations and dashboards with Power BI and Fabric Copilot Apply machine learning and NLP techniques at scale with PySpark ML and Azure Machine Learning Implement governance and compliance using Microsoft Purview Optimize cost, performance, and scalability for enterprise big data systems
To be successful, you should have prior knowledge of Python and basic SQL. By the end, you’ll complete portfolio-ready projects—ranging from architecture diagrams to streaming pipelines and Power BI dashboards—that demonstrate job-ready skills in big data analytics.
Syllabus
- Course 1: Fundamentals of Big Data with Microsoft Azure
- Course 2: Data Storage and Management for Big Data
- Course 3: Data Processing, Exploratory Analysis and Visualization
- Course 4: Data Analytics and Machine Learning for Big Data
- Course 5: Big Data Management and Optimization
Courses
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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
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This advanced course teaches machine learning and AI techniques for big data systems. Learners will build end-to-end ML pipelines with PySpark ML, implement supervised and unsupervised models, and apply NLP techniques at scale. The course also explores deep learning, distributed training, and integrating Generative AI into big data workflows. By the end of this course, you will be able to: - Implement ML pipelines using PySpark ML - Build supervised, unsupervised, and recommendation models - Apply NLP and text analytics to large datasets -Integrate Generative AI and LLMs with big data systems Tools & Software: PySpark ML, PyTorch, TensorFlow, Azure Machine Learning, Azure OpenAI Service Skills: Machine learning, NLP, Deep learning, Generative AI, Model evaluation
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This course introduces distributed computing frameworks and big data visualization techniques. Learners will explore MapReduce, work with Apache Spark, implement transformations with PySpark, and use Spark SQL for large-scale analysis. The course concludes with building compelling dashboards and reports using Power BI for actionable business insights. By the end of this course, you will be able to: - Explain distributed computing and MapReduce concepts - Process large datasets using Apache Spark and PySpark - Apply Spark SQL for advanced queries and transformations - Create dashboards and visualizations using Power BI Tools & Software: Apache Spark, PySpark, Azure Databricks, Power BI Skills: Distributed computing, Data analysis, PySpark, Spark SQL, Data visualization
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This course provides a comprehensive overview of data storage and management approaches for big data. Learners will explore structured, semi-structured, and unstructured data formats, compare SQL and NoSQL database technologies, and implement data lakes and data warehouses. The course includes working with various file formats and understanding the differences between batch and real-time processing approaches. Course Learning Objectives: By the end of this course, you will be able to: - Compare and implement SQL and NoSQL database solutions for different big data scenarios - Work effectively with structured, semi-structured, and unstructured data formats - Design and implement data lakes and data warehouses for big data workloads - Build data pipelines using ETL and ELT approaches with Azure Data Factory - Differentiate between batch and real-time processing methodologies and implement appropriate solutions
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This foundational course introduces learners to key concepts in big data, cloud computing principles, and Microsoft Azure technologies. Learners will understand the characteristics of big data, explore the big data ecosystem within Azure, and gain practical experience with key tools, including Azure services and Databricks. The course includes cost comparisons between major cloud providers and introduces key concepts in cluster computing. Course Learning Objectives By the end of this course, you will be able to: - Explain the fundamental concepts and characteristics of big data - Describe cloud computing principles and their relevance to big data solutions - Navigate and utilize the Microsoft Azure platform for big data workloads - Understand cluster computing concepts and implement basic Azure Databricks clusters - Compare costs across major cloud providers (Azure, AWS, GCP) for big data scenarios - Set up and configure basic resources in Azure for big data implementations
Taught by
Microsoft