Module 1: Azure Fundamentals for Big Data

• Overview of Azure services for big data.
• Azure Blob Storage and data lake concepts.
• Implementing Azure Data Factory pipelines.
• Introduction to Azure Databricks.
• Azure Synapse Analytics for large-scale data.
• Azure Stream Analytics for real-time processing.
• Data security with Azure Active Directory.
• Compliance and governance in the Azure cloud.

Module 2: Data Processing with Azure HDInsight

• Hadoop ecosystem components in Azure HDInsight.
• Running Hive and Pig for data transformation.
• Implementing batch processing workflows.
• Integration with Azure storage and data services.
• Monitoring and optimisation of HDInsight clusters.
• Customisation with script actions.

Module 3: Real-Time Analytics with Azure Stream Analytics

• Ingesting streaming data using Azure Event Hubs.
• Defining real-time analytics jobs in Stream Analytics.
• Visualising real-time data with Power BI.

Module 4: Developing Big Data Solutions with Azure Databricks

• Configuring Azure Databricks workspaces.
• Collaborative data exploration and notebook workflows.
• Data engineering with Spark SQL and DataFrames.
• Developing scalable machine learning models with MLlib.
• Integration with Azure services for complete solutions.
• Delta Lake for ACID transactions and versioned data storage.
• Data visualisation and reporting via Databricks dashboards.
• Optimising Databricks jobs for performance and cost.
• Managing jobs and clusters with Databricks utilities.

Module 5: Data Warehousing with Azure Synapse Analytics

• Building and managing data warehouses.
• Data loading strategies and ETL in Synapse Analytics.
• Employing T-SQL for complex data queries.
• Synapse Analytics security features.
• Performance tuning and maintenance.

Module 6: Advanced Analytics with Azure Machine Learning

• Azure Machine Learning Workbench environment.
• Building and deploying predictive models.
• Connecting ML services with Azure data pipelines.
• Model management and performance tracking.

Module 7: Internet of Things (IoT) on Azure

• Introduction to Azure IoT Hub and related services.
• Device provisioning and management.
• Building IoT solutions with Azure IoT Central.
• Stream processing with Azure Time Series Insights.
• Edge computing with Azure IoT Edge.
• Integrating IoT data with analytics processes.
• Security considerations for IoT in Azure.
• Case studies on IoT data analytics.

Module 8: AI and Cognitive Services Integration

• Utilising Azure AI services for analytics.
• Building bots with Azure Bot Service.
• Text analytics and language understanding with Cognitive Services.
• Image and video processing with Computer Vision APIs.
• Custom AI solutions with Azure Machine Learning Designer.
• Best practices for deploying AI models.
• Incorporating Azure Search for data discoverability.
• Managed AI services vs custom model deployment considerations.
• Ethical considerations in deploying AI solutions.

Module 9: Data Visualisation and Business Intelligence

• Power BI integration with Azure services.
• Designing interactive reports and dashboards.
• Advanced data visualisation techniques.

Module 10: Capstone Project

• Integrating various Azure components to solve a real-world big data problem.
• Synthesising insights and presenting analytic findings.
• Peer-review and evaluation of capstone projects.

Module 11: Streamlining Data Lake Analytics

• Architecture and setup of Azure Data Lake.
• Data exploration with U-SQL scripting.
• Managing metadata with Azure Data Catalogue.
• Performance tuning and optimisation strategies.
• Access control and security best practices for data lakes.
• Integration with Azure Synapse for complex analytics.
• Establishing a hierarchical namespace with Azure Data Lake Storage Gen2.
• Implementing end-to-end data lake analytics solutions.

Module 12: Advanced Data Engineering on Azure Synapse

• Deep dive into data integration with pipelines and data flows.
• Real-world scenarios: batch scoring, data warehousing, and ETL.
• Advanced data modelling techniques and best practices.
• Querying and analysing data using Azure Synapse SQL pools.
• Optimising data storage with partitioning and indexing strategies.
• Continuous integration and delivery (CI/CD) in Synapse workflows.

Module 13: Big Data Analytics with Azure Analysis Services

• Developing semantic data models for enterprise analytics.
• Deploying and managing Analysis Services instances.
• Consuming data with Power BI, Excel, and other applications.
• Automation and scaling with Azure Analysis Services.
• Updated best practices for MDX and DAX expressions.
• Security features and row-level security implementation.

Module 14: Security and Compliance in Azure Big Data

• Understanding Azure’s security infrastructure for big data.
• Data encryption at rest and in transit.
• Implementing Azure Key Vault for managing encryption keys.
• Security auditing and threat detection with Azure Security Center.
• Navigating compliance frameworks and data protection laws.
• Implementing data governance strategies with Azure Purview.

Module 15: Multi-Cloud and Hybrid Big Data Architectures

• Designing big data architectures for hybrid cloud environments.
• Connecting on-premises systems with Azure Stack.
• Cross-service orchestration with Azure Arc.
• Managing multi-cloud resources with Azure management tools.
• Best practices for data synchronisation and consistency.

Module 16: Advanced Analytics with Synapse Spark Pools

• In-depth understanding of Spark pools in Azure Synapse Analytics.
• Best practices for job and resource management.
• Advanced analytics and machine learning with Spark pools.
• Integration with DevOps for Spark applications.
• Troubleshooting common issues and optimisation tips.

Module 17: Big Data Governance and Lifecycle Management

• Implementing data governance frameworks in Azure.
• Lifecycle management strategies from ingestion to disposal.
• Metadata management and data cataloguing with Azure Purview.
• Implementing data quality services and master data management (MDM).
• Auditing and monitoring for data compliance and usage.