Azure Big Data Analytics Certification Course
Date | Format | Duration | Fees (GBP) | Register |
---|---|---|---|---|
10 Feb - 21 Feb, 2025 | Live Online | 10 Days | £5825 | Register → |
03 Mar - 07 Mar, 2025 | Live Online | 5 Days | £2850 | Register → |
07 Apr - 09 Apr, 2025 | Live Online | 3 Days | £1975 | Register → |
12 May - 16 May, 2025 | Live Online | 5 Days | £2850 | Register → |
23 Jun - 04 Jul, 2025 | Live Online | 10 Days | £5825 | Register → |
18 Aug - 22 Aug, 2025 | Live Online | 5 Days | £2850 | Register → |
29 Sep - 03 Oct, 2025 | Live Online | 5 Days | £2850 | Register → |
29 Oct - 31 Oct, 2025 | Live Online | 3 Days | £1975 | Register → |
08 Dec - 10 Dec, 2025 | Live Online | 3 Days | £1975 | Register → |
Date | Venue | Duration | Fees (GBP) | Register |
---|---|---|---|---|
07 Apr - 09 Apr, 2025 | Athens | 3 Days | £3825 | Register → |
05 May - 09 May, 2025 | Madrid | 5 Days | £4750 | Register → |
02 Jun - 06 Jun, 2025 | Nairobi | 5 Days | £4350 | Register → |
07 Jul - 11 Jul, 2025 | Barcelona | 5 Days | £4750 | Register → |
04 Aug - 08 Aug, 2025 | Nairobi | 5 Days | £4350 | Register → |
01 Sep - 05 Sep, 2025 | Nairobi | 5 Days | £4350 | Register → |
06 Oct - 17 Oct, 2025 | Paris | 10 Days | £8750 | Register → |
03 Nov - 07 Nov, 2025 | Dubai | 5 Days | £4200 | Register → |
01 Dec - 05 Dec, 2025 | London | 5 Days | £4750 | Register → |
Why Select this Training Course?
The Azure Big Data Analytics Certification Course is a professionally tailored programme offering expertise in the increasingly critical field of big data analytics within the Azure cloud environment. This course is designed to propel your proficiency in Azure services for complex data analytics solutions.
Why is Azure a leading platform for Big Data Analytics?
Azure provides a comprehensive and flexible environment for analysing big data, with integrated tools to process, manage, and visualise large datasets efficiently.
What practical skills will this course deliver?
You will gain hands-on experience in deploying Azure data services, optimising data processing workflows, and extracting actionable insights through advanced analytics techniques.
Who Should Attend?
This course is apt for:
- Data Analysts
- Business Intelligence Professionals
- Data Scientists
- Database Administrators
- Cloud Computing Specialists
- IT Professionals seeking to specialise in big data analytics
What are the Course Objectives?
Participants will:
- Master key Azure big data services and tools.
- Learn to build scalable big data processing pipelines.
- Develop skills in data analytics, machine learning, and AI.
- Implement data solutions adhering to security and compliance standards.
- Harness the power of real-time analytics and IoT solutions.
How will this course be presented?
The course will include:
- Live demonstrations on Azure platforms.
- Hands-on labs and real-world project exercises.
- Interactive Q&A sessions with certified Azure experts.
- Access to a variety of learning materials and Azure resources.
What are the Topics Covered in this Course?
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.