15 Topics
10 Days
75 Sessions
Programme Overview
Training Description
Who Should Attend
This course is ideal for;
- Data Engineers
- Data Architects
- Cloud Developers
- Data Analysts
- Database Administrators
- Business Intelligence Developers
- Anyone needing GCP data engineering skills
Session Objectives
- Understand the fundamentals of Azure data engineering services (Data Factory, Synapse Analytics).
- Master Azure Data Factory for data integration and ETL processes.
- Utilize Azure Synapse Analytics for data warehousing and analytics.
- Implement data pipelines and data flows in Azure.
- Design and build scalable data solutions on the Azure platform.
- Optimize Azure data services for performance and cost-effectiveness.
- Troubleshoot and address common challenges in Azure data engineering.
- Implement data security and access control in Azure data services.
- Integrate Azure data services with other Azure and third-party tools.
- Understand how to handle large datasets and data warehousing in Azure.
- Explore advanced Azure data engineering features (e.g., Synapse Pipelines, Spark integration).
- Apply real world use cases for Azure data engineering services.
- Leverage Azure's ecosystem for efficient data engineering workflows.
About the Course
Empower your data infrastructure with our Azure Data Engineering Services (Data Factory, Synapse Analytics) Training Course. This program is designed to equip you with the essential skills to build data solutions on the Microsoft Azure platform, enabling you to design and deploy scalable and efficient data architectures. In today's data-driven world, mastering Azure data engineering tools is crucial for organizations seeking to derive actionable insights from their data. Our Azure data engineering training course offers hands-on experience and expert guidance, empowering you to leverage Azure Data Factory and Synapse Analytics for diverse data engineering tasks.
This Azure data solutions training delves into the core concepts of Azure data engineering, covering topics such as data integration, data warehousing, and analytics. You'll gain expertise in using industry-standard Azure services to build data solutions on the Microsoft Azure platform, meeting the demands of modern data-intensive organizations. Whether you're a data engineer, data architect, or cloud developer, this Azure Data Engineering Services (Data Factory, Synapse Analytics) course will empower you to design and implement high-performance data solutions on the Azure platform.
Curriculum & Topics
15 Topics | 75 Sessions
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Workshop 1.1:
Fundamentals of Azure data engineering services (Data Factory, Synapse Analytics).
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Workshop 1.2:
Overview of data integration, data warehousing, and analytics on Azure.
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Workshop 1.3:
Setting up an Azure data engineering development environment.
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Workshop 1.4:
Introduction to Azure Data Factory and Synapse Analytics.
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Workshop 1.5:
Best practices for Azure data engineering.
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Workshop 2.1:
Mastering Azure Data Factory for data integration and ETL processes.
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Workshop 2.2:
Utilizing pipelines, datasets, and linked services.
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Workshop 2.3:
Designing and building data integration workflows.
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Workshop 2.4:
Optimizing Data Factory pipelines for performance.
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Workshop 2.5:
Best practices for Azure Data Factory.
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Workshop 3.1:
Utilizing Azure Synapse Analytics for data warehousing and analytics.
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Workshop 3.2:
Implementing SQL pools and Spark pools.
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Workshop 3.3:
Designing and building data warehouses with Synapse.
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Workshop 3.4:
Optimizing Synapse queries for analytical workloads.
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Workshop 3.5:
Best practices for Azure Synapse Analytics.
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Workshop 4.1:
Implementing data pipelines and data flows in Azure.
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Workshop 4.2:
Utilizing mapping data flows for visual data transformation.
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Workshop 4.3:
Designing and building complex data pipelines.
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Workshop 4.4:
Optimizing data flows for performance and scalability.
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Workshop 4.5:
Best practices for data pipelines.
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Workshop 5.1:
Designing and building scalable data solutions on the Azure platform.
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Workshop 5.2:
Utilizing Azure storage and compute resources.
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Workshop 5.3:
Implementing data ingestion, transformation, and loading (ETL).
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Workshop 5.4:
Optimizing solutions for large-scale data processing.
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Workshop 5.5:
Best practices for Azure data solutions.
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Workshop 6.1:
Optimizing Azure data services for performance and cost-effectiveness.
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Workshop 6.2:
Utilizing performance tuning and monitoring tools.
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Workshop 6.3:
Implementing data partitioning and compression strategies.
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Workshop 6.4:
Designing cost-effective data solutions.
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Workshop 6.5:
Best practices for optimization.
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Workshop 7.1:
Debugging common challenges in Azure data engineering.
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Workshop 7.2:
Analyzing Azure service logs and error messages.
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Workshop 7.3:
Utilizing troubleshooting techniques for problem resolution.
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Workshop 7.4:
Resolving common data engineering issues.
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Workshop 7.5:
Best practices for troubleshooting.
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Workshop 8.1:
Implementing data security and access control in Azure data services.
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Workshop 8.2:
Utilizing Azure Active Directory and RBAC.
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Workshop 8.3:
Designing and building secure data solutions.
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Workshop 8.4:
Optimizing security for data protection.
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Workshop 8.5:
Best practices for security.
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Workshop 9.1:
Integrating Azure data services with other Azure and third-party tools.
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Workshop 9.2:
Utilizing Azure Blob Storage, Azure Databricks, and Power BI.
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Workshop 9.3:
Implementing data integration with external data sources.
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Workshop 9.4:
Optimizing integration for data retrieval and analysis.
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Workshop 9.5:
Best practices for integration.
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Workshop 10.1:
Understanding how to handle large datasets and data warehousing in Azure.
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Workshop 10.2:
Utilizing Synapse dedicated SQL pools and serverless SQL pools.
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Workshop 10.3:
Implementing data partitioning and parallel processing.
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Workshop 10.4:
Designing scalable data warehousing solutions.
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Workshop 10.5:
Best practices for large datasets.
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Workshop 11.1:
Exploring advanced Azure data engineering features (Synapse Pipelines, Spark integration).
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Workshop 11.2:
Utilizing Synapse Pipelines for orchestration.
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Workshop 11.3:
Implementing Spark integration for data processing.
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Workshop 11.4:
Designing and building advanced data engineering solutions.
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Workshop 11.5:
Optimizing advanced techniques for specific applications.
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Workshop 11.6:
Best practices for advanced features.
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Workshop 12.1:
Implementing Azure data engineering for real-time analytics and monitoring.
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Workshop 12.2:
Utilizing Azure data services for data warehousing and business intelligence.
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Workshop 12.3:
Implementing Azure data engineering for machine learning and data science.
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Workshop 12.4:
Utilizing Azure data engineering for log processing and data analysis.
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Workshop 12.5:
Best practices for real-world applications.
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Workshop 13.1:
Utilizing Azure data engineering tools and frameworks (Azure DevOps, Azure CLI).
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Workshop 13.2:
Implementing data pipelines with specific tools.
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Workshop 13.3:
Designing and building automated data workflows.
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Workshop 13.4:
Optimizing tool usage for efficient development.
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Workshop 13.5:
Best practices for tool implementation.
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Workshop 14.1:
Implementing performance monitoring and logging for Azure data services.
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Workshop 14.2:
Utilizing Azure Monitor and Log Analytics.
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Workshop 14.3:
Designing and building performance dashboards.
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Workshop 14.4:
Optimizing monitoring for real-time insights.
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Workshop 14.5:
Best practices for monitoring.
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Workshop 15.1:
Emerging trends in Azure data engineering.
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Workshop 15.2:
Utilizing AI for data pipeline automation.
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Workshop 15.3:
Implementing data mesh architectures on Azure.
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Workshop 15.4:
Best practices for future applications.
