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How to operate with non-relational data on Azure

The main concepts covered in this domain include:

  • Offerings non-relational data on Azure – within this topic, the learner will be conversant with the following in Azure: data services, Cosmos DB APIs, Table storage, Blob storage, File storage.
  • Non relation data workloads – this sub-section mostly emphasizes the features of non-relational data, when to use it, one's knowledge of the variants of NoSQL and non-relational data, and the ability to choose and recommend appropriate data store.
  • Core administration tasks – this sub-domain covers the processes of deployment and provisioning when it comes to non-relational data, data security, diverse tools for management, and defining the issues with connectivity.

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Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/dp-900

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Microsoft DP-900 Deutsch Exam Syllabus Topics:

SectionWeightObjectives
Describe core data concepts25–30%- Describe types of data
  • 1. File formats
    • 2. Structured, semi-structured, and unstructured data
      - Describe data workloads
      • 1. Analytical workloads
        • 2. Batch and streaming data
          • 3. Transactional workloads
            - Describe roles and responsibilities
            • 1. Data engineer, data analyst, database administrator
              Describe considerations for working with non-relational data on Azure15–20%- Describe Azure storage services
              • 1. Blob Storage
                • 2. Table Storage
                  • 3. File Storage
                    • 4. Queue Storage
                      - Describe non-relational data concepts
                      • 1. Characteristics of NoSQL data
                        • 2. Use cases for non-relational data
                          - Describe Azure Cosmos DB
                          • 1. Capabilities and features
                            • 2. Consistency levels and partitioning
                              • 3. APIs and data models
                                Describe an analytics workload on Azure25–30%- Describe real-time analytics
                                • 1. Azure Stream Analytics
                                  • 2. Stream processing concepts
                                    - Describe data visualization
                                    • 1. Power BI capabilities
                                      • 2. Data models and reports
                                        - Describe Azure analytics services
                                        • 1. Azure Databricks
                                          • 2. Microsoft Fabric
                                            • 3. Azure HDInsight
                                              • 4. Azure Synapse Analytics
                                                • 5. Azure Data Lake Storage Gen2
                                                  - Describe big data and analytics concepts
                                                  • 1. Modern data warehouse architecture
                                                    • 2. Data lakes and data warehouses
                                                      • 3. Data ingestion and processing
                                                        Identify considerations for relational data on Azure20–25%- Describe Azure relational data services
                                                        • 1. Open-source databases: PostgreSQL, MySQL, MariaDB
                                                          • 2. Azure SQL Database
                                                            • 3. SQL Server on Azure Virtual Machines
                                                              • 4. Azure SQL Managed Instance
                                                                - Describe relational data concepts
                                                                • 1. Relationships, keys, constraints
                                                                  • 2. Query data with SQL
                                                                    • 3. Normalization

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