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Microsoft

Work with Data Warehouses using Azure Synapse Analytics

Microsoft via Microsoft Learn

Overview

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  • Module 1: Design a Modern Data Warehouse using Azure Synapse Analytics
  • In this module, you will:

    • Describe a Modern Data Warehouse
    • Define a Modern Data Warehouse Architecture
    • Design ingestion patterns for a Modern Data Warehouse
    • Understand data storage for a Modern Data Warehouse
    • Understand file formats and structure for a modern data warehouse
    • Prepare and transform data with Azure Synapse Analytics
    • Serve data for analysis with Azure Synapse Analytics
  • Module 2: Design a multidimensional schema to optimize analytical workloads
  • In this module, you will:

    • Design and implement a star schema
    • Design and implement a snowflake schema
    • Design and implement a time dimension table
  • Module 3: Use data loading best practices in Azure Synapse Analytics
  • In this module, you will:

    • Understand data loading design goals
    • Explain loading methods into Azure Synapse Analytics
    • Manage source data files
    • Manage singleton updates
    • Set-up dedicated data loading accounts
    • Manage concurrent access to Azure Synapse Analytics
    • Implement Workload Management
    • Simplify ingestion with the Copy Activity
  • Module 4: Optimize data warehouse query performance in Azure Synapse Analytics
  • In this module, you will:

    • Understand performance issues related to tables
    • Understand table distribution design
    • Use indexes to improve query performance
    • Create statistics to improve query performance
    • Improve query performance with Materialized Views
    • Use read committed snapshot for data consistency
    • Optimize common queries with result-set caching
  • Module 5: Integrate SQL and Apache Spark pools in Azure Synapse Analytics
  • After completing this module, you will be able to:

    • Describe the integration methods between SQL and Spark Pools in Azure Synapse Analytics
    • Understand the use-cases for SQL and Spark Pools integration
    • Authenticate in Azure Synapse Analytics
    • Transfer data between SQL and Spark Pool in Azure Synapse Analytics
    • Authenticate between Spark and SQL Pool in Azure Synapse Analytics
    • Integrate SQL and Spark Pools in Azure Synapse Analytics
    • Externalize the use of Spark Pools within Azure Synapse workspace
    • Transfer data outside the Synapse workspace using SQL Authentication
    • Transfer data outside the Synapse workspace using the PySpark Connector
    • Transform data in Apache Spark and write back to SQL Pool in Azure Synapse Analytics
  • Module 6: Understand data warehouse developer features of Azure Synapse Analytics
  • In this module, you will:

    • Explore the development tools for Azure Synapse Analytics
    • Understand Transact-SQL language capabilities for Azure Synapse Analytics
    • Work with Windowing functions
    • Work with approximate execution
    • Work with JSON data in SQL Pools
    • Encapsulate Transact-SQL logic with stored procedures
  • Module 7: Manage and monitor data warehouse activities in Azure Synapse Analytics
  • In this module, you will:

    • Scale compute resources in Azure Synapse Analytics
    • Pause compute in Azure Synapse Analytics
    • Manage workloads in Azure Synapse Analytics
    • Use Azure Advisor to review recommendations
    • Use Dynamic Management Views to identify and troubleshoot query performance
  • Module 8: Analyze and optimize data warehouse storage in Azure Synapse Analytics
  • In this module, you will:

    • Understand skewed data and space usage
    • Understand column store storage details
    • Understand the impact of wrong choices for column data types
    • Describe the impact of materialized views
    • Understand rules for minimally logged operations
  • Module 9: Secure a data warehouse in Azure Synapse Analytics
  • In this module, you will:

    • Understand network security options for Azure Synapse Analytics
    • Configure Conditional Access
    • Configure Authentication
    • Manage authorization through column and row level security
    • Manage sensitive data with Dynamic Data masking
    • Implement encryption in Azure Synapse Analytics

Syllabus

  • Module 1: Design a Modern Data Warehouse using Azure Synapse Analytics
    • Introduction
    • Describe a modern data warehouse
    • Define a modern data warehouse architecture
    • Exercise - Identify modern data warehouse architecture components
    • Design ingestion patterns for a modern data warehouse
    • Understand data storage for a modern data warehouse
    • Understand file formats and structure for a modern data warehouse
    • Prepare and transform data with Azure Synapse Analytics
    • Serve data for analysis with Azure Synapse Analytics
    • Knowledge check
    • Summary
  • Module 2: Design a multidimensional schema to optimize analytical workloads
    • Introduction
    • Design and implement a star schema
    • Exercise - Create a star schema
    • Design and implement a snowflake schema
    • Exercise - Create a snowflake schema
    • Design and implement a time dimension table
    • Exercise - Create and populate a time dimension table
    • Knowledge check
    • Summary
  • Module 3: Use data loading best practices in Azure Synapse Analytics
    • Introduction
    • Understand data load design goals
    • Explain load methods into Azure Synapse Analytics
    • Manage source data files
    • Manage singleton updates
    • Set-up dedicated data load accounts
    • Implement workload management
    • Exercise - implement workload management
    • Simplify ingestion with the Copy Activity
    • Knowledge check
    • Summary
  • Module 4: Optimize data warehouse query performance in Azure Synapse Analytics
    • Introduction
    • Understand performance issues related to tables
    • Exercise - Understand performance issues related to tables
    • Understand table distribution design
    • Use indexes to improve query performance
    • Exercise - Use table distribution and indexes to improve performance
    • Create statistics to improve query performance
    • Improve query performance with materialized views
    • Use read committed snapshot for data consistency
    • Optimize common queries with result-set caching
    • Knowledge check
    • Summary
  • Module 5: Integrate SQL and Apache Spark pools in Azure Synapse Analytics
    • Introduction
    • Describe the integration methods between SQL and spark pools in Azure Synapse Analytics
    • Understand the use-cases for SQL and spark pools integration
    • Authenticate in Azure Synapse Analytics
    • Transfer data between SQL and spark pool in Azure Synapse Analytics
    • Authenticate between spark and SQL pool in Azure Synapse Analytics
    • Exercise: Integrate SQL and spark pools in Azure Synapse Analytics
    • Externalize the use of spark pools within Azure Synapse Workspace
    • Transfer data outside the synapse workspace using the PySpark connector
    • Knowledge check
    • Summary
  • Module 6: Understand data warehouse developer features of Azure Synapse Analytics
    • Introduction
    • Explore the development tools for Azure Synapse Analytics
    • Understand transact-SQL language capabilities for Azure Synapse Analytics
    • Work with windowing functions
    • Exercise - work with windowing functions
    • Work with approximate execution
    • Exercise - work with approximate execution
    • Work with JSON data in SQL pools
    • Encapsulate transact-SQL logic with stored procedures
    • Knowledge check
    • Summary
  • Module 7: Manage and monitor data warehouse activities in Azure Synapse Analytics
    • Introduction
    • Scale compute resources in Azure Synapse Analytics
    • Pause compute in Azure Synapse Analytics
    • Manage workloads in Azure Synapse Analytics
    • Use Azure Advisor to review recommendations
    • Use dynamic management views to identify and troubleshoot query performance
    • Knowledge check
    • Summary
  • Module 8: Analyze and optimize data warehouse storage in Azure Synapse Analytics
    • Introduction
    • Understand skewed data and space usage
    • Exercise - Check for skewed data and space usage
    • Understand column store storage details
    • Exercise - View column store storage details
    • Understand the impact of wrong choices for column data types
    • Exercise - Compare storage requirements between optimal and sub-optimal column data types
    • Describe the impact of materialized views
    • Exercise - Improve the execution plan of a query with a materialized view
    • Understand rules for minimally logged operations
    • Exercise - Optimize a delete operation
    • Knowledge check
    • Summary
  • Module 9: Secure a data warehouse in Azure Synapse Analytics
    • Introduction
    • Understand network security options for Azure Synapse Analytics
    • Configure Conditional Access
    • Configure authentication
    • Manage authorization through column and row level security
    • Exercise - Manage authorization through column and row level security
    • Manage sensitive data with Dynamic Data Masking
    • Implement encryption in Azure Synapse Analytics
    • Knowledge check
    • Summary

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