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MS- DP203T00: Data Engineering on Microsoft Azure
Course Code: MS- DP203T00
In this course, the student will learn about the data engineering patterns and practices as it pertains to working with batch and real-time analytical solutions using Azure data platform technologies. Students will begin by understanding the core compute and storage technologies that are used to build an analytical solution. They will then explore how to design an analytical serving layers and focus on data engineering considerations for working with source files. The students will learn how to interactively explore data stored in files in a data lake. They will learn the various ingestion techniques that can be used to load data using the Apache Spark capability found in Azure Synapse Analytics or Azure Databricks, or how to ingest using Azure Data Factory or Azure Synapse pipelines. The students will also learn the various ways they can transform the data using the same technologies that is used to ingest data. The student will spend time on the course learning how to monitor and analyze the performance of analytical system so that they can optimize the performance of data loads, or queries that are issued against the systems. They will understand the importance of implementing security to ensure that the data is protected at rest or in transit. The student will then show how the data in an analytical system can be used to create dashboards or build predictive models in Azure Synapse Analytics.
The primary audience for this course is data professionals, data architects, and business intelligence professionals who want to learn about data engineering and building analytical solutions using data platform technologies that exist on Microsoft Azure. The secondary audience for this course data analysts and data scientists who work with analytical solutions built on Microsoft Azure.
There are no pre-requisites for taking this course. Technical IT experience is not required however some general IT knowledge or experience would be beneficial. Successful students start this course with knowledge of cloud computing and core data concepts and professional experience with data solutions, specifically completing:
- AZ-900: Azure Fundamentals
- DP-900: Microsoft Azure Data Fundamentals
After completing this course, students will be able to:
- Explore compute and storage options for data engineering workloads in Azure
- Design and Implement the serving layer
- Understand data engineering considerations
This course will prepare delegates to write the Microsoft DP203: Data Engineering on Microsoft Azure exam.
Modules
Microsoft Azure provides a comprehensive platform for data engineering; but what is data engineering? Complete this module to find out.
Lessons
- What is data engineering.
- Important data engineering concepts.
- Data engineering in Microsoft Azure.
After completing this module, students will be able to:
- Identify common data engineering tasks.
- Describe common data engineering concepts.
- Identify Azure services for data engineering.
Microsoft Azure provides a comprehensive platform for data engineering; but what is data engineering? Complete this module to find out.
Data lakes are a core element of data analytics architectures. Azure data lake storage Gen2 provides a scalable, secure, cloud-based solution for data lake storage.
Lessons
- Understand Azure Data Lake Storage Gen2.
- Enable Azure Data Lake Storage Gen2 in Azure Storage.
- Compare Azure Data Lake Store to Azure Blob storage.
- Understand the stages for processing big data.
- Use Azure Data Lake Storage Gen2 in data analytics workloads.
After completing this module, students will be able to:
- Describe the key features and benefits of Azure Data Lake Storage Gen2.
- Enable Azure Data Lake Storage Gen2 in an Azure Storage account.
- Compare Azure Data Lake Storage Gen2 and Azure Blob storage.
- Describe where Azure Data Lake Storage Gen2 fits in the stages of analytical processing.
- Describe how Azure data Lake Storage Gen2 is used in common analytical workloads.
Learn about the features and capabilities of Azure Synapse Analytics - a cloud-based platform for big data processing and analysis.
Lessons
- How Azure Synapse Analytics works
- When to use Azure Synapse Analytics
- Exercise - Explore Azure Synapse Analytics
After completing this module, students will be able to:
- Identify the business problems that Azure Synapse Analytics addresses.
- Describe core capabilities of Azure Synapse Analytics.
- Determine when to use Azure Synapse Analytics.
With Azure Synapse serverless SQL pool, you can leverage your SQL skills to explore and analyze data in files, without the need to load the data into a relational database.
Lessons
- When to use Azure Synapse Analytics
- Understand Azure Synapse serverless SQL pool capabilities and use cases
- Query files using a serverless SQL pool
- Create external database objects
- Exercise - Query files using a serverless SQL pool
After completing this module, students will be able to:
- Identify capabilities and use cases for serverless SQL pools in Azure Synapse Analytics
- Query CSV, JSON, and Parquet files using a serverless SQL pool
- Create external database objects in a serverless SQL pool
By using a serverless SQL pool in Azure Synapse Analytics, you can use the ubiquitous SQL language to transform data in files in a data lake.
Lessons
- Transform data files with the CREATE EXTERNAL TABLE AS SELECT statement
- Encapsulate data transformations in a stored procedure
- Include a data transformation stored procedure in a pipeline
- Exercise - Transform files using a serverless SQL pool
After completing this module, students will be able to:
- Familiarity with Azure Synapse Analytics.
- Experience using Transact-SQL to query and manipulate data.
Why choose between working with files in a data lake or a relational database schema? With lake databases in Azure Synapse Analytics, you can combine the benefits of both.
Lessons
- Understand lake database concepts
- Explore database templates
- Create a lake database
- Use a lake database
- Exercise - Analyze data in a lake database
After completing this module, students will be able to:
- Understand lake database concepts and components
- Describe database templates in Azure Synapse Analytics
- Create a lake database
Learn how you can set up security when using Azure Synapse serverless SQL pools
Lessons
- Introduction
- Choose an authentication method in Azure Synapse serverless SQL pools
- Manage users in Azure Synapse serverless SQL pools
- Manage user permissions in Azure Synapse serverless SQL pools
- Module assessment
- Summary
After completing this module, students will be able to:
- Choose an authentication method in Azure Synapse serverless SQL pools
- Manage users in Azure Synapse serverless SQL pools
- Manage user permissions in Azure Synapse serverless SQL pools
Apache Spark is a core technology for large-scale data analytics. Learn how to use Spark in Azure Synapse Analytics to analyze and visualize data in a data lake.
Lessons
- Introduction
- Get to know Apache Spark
- Use Spark in Azure Synapse Analytics
- Analyze data with Spark
- Visualize data with Spark
- Exercise - Analyze data with Spark
- Module assessment
- Summary
After completing this module, students will be able to:
- Identify core features and capabilities of Apache Spark.
- Configure a Spark pool in Azure Synapse Analytics.
- Run code to load, analyze, and visualize data in a Spark notebook.
Data engineers commonly need to transform large volumes of data. Apache Spark pools in Azure Synapse Analytics provide a distributed processing platform that they can use to accomplish this goal.
Lessons
- Introduction
- Modify and save dataframes
- Partition data files
- Transform data with SQL
- Exercise: Transform data with Spark in Azure Synapse Analytics
- Module assessment
- Summary
After completing this module, students will be able to:
- Use Apache Spark to modify and save dataframes
- Partition data files for improved performance and scalability.
- Transform data with SQL
Delta Lake is an open source relational storage area for Spark that you can use to implement a data lakehouse architecture in Azure Synapse Analytics.
Lessons
- Introduction
- Understand Delta Lake
- Create Delta Lake tables
- Create catalog tables
- Use Delta Lake with streaming data
- Use Delta Lake in a SQL pool
- Exercise - Use Delta Lake in Azure Synapse Analytics
- Module assessment
- Summary
After completing this module, students will be able to:
- Describe core features and capabilities of Delta Lake.
- Create and use Delta Lake tables in a Synapse Analytics Spark pool.
- Create Spark catalog tables for Delta Lake data.
- Use Delta Lake tables for streaming data.
- Query Delta Lake tables from a Synapse Analytics SQL pool.
Pipelines are the lifeblood of a data analytics solution. Learn how to use Azure Synapse Analytics pipelines to build integrated data solutions that extract, transform, and load data across diverse systems.
Lessons
- Understand pipelines in Azure Synapse Analytics
- Create a pipeline in Azure Synapse Studio
- Define data flows
- Run a pipeline
- Exercise - Build a data pipeline in Azure Synapse Analytics
- Module assessment
- Summary
After completing this module, students will be able to:
- Describe core concepts for Azure Synapse Analytics pipelines.
- Create a pipeline in Azure Synapse Studio.
- Implement a data flow activity in a pipeline.
- Initiate and monitor pipeline runs.
Apache Spark provides data engineers with a scalable, distributed data processing platform, which can be integrated into an Azure Synapse Analytics pipeline.
Lessons
- Introduction
- Understand Synapse Notebooks and Pipelines
- Use a Synapse notebook activity in a pipeline
- Use parameters in a notebook
- Exercise - Use an Apache Spark notebook in a pipeline
- Module assessment
- Summary
After completing this module, students will be able to:
- Describe notebook and pipeline integration.
- Use a Synapse notebook activity in a pipeline.
- Use parameters with a notebook activity.
Relational data warehouses are a core element of most enterprise Business Intelligence (BI) solutions, and are used as the basis for data models, reports, and analysis.
Lessons
- Introduction
- Design a data warehouse schema
- Create data warehouse tables
- Load data warehouse tables
- Query a data warehouse
- Exercise - Explore a data warehouse
- Module assessment
- Summary
After completing this module, students will be able to:
- Design a schema for a relational data warehouse.
- Create fact, dimension, and staging tables.
- Use SQL to load data into data warehouse tables.
- Use SQL to query relational data warehouse tables.
A core responsibility for a data engineer is to implement a data ingestion solution that loads new data into a relational data warehouse.
Lessons
- Introduction
- Load staging tables
- Load dimension tables
- Load time dimension tables
- Load slowly changing dimensions
- Load fact tables
- Perform post load optimization
- Exercise - load data into a relational data warehouse
- Module assessment
- Summary
After completing this module, students will be able to:
- Load staging tables in a data warehouse
- Load dimension tables in a data warehouse
- Load time dimensions in a data warehouse
- Load slowly changing dimensions in a data warehouse
- Load fact tables in a data warehouse
- Perform post-load optimizations in a data warehouse
Learn how to manage and monitor Azure Synapse Analytics.
Lessons
- 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
- Module assessment
- Summary
After completing this module, students will be able to:
- 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
Learn how to approach and implement security to protect your data with Azure Synapse Analytics.
Lessons
- 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
- Module assessment
- Summary
After completing this module, students will be able to:
- 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
Learn how hybrid transactional / analytical processing (HTAP) can help you perform operational analytics with Azure Synapse Analytics.
Lessons
- Introduction
- Understand hybrid transactional and analytical processing patterns
- Describe Azure Synapse Link
- Module assessment
- Summary
After completing this module, students will be able to:
- Describe Hybrid Transactional / Analytical Processing patterns.
- Identify Azure Synapse Link services for HTAP.
Azure Synapse Link for Azure Cosmos DB enables HTAP integration between operational data in Azure Cosmos DB and Azure Synapse Analytics runtimes for Spark and SQL.
Lessons
- Introduction
- Enable Cosmos DB account to use Azure Synapse Link
- Create an analytical store enabled container
- Create a linked service for Cosmos DB
- Query Cosmos DB data with Spark
- Query Cosmos DB with Synapse SQL
- Exercise - Implement Azure Synapse Link for Cosmos DB
- Module assessment
- Summary
After completing this module, students will be able to:
- Configure an Azure Cosmos DB Account to use Azure Synapse Link.
- Create an analytical store enabled container.
- Create a linked service for Azure Cosmos DB.
- Analyze linked data using Spark.
- Analyze linked data using Synapse SQL.
Azure Synapse Link for SQL enables low-latency synchronization of operational data in a relational database to Azure Synapse Analytics.
Lessons
- Introduction
- What is Azure Synapse Link for SQL?
- Configure Azure Synapse Link for Azure SQL Database
- Configure Azure Synapse Link for SQL Server 2022
- Exercise - Implement Azure Synapse Link for SQL
- Module assessment
- Summary
After completing this module, students will be able to:
- Understand key concepts and capabilities of Azure Synapse Link for SQL.
- Configure Azure Synapse Link for Azure SQL Database.
- Configure Azure Synapse Link for Microsoft SQL Server.
Azure Stream Analytics enables you to process real-time data streams and integrate the data they contain into applications and analytical solutions.
Lessons
- Introduction
- Understand data streams
- Understand event processing
- Understand window functions
- Exercise - Get started with Azure Stream Analytics
- Module assessment
- Summary
After completing this module, students will be able to:
- Understand data streams.
- Understand event processing.
- Understand window functions.
- Get started with Azure Stream Analytics.
Azure Stream Analytics provides a real-time data processing engine that you can use to ingest streaming event data into Azure Synapse Analytics for further analysis and reporting.
Lessons
- Introduction
- Stream ingestion scenarios
- Configure inputs and outputs
- Define a query to select, filter, and aggregate data
- Run a job to ingest data
- Exercise - Ingest streaming data into Azure Synapse Analytics
- Module assessment
- Summary
After completing this module, students will be able to:
- Describe common stream ingestion scenarios for Azure Synapse Analytics.
- Configure inputs and outputs for an Azure Stream Analytics job.
- Define a query to ingest real-time data into Azure Synapse Analytics.
- Run a job to ingest real-time data, and consume that data in Azure Synapse Analytics.
By combining the stream processing capabilities of Azure Stream Analytics and the data visualization capabilities of Microsoft Power BI, you can create real-time data dashboards.
Lessons
- Introduction
- Use a Power BI output in Azure Stream Analytics
- Create a query for real-time visualization
- Create real-time data visualizations in Power BI
- Exercise - Create a real-time data visualization
- Module assessment
- Summary
After completing this module, students will be able to:
- Configure a Stream Analytics output for Power BI.
- Use a Stream Analytics query to write data to Power BI.
- Create a real-time data visualization in Power BI.
Azure Databricks is a cloud service that provides a scalable platform for data analytics using Apache Spark.
Lessons
- Introduction
- Get started with Azure Databricks
- Identify Azure Databricks workloads
- Understand key concepts
- Data governance using Unity Catalog and Microsoft Purview
- Exercise - Explore Azure Databricks
- Module assessment
- Summary
After completing this module, students will be able to:
- Provision an Azure Databricks workspace
- Identify core workloads for Azure Databricks
- Use Data Governance tools Unity Catalog and Microsoft Purview
- Describe key concepts of an Azure Databricks solution
Learn how to perform data analysis using Azure Databricks. Explore various data ingestion methods and how to integrate data from sources like Azure Data Lake and Azure SQL Database. This module guides you through using collaborative notebooks to perform exploratory data analysis (EDA), so you can visualize, manipulate, and examine data to uncover patterns, anomalies, and correlations.
Lessons
- Introduction
- Ingest data with Azure Databricks
- Data exploration tools in Azure Databricks
- Data analysis using DataFrame APIs
- Exercise - Explore data with Azure Databricks
- Module assessment
- Summary
After completing this module, students will be able to:
- Ingest data using Azure Databricks.
- Using the different data exploration tools in Azure Databricks.
- Analyze data with DataFrame APIs.
Delta Lake is a data management solution in Azure Databricks providing features including ACID transactions, schema enforcement, and time travel ensuring data consistency, integrity, and versioning capabilities.
Lessons
- Introduction
- Get started with Delta Lake
- Manage ACID transactions
- Implement schema enforcement
- Data versioning and time travel in Delta Lake
- Data integrity with Delta Lake
- Exercise - Use Delta Lake in Azure Databricks
- Module assessment
- Summary
After completing this module, students will be able to:
- What Delta Lake is
- How to manage ACID transactions using Delta Lake
- How to use schema versioning and time travel in Delta Lake
- How to maintain data integrity with Delta Lake
Building data pipelines with Delta Live Tables enables real-time, scalable, and reliable data processing using Delta Lake's advanced features in Azure Databricks
Lessons
- Introduction
- Explore Delta Live Tables
- Data ingestion and integration
- Real-time processing
- Exercise - Create a data pipeline with Delta Live Tables
- Module assessment
- Summary
After completing this module, students will be able to:
- Describe Delta Live Tables
- Ingest data into Delta Live Tables
- Use Data Pipelines for real time data processing