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Data Engineering on Microsoft Azure
Duración
4 Days
Clave
MOC-DP-203T00-ES
Formatos disponibles
Classroom Training, Online Training
Descripción del curso
Temario
In this course, the student will learn how to implement and manage data engineering workloads on Microsoft Azure, using Azure services such as Azure Synapse Analytics, Azure Data Lake Storage Gen2, Azure Stream Analytics, Azure Databricks, and others. The course focuses on common data engineering tasks such as orchestrating data transfer and transformation pipelines, working with data files in a data lake, creating and loading relational data warehouses, capturing and aggregating streams of real-time data, and tracking data assets and lineage.Objetivos
Dirigido a
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.
Job role: Data Engineer
Pre-requisitos
-
Successful students start this course with knowledge of cloud computing and core data concepts and professional experience with data solutions.
Specifically completing:
- Azure Fundamentals
- Microsoft Azure Data Fundamentals
Contenido
Introduction to data engineering on Azure
- Introduction
- What is data engineering
- Important data engineering concepts
- Data engineering in Microsoft Azure
- Knowledge check
- Introduction
- 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
- Knowledge check
- Introduction
- What is Azure Synapse Analytics
- How Azure Synapse Analytics works
- When to use Azure Synapse Analytics
- Exercise - Explore Azure Synapse Analytics
- Knowledge check
- Introduction
- 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
- Knowledge check
- Introduction
- 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
- Knowledge check
- Introduction
- Understand lake database concepts
- Explore database templates
- Create a lake database
- Use a lake database
- Exercise - Analyze data in a lake database
- Knowledge check
- 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
- Knowledge check
- Introduction
- Modify and save dataframes
- Partition data files
- Transform data with SQL
- Exercise: Transform data with Spark in Azure Synapse Analytics
- Knowledge check
- 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
- Knowledge check
- Introduction
- Design a data warehouse schema
- Create data warehouse tables
- Load data warehouse tables
- Query a data warehouse
- Exercise - Explore a data warehouse
- Knowledge check
- 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
- Knowledge check
- Introduction
- 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
- Knowledge check
- 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
- Knowledge check
- Introduction
- Understand hybrid transactional and analytical processing patterns
- Describe Azure Synapse Link
- Knowledge check
- 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
- Knowledge check
- 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
- Knowledge check
- Introduction
- Understand data streams
- Understand event processing
- Understand window functions
- Exercise - Get started with Azure Stream Analytics
- Knowledge check
- 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
- Knowledge check
- 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
- Knowledge check
- Introduction
- What is Microsoft Purview?
- How Microsoft Purview works
- When to use Microsoft Purview
- Knowledge check
- Introduction
- Catalog Azure Synapse Analytics data assets in Microsoft Purview
- Connect Microsoft Purview to an Azure Synapse Analytics workspace
- Search a Purview catalog in Synapse Studio
- Track data lineage in pipelines
- Exercise - Integrate Azure Synapse Analytics and Microsoft Purview
- Knowledge check
- Introduction
- Get started with Azure Databricks
- Identify Azure Databricks workloads
- Understand key concepts
- Exercise - Explore Azure Databricks
- Knowledge check
- Introduction
- Get to know Spark
- Create a Spark cluster
- Use Spark in notebooks
- Use Spark to work with data files
- Visualize data
- Exercise - Use Spark in Azure Databricks
- Knowledge check
- Introduction
- Understand Azure Databricks notebooks and pipelines
- Create a linked service for Azure Databricks
- Use a Notebook activity in a pipeline
- Use parameters in a notebook
- Exercise - Run an Azure Databricks Notebook with Azure Data Factory
- Knowledge check
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