Nastavni plan

Pregledaj

Module 1: Introduction to data engineering on 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

Module 2: Introduction to Azure Data Lake Storage Gen2 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. 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

Module 3: Introduction to Azure Synapse Analytics Learn about the features and capabilities of Azure Synapse Analytics - a cloud-based platform for big data processing and analysis. 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

Module 4: Use Azure Synapse serverless SQL pool to query files in a data lake 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. 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

Module 5: Use Azure Synapse serverless SQL pools to transform data in a data lake 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. After completing this module, students will be able to:

• Use a CREATE EXTERNAL TABLE AS SELECT (CETAS) statement to transform data
• Encapsulate a CETAS statement in a stored procedure
• Include a data transformation stored procedure in a pipeline

Module 6: Create a lake database in Azure Synapse Analytics 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. 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

Module 7: Analyze data with Apache Spark in Azure Synapse Analytics 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. 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

Module 8: Transform data with Spark in Azure Synapse Analytics 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. After completing this module, students will be able to:

• Use Apache Spark to modify and save data frames
• Partition data files for improved performance and scalability
• Transform data with SQL

Module 9: Use Delta Lake in Azure Synapse Analytics 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. 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

Module 10: Analyze data in a relational data warehouse 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. 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

Module 12: Load data into a relational data warehouse A core responsibility for a data engineer is to implement a data ingestion solution that loads new data into a relational data warehouse. 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

Module 13: Build a data pipeline in Azure Synapse Analytics 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. 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

Module 14: Use Spark Notebooks in an Azure Synapse Pipeline Apache Spark provides data engineers with a scalable, distributed data processing platform, which can be integrated into an Azure Synapse Analytics pipeline. 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

Module 15: Plan hybrid transactional and analytical processing using Azure Synapse Analytics Learn how hybrid transactional / analytical processing (HTAP) can help you perform operational analytics with Azure Synapse Analytics. After completing this module, students will be able to:

• Describe Hybrid Transactional / Analytical Processing patterns
• Identify Azure Synapse Link services for HTAP

Module 16: Implement Azure Synapse Link with Azure Cosmos DB 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. 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

Module 17: Implement Azure Synapse Link for SQL Azure Synapse Link for SQL enables low-latency synchronization of operational data in a relational database to Azure Synapse Analytics. 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

Module 18: Get started with Azure Stream Analytics Azure Stream Analytics enables you to process real-time data streams and integrate the data they contain into applications and analytical solutions. After completing this module, students will be able to:

• Understand data streams
• Understand event processing
• Understand window functions
• Get started with Azure Stream Analytics

Module 19: Ingest streaming data using Azure Stream Analytics and Azure Synapse 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. 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

Module 20: Visualize real-time data with Azure Stream Analytics and Power BI 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. 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

Module 21: Introduction to Microsoft Purview In this module, you'll evaluate whether Microsoft Purview is the right choice for your data discovery and governance needs. After completing this module, students will be able to:

• Evaluate whether Microsoft Purview is appropriate for data discovery and governance needs
• Describe how the features of Microsoft Purview work to provide data discovery and governance

Module 23: Integrate Microsoft Purview and Azure Synapse Analytics Learn how to integrate Microsoft Purview with Azure Synapse Analytics to improve data discoverability and lineage tracking. After completing this module, students will be able to:

• Catalog Azure Synapse Analytics database assets in Microsoft Purview
• Configure Microsoft Purview integration in Azure Synapse Analytics
• Search the Microsoft Purview catalog from Synapse Studio
• Track data lineage in Azure Synapse Analytics pipelines activities

Module 24: Explore Azure Databricks Azure Databricks is a cloud service that provides a scalable platform for data analytics using Apache Spark. After completing this module, students will be able to:

• Provision an Azure Databricks workspace
• Identify core workloads and personas for Azure Databricks
• Describe key concepts of an Azure Databricks solution

Module 25: Use Apache Spark in Azure Databricks Azure Databricks is built on Apache Spark and enables data engineers and analysts to run Spark jobs to transform, analyze and visualize data at scale. After completing this module, students will be able to:

• Describe key elements of the Apache Spark architecture
• Create and configure a Spark cluster
• Describe use cases for Spark
• Use Spark to process and analyze data stored in files
• Use Spark to visualize data

Module 26: Run Azure Databricks Notebooks with Azure Data Factory Using pipelines in Azure Data Factory to run notebooks in Azure Databricks enables you to automate data engineering processes at cloud scale. After completing this module, students will be able to:

• Describe how Azure Databricks notebooks can be run in a pipeline
• Create an Azure Data Factory linked service for Azure Databricks
• Use a Notebook activity in a pipeline
• Pass parameters to a notebook

Preuzmi