Data Engineering on Microsoft Azure (DP-203T00)

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.

Description

Overview

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.

Course Objectives

After completing this course, students will be able to:

  • Introduction to data engineering on Azure
  • Introduction to Azure Data Lake Storage Gen2
  • Introduction to Azure Synapse Analytics
  • Use Azure Synapse serverless SQL pool to query files in a data lake
  • Use Azure Synapse serverless SQL pools to transform data in a data lake
  • Create a lake database in Azure Synapse Analytics
  • Analyze data with Apache Spark in Azure Synapse Analytics
  • Transform data with Spark in Azure Synapse Analytics
  • Use Delta Lake in Azure Synapse Analytics
  • Analyze data in a relational data warehouse
  • Load data into a relational data warehouse
  • Build a data pipeline in Azure Synapse Analytics
  • Use Spark Notebooks in an Azure Synapse Pipeline
  • Plan hybrid transactional and analytical processing using Azure Synapse Analytics
  • Implement Azure Synapse Link with Azure Cosmos DB
  • Implement Azure Synapse Link for SQL
  • Get started with Azure Stream Analytics
  • Ingest streaming data using Azure Stream Analytics and Azure Synapse Analytics
  • Visualize real-time data with Azure Stream Analytics and Power BI
  • Introduction to Microsoft Purview
  • Integrate Microsoft Purview and Azure Synapse Analytics
  • Explore Azure Databricks
  • Use Apache Spark in Azure Databricks
  • Run Azure Databricks Notebooks with Azure Data Factory

Who Should Attend

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 includes data analysts and data scientists who work with analytical solutions built on Microsoft Azure.

Course Outline

Module 1: Introduction to Data Engineering on Azure

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

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

Module 9: Use Delta Lake in Azure Synapse Analytics

  • 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

  • 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 11: Load data into a relational data warehouse

  • 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 12: Build a data pipeline in Azure Synapse Analytics

  • 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 13: Use Spark Notebooks in an Azure Synapse Pipeline

  • Describe notebook and pipeline integration
  • Use a Synapse notebook activity in a pipeline
  • Use parameters with a notebook activity

Module 14: Plan hybrid transactional and analytical processing using Azure Synapse Analytics

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

Module 15: Implement Azure Synapse Link with Azure Cosmos DB

  • 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 16: Implement Azure Synapse Link for SQL

  • 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 17: Get started with Azure Stream Analytics

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

Module 18: Ingest streaming data using Azure Stream Analytics and Azure Synapse Analytics

  • 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 19: Visualize real-time data with Azure Stream Analytics and Power BI

  • 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 20: Introduction to Microsoft Purview

  • 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 21: Integrate Microsoft Purview and Azure Synapse Analytics

  • 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 22: Explore Azure Databricks

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

Module 23: Use Apache Spark in Azure Databricks

  • 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 24: Run Azure Databricks Notebooks with Azure Data Factory

  • 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

Prerequisites

Successful students start this course with knowledge of cloud computing and core data concepts and professional experience with data solutions. Specifically completing:

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A Certified Penetration Testing Engineer (C)PTE) imagines all of the ways that a hacker can penetrate a data system. In this course you will learn 5 Key Elements of Pen Testing, discover the latest vulnerabilities and the techniques malicious hackers are using to acquire and destroy data and learn more about the business skills needed to identify protection opportunities, justify testing activities and optimize security controls appropriate to the business needs in order to reduce business risk.

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The Certified Secure Web Application Engineer (C)SWAE) course is delivered by high level OWASP experts and students can expect to obtain real world security knowledge that enables them to recognize vulnerabilities, exploit system weaknesses and help safeguard against application threats. You will learn how to develop web applications that aren't subject to common vulnerabilities, and how to test and validate that their applications are secure, reliable and resistant to attack.

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This course is intended for power users and IT professionals who are tasked with working within the SharePoint 2016 environment and conduct site collection and site administration. This course is for an on-premise SharePoint environment.

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This course teaches developers how to create applications using the SQL API and SDK for Azure Cosmos DB. Students will learn how to write efficient queries, create indexing policies, manage and provision resources, and perform common operations with the SDK.

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In this course students will learn the various security, compliance, and identity concepts. After attending you will be able to describe the capabilities of Microsoft’s identity and access management solutions and Microsoft security and compliance solutions.

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This course introduces a process for effectively planning and designing a functional, efficient database. Knowing how to plan a relational database is important to the success of the databases you create. Without planning, you cannot possibly know what the database needs to do, or even what information to include in the database. Planning a database is essential and prevents the extra work of fixing data maintenance problems later on.

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This five-day course describes how to set up, configure and manage an Office 365 tenant including identities, and the core services of Microsoft 365. In this five-day course, you will learn how to plan the configuration of an Office 365 tenant including integration with existing user identities; plan, configure and manage core services; and report on key metrics.

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This five-day instructor-led course provides students who administer and maintain SQL Server databases with the knowledge and skills to administer a SQL server database infrastructure. Additionally, it will be of use to individuals who develop applications that deliver content from SQL Server databases.

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