DP-203 Data Engineering on Microsoft Azure

In this course, the student will learn about the data engineering as it pertains to working with batch and real-time analytical solutions using Azure data platform technologies.

Overview

In this course, the student will learn about the data engineering 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. 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. 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 to create a real-time analytical system to create real-time analytical solutions.

Prerequisites

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

Specifically completing:

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

Course Outline

    • Introduction to Azure Synapse Analytics
    • Describe Azure Databricks
    • Introduction to Azure Data Lake storage
    • Describe Delta Lake architecture
    • Work with data streams by using Azure Stream Analytics

Lab: Explore compute and storage options for data engineering workloads

  • Design a multidimensional schema to optimize analytical workloads
  • Code-free transformation at scale with Azure Data Factory
  • Populate slowly changing dimensions in Azure Synapse Analytics pipelines

Lab: Designing and Implementing the Serving Layer

  • Design a Modern Data Warehouse using Azure Synapse Analytics
  • Secure a data warehouse in Azure Synapse Analytics

Lab: Data engineering considerations

  • Explore Azure Synapse serverless SQL pools capabilities
  • Query data in the lake using Azure Synapse serverless SQL pools
  • Create metadata objects in Azure Synapse serverless SQL pools
  • Secure data and manage users in Azure Synapse serverless SQL pools

Lab: Run interactive queries using serverless SQL pools

  • Understand big data engineering with Apache Spark in Azure Synapse Analytics
  • Ingest data with Apache Spark notebooks in Azure Synapse Analytics
  • Transform data with DataFrames in Apache Spark Pools in Azure Synapse Analytics
  • Integrate SQL and Apache Spark pools in Azure Synapse Analytics

Lab: Explore, transform, and load data into the Data Warehouse using Apache Spark

  • Describe Azure Databricks
  • Read and write data in Azure Databricks
  • Work with DataFrames in Azure Databricks
  • Work with DataFrames advanced methods in Azure Databricks

Lab: Data Exploration and Transformation in Azure Databricks

  • Describe Azure Databricks
  • Read and write data in Azure Databricks
  • Work with DataFrames in Azure Databricks
  • Work with DataFrames advanced methods in Azure Databricks

Lab: Data Exploration and Transformation in Azure Databricks

  • Use data loading best practices in Azure Synapse Analytics
  • Petabyte-scale ingestion with Azure Data Factory

Lab: Ingest and load Data into the Data Warehouse

  • Data integration with Azure Data Factory or Azure Synapse Pipelines
  • Code-free transformation at scale with Azure Data Factory or Azure Synapse Pipelines

Lab: Transform Data with Azure Data Factory or Azure Synapse Pipelines

  • Orchestrate data movement and transformation in Azure Data Factory

Lab: Orchestrate data movement and transformation in Azure Synapse Pipelines

  • Optimize data warehouse query performance in Azure Synapse Analytics
  • Understand data warehouse developer features of Azure Synapse Analytics

Lab: Optimize Query Performance with Dedicated SQL Pools in Azure Synapse

  • Analyze and optimize data warehouse storage in Azure Synapse Analytics

Lab: Analyze and Optimize Data Warehouse Storage

  • Design hybrid transactional and analytical processing using Azure Synapse Analytics
  • Configure Azure Synapse Link with Azure Cosmos DB
  • Query Azure Cosmos DB with Apache Spark pools
  • Query Azure Cosmos DB with serverless SQL pools

Lab: Support Hybrid Transactional Analytical Processing (HTAP) with Azure Synapse Link

  • Secure a data warehouse in Azure Synapse Analytics
  • Configure and manage secrets in Azure Key Vault
  • Implement compliance controls for sensitive data

Lab: End-to-end security with Azure Synapse Analytics

  • Enable reliable messaging for Big Data applications using Azure Event Hubs
  • Work with data streams by using Azure Stream Analytics
  • Ingest data streams with Azure Stream Analytics

Lab: Real-time Stream Processing with Stream Analytics

  • Process streaming data with Azure Databricks structured streaming

Lab: Create a Stream Processing Solution with Event Hubs and Azure Databricks

  • Create reports with Power BI using its integration with Azure Synapse Analytics

Lab: Build reports using Power BI integration with Azure Synapse Analytics

  • Use the integrated machine learning process in Azure Synapse Analytics

Lab: Perform Integrated Machine Learning Processes in Azure Synapse Analytics

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