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

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* Actual course outline may vary depending on offering center. Contact your sales representative for more information.

Learning Objectives

Explore compute and storage options for data engineering workloads in Azure Design and Implement the serving layer Understand data engineering considerations Run interactive queries using serverless SQL pools Explore, transform, and load data into the Data Warehouse using Apache Spark Perform data Exploration and Transformation in Azure Databricks Ingest and load Data into the Data Warehouse Transform Data with Azure Data Factory or Azure Synapse Pipelines Integrate Data from Notebooks with Azure Data Factory or Azure Synapse Pipelines Optimize Query Performance with Dedicated SQL Pools in Azure Synapse Analyze and Optimize Data Warehouse Storage Support Hybrid Transactional Analytical Processing (HTAP) with Azure Synapse Link Perform end-to-end security with Azure Synapse Analytics Perform real-time Stream Processing with Stream Analytics Create a Stream Processing Solution with Event Hubs and Azure Databricks Build reports using Power BI integration with Azure Synpase Analytics Perform Integrated Machine Learning Processes in Azure Synapse Analytics

1

  • Explore compute and storage options for data engineering workloads

  • 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
2

  • Design and implement the serving layer

  • 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
3

  • Data engineering considerations for source files

  • Design a Modern Data Warehouse using Azure Synapse Analytics
    Secure a data warehouse in Azure Synapse Analytics
4

  • Run interactive queries using Azure Synapse Analytics serverless SQL pools

  • 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
5

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

  • 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
6

  • 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
7

  • Ingest and load data into the data warehouse

  • Use data loading best practices in Azure Synapse Analytics
    Petabyte-scale ingestion with Azure Data Factory
8

  • Transform data with Azure Data Factory or Azure Synapse Pipelines

  • Data integration with Azure Data Factory or Azure Synapse Pipelines
    Code-free transformation at scale with Azure Data Factory or Azure Synapse Pipelines
9

  • Orchestrate data movement and transformation in Azure Synapse Pipelines

  • Orchestrate data movement and transformation in Azure Data Factory
10

  • Optimize query performance with dedicated SQL pools in Azure Synapse

  • Optimize data warehouse query performance in Azure Synapse Analytics
    Understand data warehouse developer features of Azure Synapse Analytics
11

  • Analyze and Optimize Data Warehouse Storage

  • Analyze and optimize data warehouse storage in Azure Synapse Analytics
12

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

  • 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
13

  • End-to-end security with Azure Synapse Analytics

  • Secure a data warehouse in Azure Synapse Analytics
    Configure and manage secrets in Azure Key Vault
    Implement compliance controls for sensitive data
14

  • Real-time Stream Processing with Stream 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
15

  • Create a Stream Processing Solution with Event Hubs and Azure Databricks

  • Process streaming data with Azure Databricks structured streaming
16

  • Build reports using Power BI integration with Azure Synpase Analytics

  • Create reports with Power BI using its integration with Azure Synapse Analytics
17

  • Perform Integrated Machine Learning Processes in Azure Synapse Analytics

  • Use the integrated machine learning process in Azure Synapse Analytics

Audience

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.

Language

English

Prerequisites

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

$2,499

Length: 4.0 days (32 hours)

Level:

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