Programming & Development

DP-203T00 Data Engineering on Microsoft Azure


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 students will also learn the various ways they can transform the data using the same technologies that is used to ingest data. The student will spend time on the course learning how to monitor and analyze the performance of analytical system so that they can optimize the performance of data loads, or queries that are issued against the systems. 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 the data in an analytical system can be used to create dashboards, or build predictive models in Azure Synapse Analytics.

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 Objectives
Explore compute and storage options for data engineering workloads in Azure
Run interactive queries using serverless SQL pools
Perform data Exploration and Transformation in Azure Databricks
Explore, transform, and load data into the Data Warehouse using Apache Spark
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
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

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

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

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

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

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

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

Orchestrate data movement and transformation in Azure Data Factory

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

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

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

Process streaming data with Azure Databricks structured streaming

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