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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. Students will begin...

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$2,380 USD GSA  $1,798.49
Course Code DP-203T00
Duration 4 days
Available Formats Classroom, Virtual

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.

Audience Profile

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.

Skills Gained

After completing this course, students will be able to:

  • 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

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

Course Details

Outline

  • Introduction to Azure Synapse Analytics
  • Explore Azure Databricks
  • Introduction to Azure Data Lake Storage Gen2
  • Get started with Azure Stream 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
  • Secure data and manage users in Azure Synapse serverless SQL pools
  • Use Apache Spark in Azure Databricks
  • Use Delta Lake in Azure Databricks
  • Analyze data with Apache Spark in Azure Synapse Analytics
  • Integrate SQL and Apache Spark pools in Azure Synapse Analytics
  • Petabyte-scale ingestion with Azure Data Factory or Azure Synapse Pipeline
  • Integrate data with Azure Data Factory or Azure Synapse Pipeline
  • Perform code-free transformation at scale with Azure Data Factory or Azure Synapse Pipeline
  • Orchestrate data movement and transformation in Azure Data Factory or Azure Synapse Pipeline
  • Plan hybrid transactional and analytical processing using Azure Synapse Analytics
  • Implement Azure Synapse Link with Azure Cosmos DB
  • Secure a data warehouse in Azure Synapse Analytics
  • Configure and manage secrets in Azure Key Vault
  • Implement compliance controls for sensitive data
  • Enable reliable messaging for Big Data applications using Azure Event Hubs