Course Name

Data Engineering on Microsoft Azure

  • Code: DP-203
  • Duration: 4 Day(s)
  • Date: On Request
  • Price: R8, 075.00 (excl. VAT)
Call me back
Get Overview
Book now!

Course Introduction

In this course, the student will learn how to implement and manage data engineering workloads on Microsoft Azure, using Azure services such as Azure Synapse Analytics, Azure Data Lake Storage Gen2, Azure Stream Analytics, Azure Databricks, and others.

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 Delivery Method

Our courses have flexible delivery options:

  • In-person classroom training at the Impactful training
    facilities

  • Virtual instructor-led training
  • Nationally: on-site at the client

Course Intended Audience

This course is intended for:

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 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 Content

  • Introduction to data engineering on Azure
    • Microsoft Azure provides a comprehensive platform for data engineering; but what is data engineering? Complete this module to find out.
  • Introduction to Azure Data Lake Storage Gen2
    • Data lakes are a core element of data analytics architectures. Azure Data Lake Storage Gen2 provides a scalable, secure, cloud-based solution for data lake storage.
  • Introduction to Azure Synapse Analytics
    • Learn about the features and capabilities of Azure Synapse Analytics – a cloud-based platform for big data processing and analysis.
  • Use Azure Synapse serverless SQL pool to query files in a data lake
    • With Azure Synapse serverless SQL pool, you can leverage your SQL skills to explore and analyze data in files, without the need to load the data into a relational database.
  • Use Azure Synapse serverless SQL pools to transform data in a data lake
    • By using a serverless SQL pool in Azure Synapse Analytics, you can use the ubiquitous SQL language to transform data in files in a data lake.
  • Create a lake database in Azure Synapse Analytics
    • Why choose between working with files in a data lake or a relational database schema? With lake databases in Azure Synapse Analytics, you can combine the benefits of both.
  • Analyze data with Apache Spark in Azure Synapse Analytics
    • Apache Spark is a core technology for large-scale data analytics. Learn how to use Spark in Azure Synapse Analytics to analyze and visualize data in a data lake.
  • Transform data with Spark in Azure Synapse Analytics
    • Data engineers commonly need to transform large volumes of data. Apache Spark pools in Azure Synapse Analytics provide a distributed processing platform that they can use to accomplish this goal.
  • Use Delta Lake in Azure Synapse Analytics
    • Delta Lake is an open source relational storage area for Spark that you can use to implement a data lakehouse architecture in Azure Synapse Analytics.
  • Analyze data in a relational data warehouse
    • Relational data warehouses are a core element of most enterprise Business Intelligence (BI) solutions, and are used as the basis for data models, reports, and analysis.
  • Load data into a relational data warehouse
    • A core responsibility for a data engineer is to implement a data ingestion solution that loads new data into a relational data warehouse.
  • Build a data pipeline in Azure Synapse Analytics
    • Pipelines are the lifeblood of a data analytics solution. Learn how to use Azure Synapse Analytics pipelines to build integrated data solutions that extract, transform, and load data across diverse systems.
  • Use Spark Notebooks in an Azure Synapse Pipeline
    • Apache Spark provides data engineers with a scalable, distributed data processing platform, which can be integrated into an Azure Synapse Analytics pipeline.
  • Plan hybrid transactional and analytical processing using Azure Synapse Analytics
    • Learn how hybrid transactional / analytical processing (HTAP) can help you perform operational analytics with Azure Synapse Analytics.
  • Implement Azure Synapse Link with Azure Cosmos DB
    • Azure Synapse Link for Azure Cosmos DB enables HTAP integration between operational data in Azure Cosmos DB and Azure Synapse Analytics runtimes for Spark and SQL.
  • Implement Azure Synapse Link for SQL
    • Azure Synapse Link for SQL enables low-latency synchronization of operational data in a relational database to Azure Synapse Analytics.
  • Get started with Azure Stream Analytics
    • Azure Stream Analytics enables you to process real-time data streams and integrate the data they contain into applications and analytical solutions.
  • Ingest streaming data using Azure Stream Analytics and Azure Synapse Analytics
    • Azure Stream Analytics provides a real-time data processing engine that you can use to ingest streaming event data into Azure Synapse Analytics for further analysis and reporting.
  • Visualize real-time data with Azure Stream Analytics and Power BI
    • By combining the stream processing capabilities of Azure Stream Analytics and the data visualization capabilities of Microsoft Power BI, you can create real-time data dashboards.
  • Introduction to Microsoft Purview
    • In this module, you’ll evaluate whether Microsoft Purview is the right choice for your data discovery and governance needs.
  • Integrate Microsoft Purview and Azure Synapse Analytics
    • Learn how to integrate Microsoft Purview with Azure Synapse Analytics to improve data discoverability and lineage tracking.
  • Explore Azure Databricks
    • Azure Databricks is a cloud service that provides a scalable platform for data analytics using Apache Spark.
  • Use Apache Spark in Azure Databricks
    • Azure Databricks is built on Apache Spark and enables data engineers and analysts to run Spark jobs to transform, analyze and visualize data at scale.
  • Run Azure Databricks Notebooks with Azure Data Factory
    • Using pipelines in Azure Data Factory to run notebooks in Azure Databricks enables you to automate data engineering processes at cloud scale.
L❤ ve this. Share it now!

Need additional information?

We are here to support your growth every step of the way

Please call me

Get in touch

Contact the Impactful team if you need any assistance.
Email: impactful@lrmg.co.za
Tel: +27 87 941 5764

Course Introduction

In this course, the student will learn how to implement and manage data engineering workloads on Microsoft Azure, using Azure services such as Azure Synapse Analytics, Azure Data Lake Storage Gen2, Azure Stream Analytics, Azure Databricks, and others.

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 Delivery Method

Our courses have flexible delivery options:

  • In-person classroom training at the Impactful training
    facilities

  • Virtual instructor-led training
  • Nationally: on-site at the client

Course Intended Audience

This course is intended for:

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 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 Content

  • Introduction to data engineering on Azure
    • Microsoft Azure provides a comprehensive platform for data engineering; but what is data engineering? Complete this module to find out.
  • Introduction to Azure Data Lake Storage Gen2
    • Data lakes are a core element of data analytics architectures. Azure Data Lake Storage Gen2 provides a scalable, secure, cloud-based solution for data lake storage.
  • Introduction to Azure Synapse Analytics
    • Learn about the features and capabilities of Azure Synapse Analytics – a cloud-based platform for big data processing and analysis.
  • Use Azure Synapse serverless SQL pool to query files in a data lake
    • With Azure Synapse serverless SQL pool, you can leverage your SQL skills to explore and analyze data in files, without the need to load the data into a relational database.
  • Use Azure Synapse serverless SQL pools to transform data in a data lake
    • By using a serverless SQL pool in Azure Synapse Analytics, you can use the ubiquitous SQL language to transform data in files in a data lake.
  • Create a lake database in Azure Synapse Analytics
    • Why choose between working with files in a data lake or a relational database schema? With lake databases in Azure Synapse Analytics, you can combine the benefits of both.
  • Analyze data with Apache Spark in Azure Synapse Analytics
    • Apache Spark is a core technology for large-scale data analytics. Learn how to use Spark in Azure Synapse Analytics to analyze and visualize data in a data lake.
  • Transform data with Spark in Azure Synapse Analytics
    • Data engineers commonly need to transform large volumes of data. Apache Spark pools in Azure Synapse Analytics provide a distributed processing platform that they can use to accomplish this goal.
  • Use Delta Lake in Azure Synapse Analytics
    • Delta Lake is an open source relational storage area for Spark that you can use to implement a data lakehouse architecture in Azure Synapse Analytics.
  • Analyze data in a relational data warehouse
    • Relational data warehouses are a core element of most enterprise Business Intelligence (BI) solutions, and are used as the basis for data models, reports, and analysis.
  • Load data into a relational data warehouse
    • A core responsibility for a data engineer is to implement a data ingestion solution that loads new data into a relational data warehouse.
  • Build a data pipeline in Azure Synapse Analytics
    • Pipelines are the lifeblood of a data analytics solution. Learn how to use Azure Synapse Analytics pipelines to build integrated data solutions that extract, transform, and load data across diverse systems.
  • Use Spark Notebooks in an Azure Synapse Pipeline
    • Apache Spark provides data engineers with a scalable, distributed data processing platform, which can be integrated into an Azure Synapse Analytics pipeline.
  • Plan hybrid transactional and analytical processing using Azure Synapse Analytics
    • Learn how hybrid transactional / analytical processing (HTAP) can help you perform operational analytics with Azure Synapse Analytics.
  • Implement Azure Synapse Link with Azure Cosmos DB
    • Azure Synapse Link for Azure Cosmos DB enables HTAP integration between operational data in Azure Cosmos DB and Azure Synapse Analytics runtimes for Spark and SQL.
  • Implement Azure Synapse Link for SQL
    • Azure Synapse Link for SQL enables low-latency synchronization of operational data in a relational database to Azure Synapse Analytics.
  • Get started with Azure Stream Analytics
    • Azure Stream Analytics enables you to process real-time data streams and integrate the data they contain into applications and analytical solutions.
  • Ingest streaming data using Azure Stream Analytics and Azure Synapse Analytics
    • Azure Stream Analytics provides a real-time data processing engine that you can use to ingest streaming event data into Azure Synapse Analytics for further analysis and reporting.
  • Visualize real-time data with Azure Stream Analytics and Power BI
    • By combining the stream processing capabilities of Azure Stream Analytics and the data visualization capabilities of Microsoft Power BI, you can create real-time data dashboards.
  • Introduction to Microsoft Purview
    • In this module, you’ll evaluate whether Microsoft Purview is the right choice for your data discovery and governance needs.
  • Integrate Microsoft Purview and Azure Synapse Analytics
    • Learn how to integrate Microsoft Purview with Azure Synapse Analytics to improve data discoverability and lineage tracking.
  • Explore Azure Databricks
    • Azure Databricks is a cloud service that provides a scalable platform for data analytics using Apache Spark.
  • Use Apache Spark in Azure Databricks
    • Azure Databricks is built on Apache Spark and enables data engineers and analysts to run Spark jobs to transform, analyze and visualize data at scale.
  • Run Azure Databricks Notebooks with Azure Data Factory
    • Using pipelines in Azure Data Factory to run notebooks in Azure Databricks enables you to automate data engineering processes at cloud scale.

Are you ready to start?

Sign Up
Download Overview

Certified global best practices in the new technologies…

Amazon Web Services Impactful Partner
PeopleCert Logo
Impactful is an Accredited Oracle University Partner
Impactful Partner Microsoft logo

Get ahead with your IT and Digital Talent development

Please complete the form with your information and one of our experts will get back to you soon.

Get in touch

Contact the Impactful team if you need any assistance.

Email: impactful@lrmg.co.za
Tel: +27 87 941 5764

About LRMG

Testing Elementor conditions

Testing Elementor conditions

Testing Elementor conditions

Testing Elementor conditions

CONTACT US