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Microsoft DP-203T00 - Data Engineering on Microsoft Azure

  • Length 4 days
  • Price  NZD 3250 exc GST
Course overview
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Why study this course

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.

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What you’ll learn

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


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Microsoft Azure at Lumify Work

Lumify Work has been delivering effective training across all Microsoft products for over 30 years. We are proud to be both Australia's and New Zealand’s first Microsoft Gold Learning Solutions Partner and the winner of the Microsoft MCT Superstars Award for FY24, which formally recognises us as having the highest quality Microsoft Certified Trainers (MCTs) in ANZ. All Lumify Work Microsoft Azure courses follow Microsoft Official Curriculum (MOC) and are led by MCTs.


Who is the course for?

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


Course subjects

  • Introduction to data engineering on Azure

  • Introduction to Azure Data Lake Storage Gen2

  • Introduction to Azure Synapse 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

  • Analyse data with Apache Spark in Azure Synapse Analytics

  • Transform data with Spark in Azure Synapse Analytics

  • Use Delta Lake in Azure Synapse Analytics

  • Analyse data in a relational data warehouse

  • Load data into a relational data warehouse

  • Build a data pipeline in Azure Synapse Analytics

  • Use Spark Notebooks in an Azure Synapse Pipeline

  • Plan hybrid transactional and analytical processing using Azure Synapse Analytics

  • Implement Azure Synapse Link with Azure Cosmos DB

  • Implement Azure Synapse Link for SQL

  • Get started with Azure Stream Analytics

  • Ingest streaming data using Azure Stream Analytics and Azure Synapse Analytics

  • Visualise real-time data with Azure Stream Analytics and Power BI

  • Introduction to Microsoft Purview

  • Integrate Microsoft Purview and Azure Synapse Analytics

  • Explore Azure Databricks

  • Use Apache Spark in Azure Databricks

  • Run Azure Databricks Notebooks with Azure Data Factory


Prerequisites

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

Specifically completing:


Terms & Conditions

The supply of this course by Lumify Work is governed by the booking terms and conditions. Please read the terms and conditions carefully before enrolling in this course, as enrolment in the course is conditional on acceptance of these terms and conditions.


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