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DP-203 – 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 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.

Course Objectives

After completing this course, you 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

Participant 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 Outline

  • Module 1: Explore compute and storage options for data engineering workloads
  • Module 2: Run interactive queries using Azure Synapse Analytics serverless SQL pools
  • Module 3: Data exploration and transformation in Azure Databricks
  • Module 4: Explore, transform, and load data into the Data Warehouse using Apache Spark
  • Module 5: Ingest and load data into the data warehouse
  • Module 6: Transform data with Azure Data Factory or Azure Synapse Pipelines
  • Module 7: Orchestrate data movement and transformation in Azure Synapse Pipelines
  • Module 8: End-to-end security with Azure Synapse Analytics
  • Module 9: Support Hybrid Transactional Analytical Processing (HTAP) with Azure Synapse Link
  • Module 10: Real-time Stream Processing with Stream Analytics
  • Module 11: Create a Stream Processing Solution with Event Hubs and Azure Databricks

Course Materials

The following materials are included as part of the course;

  • Microsoft Official digital Curriculum (MOC)
  • Lab Set-up

Dinesh Priyankara, MVP, MCT

Founder / Principal Architect at dinesQL (Pvt) Ltd

Dinesh is an experienced professional and database enthusiast with skills in database management systems and business intelligence, especially on the Microsoft SQL Server product suite. Possessing over 16 years of experience on data related technologies, he does training, consulting, and is a top contributor to the local SQL Server community.

DP-203 - Data Engineering on Microsoft Azure

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