About the course
This Azure Data engineering course provides hands-on training for professionals looking to design and implement data solutions using Microsoft Azure services. Participants will gain skills in data ingestion, transformation, storage, and security while working with Azure Synapse Analytics, Data Lake, Databricks, and more.
This course is ideal for aspiring and experienced data engineers, cloud professionals, and data analysts looking to enhance their expertise in Azure-based data engineering solutions.
By the end of the course, participants will have the skills needed to design and implement end-to-end data pipelines and will be well-prepared for the Microsoft DP-203 certification exam.
Talk to us about customising this course to meet your learning goals and business needs.
Online and in-house face-to-face options are available - as part of a wider customised training programme, or as a standalone workshop, on-site at your offices or at one of many flexible meeting spaces in the UK and around the World.
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- Grasp Data Engineering Fundamentals: Understand the role of a data engineer and core concepts of modern data architectures, including batch and stream processing.
- Select Azure Data Services: Identify and choose appropriate Azure storage services (Blob, Data Lake, SQL, Cosmos DB) for diverse data engineering needs.
- Implement Data Ingestion Pipelines: Design and build automated data ingestion and ETL pipelines using Azure Data Factory (ADF).
- Perform Large-Scale Data Transformation: Utilise Apache Spark on Azure Databricks or Azure Synapse Analytics for efficient, large-scale data transformation.
- Design & Query Azure Data Warehouses: Design enterprise data warehouses and query data effectively using Azure Synapse Analytics SQL pools.
- Secure Azure Data Solutions: Implement robust security measures, including RBAC, data encryption, and monitoring, for data in Azure.
- Build Real-Time Streaming Pipelines: Develop and manage end-to-end real-time data streaming pipelines using Azure Stream Analytics and Event Hubs/Kafka.
- Optimise Performance & Manage Costs: Apply techniques to optimise query performance and manage compute/storage costs across Azure data services.
- Design End-to-End Data Pipelines: Architect and design a comprehensive data engineering solution integrating various Azure data services.
- Prepare for Azure Data Engineering Certification: Review key concepts and prepare for the Microsoft Azure Data Engineering (DP-203) certification exam.
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This course is aimed at Data Engineers, Data Architects, Business Intelligence Professionals, and Software Developers working on Microsoft data solutions.
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Delegates should have some prior experience working with data, databases and cloud technologies:
Basic knowledge of Azure
Some familiarity with SQL and Python
Understanding of ETL and Data Warehousing concepts
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This Azure Data Engineering course is available for private / custom delivery for your team - as an in-house face-to-face workshop at your location of choice, or as online instructor-led training via MS Teams (or your own preferred platform).
Get in touch to find out how we can deliver tailored training which focuses on your project requirements and learning goals.
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Introduction to Data Engineering on Azure
Module 1: Understanding Data Engineering Concepts
Role of a Data Engineer
Overview of Modern Data Architectures
Batch vs. Stream Processing
Module 2: Introduction to Azure Data Services
Overview of Azure Storage Services (Blob, Data Lake)
Introduction to Azure SQL and Cosmos DB
Choosing the Right Storage Solution
Hands-on Lab:
Setting up an Azure Storage Account
Creating and Managing Data in Azure Blob Storage
Data Ingestion and Transformation
Module 3: Data Ingestion Strategies
Using Azure Data Factory (ADF) for ETL Pipelines
Ingesting Data from Various Sources (On-Prem, Cloud, APIs)
Automating Data Workflows
Module 4: Data Transformation with Azure Databricks or Synapse Analytics
Introduction to Apache Spark on Azure
Using Notebooks for Data Transformation
Optimizing and Scaling Spark Jobs
Hands-on Lab:
Creating an ADF or Synapse Pipeline for Data Ingestion
Running a Spark Job on Azure Databricks or Synapse Analytics
Data Storage, Management, and Security
Module 5: Azure Synapse Analytics & Data Warehousing
Introduction to Synapse Analytics Dedicated SQL Pools
Designing a Data Warehouse
Querying Data with Serverless SQL Pools
Module 6: Securing Data in Azure
Role-Based Access Control (RBAC)
Data Encryption and Compliance
Monitoring & Auditing Data Access
Hands-on Lab:
Creating a Synapse Analytics Workspace
Implementing Security Measures for Data Protection
Real-Time Analytics and Optimisation
Module 7: Stream Processing with Azure
Introduction to Azure Stream Analytics
Processing Real-Time Data with Event Hubs & Kafka
Building End-to-End Streaming Pipelines
Module 8: Performance Optimization & Cost Management
Query Optimization in Azure SQL & Synapse
Managing Compute and Storage Costs
Monitoring and Debugging Performance Issues
Hands-on Lab:
Deploying a Stream Analytics Job
Optimizing Queries for Faster Performance
Hands-on Project and Certification Readiness (Optional day)
Module 9: Designing an End-to-End Data Pipeline
Participants will work in teams to design a full-scale data engineering solution using the learned concepts.
Review and feedback from the instructor.
Module 10: Exam Preparation (DP-203 Focused Review)
Review Key Concepts and Exam Format
Practice Questions and Mock Test
Q&A Session for Doubts
Final Project Presentation and Wrap-Up
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https://azure.microsoft.com/en-us/products/data-studio - Data Analytics tooling from Microsoft
https://azure.microsoft.com/en-au/solutions/databases/ - Azure Database Solutions
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