Data Engineering on Microsoft Azure: The Definitive Guide
What you'll learn
- Provision Azure SQL Databases. Use query tools such as Azure Data Studio or SQL Server Management Studio (SSMS) to connect to Azure SQL databases
- Create storage accounts to store unstructured and semi-structured data. use Azure Data Studio to upload data to Azure
- Manage identities, keys, and secrets across different data platform technologies using Azure Key Vault
- Use the Azure CLI to generate Shared Access Signature (SAS). How access storage resources using SAS
- Build ETL pipeline using Azure Data Factory. Create advanced transformation logic using Data Flows
- Trigger a Data Pipeline based on storage events or specific time
- Use T-SQL to query Relational Databases and data in Azure Storage
- Transform data using Azure Synapse Analytics. Create external tables to read or write data to files in Azure Storage
- Build a modern data warehouse using Azure Synapse Analytics
- Analyze data using serverless Apache Spark pool in Synapse Analytics
Requirements
- Microsoft Azure Subscription
- Basic SQL knowledge or programming skills
- No Data Engineering experience is needed. You will learn everything you need to know
Description
Do you want to jump-start your career as an Azure Data Engineer?
Welcome to the Definitive Guide to Data Engineering on Microsoft Azure.
In this course, you will learn how to use the extensive family of Azure Data Services to build a modern data and analytics platform.
I'll take you step-by-step through engaging video tutorials and teach you everything you need to know to succeed as an Azure Data Engineer.
By the time you complete this course, you will be able to:
Architect a Data Solution on Azure
Integrate relational data and unstructured data
Build data processing pipelines using Azure Data Factory
Integrate and transform data from various data systems
Securely access data stores with Azure Key Vault and Azure role-based access control (Azure RBAC)
Perform exploratory data analysis with Azure Synapse Analytics
Manage your costs
The lectures in this course are hands-on and with lots of explanations.
I also use animations to break down complex topics.
There are SQL scripts and Jupyter notebooks that you can use to follow along easily.
You will be working closely with the documentation of Microsoft Azure as it is essential to know how to find the most up-to-date information about any service.
You will learn how to combine a range of Azure services to ingest, store and process data of all types and sizes from any data source.
Throughout this course, we will learn how to use and combine multiple Azure Services and tools, including:
Azure SQL Databases
Azure Storage
Azure Role-Based Access Control - RBAC
Azure Data Studio
Azure Storage Explorer
Azure Cost Management
Azure Data Factory
Azure Key Vault
Azure Managed Identity
Azure Synapse Analytics
Synapse Workspace
Azure Synapse Studio
Serverless SQL Pool
Serverless Apache Spark Pool
Synapse Analytics Dedicated SQL Pool
At the beginning of each Data Service, you will be introduced to the Service, learn what it is for, and then learn how to use it.
Enroll now, and learn
Microsoft, Windows, Microsoft Azure, and all Azure Data Services are either registered trademarks or trademarks of Microsoft group of companies.
This course is not certified, accredited, affiliated with, or endorsed by Microsoft Corporation.
Who this course is for:
- Anyone who wants to start using Azure in their career & get paid for their cloud and Data Engineering Skills
- Software developers curious about Data Engineering
- Database Developer or Database Administrators (DBA)
Instructor
![Wadson Guimatsa](data:image/svg+xml,%3Csvg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 64 64"%3E%3C/svg%3E){kind=avatar}
- 4.3 Instructor Rating
- 3,531 Reviews
- 29,050 Students
- 4 Courses
I'm a software developer specialized in building data-intensive applications.
I've been developing software for over 10 years.
I've worked for Industries that are very data-intensive such as the financials and industrial image processing.
Over the years, the volume of data produced by systems and humans outgrew the storage and compute capacity of the legacy RDBMS systems, and therefore I had to learn how to use the new tools and frameworks to process Big-Data
As a data engineer, I'm very motivated and passionate about building applications that can leverage the power and flexibility of cloud computing and big-data processing frameworks.