CF

Data Engineering on Azure

1h 20m 57s
English
Paid
Updated September 2026

Data Engineering on Azure is a 14-lesson 1 hour 20 minutes self-paced course by Kristijan Bakarić. Azure covers over 200 products and services for data storage, processing, and app development, and this course by Kristijan Bakarić focuses on one practical build: a streaming pipeline processing JSON-formatted Twitter-style data.

Course facts

Lessons
14
Duration
1 hour 20 minutes
Level
All levels
Language
English
Updated
2026-09-11
Instructor
Kristijan Bakarić
Price
Premium

Azure covers over 200 products and services for data storage, processing, and app development, and this course by Kristijan Bakarić focuses on one practical build: a streaming pipeline processing JSON-formatted Twitter-style data.

Azure services you'll use

  • API Management (APIM) for handling data intake
  • Blob Storage for secure, scalable storage
  • Azure Functions for the processing logic
  • Cosmos DB for storing processed data
  • Power BI for visualizing the results

How the course is structured

After an architecture overview, you'll build a Python script to send JSON data via HTTP into Azure API Management, then develop and deploy Azure Functions in Python using VS Code. From there you'll connect Event Hubs, Functions, and Cosmos DB so messages flow end to end, finishing with Power BI connected directly to Cosmos DB for real-time visualization.

Across 14 lessons and a companion GitHub repo, you'll need an active Azure account, basic Python, some familiarity with data storage and API concepts, and a general sense of how message queues work going in.

Additional

https://github.com/team-data-science/azure-data-engineering

Who teaches Data Engineering on Azure? Kristijan Bakarić

Kristijan Bakarić thumbnail

Kristijan Bakarić is a Croatian data engineer and educator focused on the Microsoft Azure data-engineering stack — the combination of Azure-specific data services (Data Factory, Synapse, Databricks on Azure) that anchors enterprise data work on the Microsoft cloud.

His CourseFlix listing carries Data Engineering on Azure — a structured treatment of building production data pipelines on Azure: the service selection trade-offs, the orchestration patterns, the storage layers (ADLS, Delta), and the operational patterns for running Azure data infrastructure.

Material is paid and aimed at data engineers working on the Microsoft Azure cloud. For broader content, see CourseFlix's Azure and Data processing and analysis category pages.

What lessons are included in Data Engineering on Azure?

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#1: Data Engineering in Azure - Streaming Data Pipelines
All Course Lessons (14)
#Lesson TitleDurationAccess
1
Data Engineering in Azure - Streaming Data Pipelines Demo
02:44
2
Introduction to Datasets and Local Preprocessing
07:07
3
Deploying your Code on Visual Studio to Docker containers
05:28
4
Develop Azure Functions via Python and VS Code
05:53
5
Deploy Azure Function to Azure Function App and Test it
06:27
6
Integrate Azure Function with Blob Storage via bindings
04:59
7
Expose Azure Function as a Backend, and Test it from Insomnia
07:06
8
Securely Store Secrets in Azure Key Vault and Connect APIM to Key Vault
04:42
9
Add Basic authentication in API Management using Key Vault and Named Values
04:36
10
Test APIM and Imported Azure Function App and Function via Local Python Program
02:35
11
Create Event Hubs and Test Capture Events Feature
07:00
12
Modify Existing Azure Function to Include Event Hubs Binding and Test It
06:43
13
Create a Cosmos DB (Core SQL) and Create a New Azure Function that writes Messages to Cosmos DB
09:04
14
Connect Power Bi Desktop via Connector, and create a dashboard
06:33
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Frequently asked questions

What are the prerequisites for enrolling in this course?
To enroll in this course, you should have a basic understanding of cloud platforms and some experience with Python programming. Familiarity with Microsoft Azure services is beneficial but not required, as the course will guide you through using Azure-specific tools such as Azure Functions and Cosmos DB.
What kind of projects will I build during the course?
During the course, you will build a streaming data processing pipeline using Azure services. This involves creating a Python script to send JSON data streams to Azure API Management, deploying Azure Functions, integrating with Blob Storage, and visualizing data with Power BI. A key project is developing a function that processes Twitter data streams and stores them in Cosmos DB.
Who is the target audience for this course?
This course is designed for data engineers and developers interested in building scalable data solutions using Microsoft Azure. It is also suitable for IT professionals who wish to enhance their skills in cloud-based data processing and visualization.
How does the course's depth compare to similar courses?
The course offers a detailed exploration of building a data processing pipeline on Azure, focusing on hands-on implementation using Azure Functions, Blob Storage, and Cosmos DB. Unlike general cloud courses, it provides specific insights into integrating Azure services for real-time data processing, making it more specialized in streaming data solutions.
What tools and platforms will I learn to use?
You will learn to use several key Azure services including API Management for data intake, Blob Storage for data storage, Azure Functions for executing processing logic, Cosmos DB for data storage, and Power BI for data visualization. Additionally, tools like Visual Studio Code and Docker containers are part of the development process.
What topics are not covered in this course?
The course does not cover topics outside the scope of Microsoft Azure, such as other cloud providers like AWS or Google Cloud. It also does not cover advanced data science techniques or in-depth machine learning algorithms, focusing instead on building and integrating Azure-based data pipelines.
What is the expected time commitment for the course?
The course consists of 14 lessons and, while the exact runtime is unspecified, you can expect to spend several hours on each lesson. This includes time for lectures, hands-on projects, and practical exercises to apply what you have learned.