Building Streaming Data Pipelines in Microsoft Azure
Real-time data processing is no longer optional—it’s competitive advantage. This course teaches you to architect and deploy production-grade streaming pipelines on Azure, from ingestion through transformation to consumption, using industry-standard tools and patterns.
AIU.ac Verdict: Essential for cloud engineers and data professionals moving beyond batch processing into event-driven architectures. You’ll gain hands-on Azure Event Hubs and Stream Analytics expertise. Note: assumes foundational Azure knowledge; not a beginner’s introduction to cloud.
What This Course Covers
You’ll work through Azure’s streaming ecosystem: Event Hubs for high-throughput ingestion, Stream Analytics for real-time transformation, and integration patterns with Kafka and other sources. The course covers windowing functions, stateful processing, and scaling considerations—critical for handling millions of events per second in production environments.
Practical modules focus on building end-to-end pipelines: configuring partitioning strategies, implementing error handling, monitoring pipeline health, and optimising costs. You’ll deploy working solutions in Azure sandboxes, bridging the gap between theory and the architectures you’ll own in enterprise settings.
Who Is This Course For?
Ideal for:
- Cloud engineers transitioning to data roles: You understand Azure fundamentals and need to specialise in real-time data infrastructure.
- Data engineers building event-driven systems: You’re moving beyond batch ETL and need Azure-native streaming patterns and best practices.
- Solutions architects designing IoT or financial systems: You need to evaluate and recommend streaming architectures that handle high-velocity, mission-critical data.
May not suit:
- Azure beginners with no cloud experience: This assumes you’re comfortable with Azure services, subscriptions, and basic networking concepts.
- Batch-only data professionals seeking introductory content: The course jumps into production patterns; foundational streaming concepts aren’t covered in depth.
Frequently Asked Questions
How long does Building Streaming Data Pipelines in Microsoft Azure take?
3 hours 2 minutes of video content. Most learners complete it over 2–3 days, including hands-on labs in Azure sandboxes.
What Azure services will I learn?
Event Hubs, Stream Analytics, and integration with Kafka. You’ll also cover monitoring, scaling, and cost optimisation specific to streaming workloads.
Do I need Azure experience before starting?
Yes. You should be comfortable with Azure portal navigation, subscriptions, and basic resource management. This isn’t an Azure fundamentals course.
Will I build real pipelines?
Absolutely. Pluralsight’s hands-on labs let you deploy and test streaming solutions in live Azure sandboxes—no local setup required.
Is this relevant for on-premises Kafka users?
Yes. The course covers Kafka integration patterns, so you can bridge on-premises streaming infrastructure with Azure services.
Course by Reza Salehi on Pluralsight. Duration: 3h 2m. Last verified by AIU.ac: March 2026.
