Handling Streaming Data with Azure Databricks Using Spark Structured Streaming
Real-time data pipelines are no longer optional—they’re competitive necessity. This course teaches you to build scalable streaming solutions using Azure Databricks and Spark Structured Streaming, moving beyond batch processing into live data architectures that enterprises depend on.
AIU.ac Verdict: Ideal for cloud engineers and data platform builders who need hands-on Spark Structured Streaming expertise without months of trial-and-error. The 2h 27m duration is tight, so you’ll need foundational Spark knowledge; this isn’t an introduction to distributed computing.
What This Course Covers
You’ll work through Spark Structured Streaming fundamentals—micro-batch processing, stateful operations, and windowing functions—all within Azure Databricks’ managed environment. Expect practical labs covering real-world scenarios: handling late-arriving data, managing state across streaming jobs, and optimising for throughput versus latency trade-offs.
The course bridges the gap between theory and production: you’ll learn checkpoint management, fault tolerance patterns, and integration with Azure services (Event Hubs, Cosmos DB). Mohit Batra structures this for immediate application—you’ll leave with deployable patterns for IoT telemetry, clickstream analytics, and financial transaction processing.
Who Is This Course For?
Ideal for:
- Cloud Data Engineers: Building real-time pipelines on Azure who need Spark Structured Streaming depth beyond documentation.
- Platform Architects: Designing streaming infrastructure and need to evaluate Databricks’ capabilities for production workloads.
- Data Platform Leads: Upskilling teams on modern streaming patterns to replace legacy batch-only architectures.
May not suit:
- Spark Beginners: This assumes RDD/DataFrame familiarity; you’ll struggle without prior distributed computing experience.
- Non-Azure Practitioners: Heavy Azure Databricks focus means limited transferability if your stack is Kafka + Flink or AWS Kinesis.
Frequently Asked Questions
How long does Handling Streaming Data with Azure Databricks Using Spark Structured Streaming take?
2 hours 27 minutes of video content. Plan 3–4 hours total including hands-on labs in Databricks sandboxes.
Do I need Azure credits to complete the labs?
Pluralsight provides sandboxed Databricks environments for the course, so no personal Azure spend required. However, testing production patterns afterward will need your own subscription.
What Spark knowledge is required beforehand?
You should be comfortable with DataFrames, SQL, and basic transformations. If you’re new to Spark entirely, take a foundational Spark course first.
Will this cover Kafka integration or just Azure Event Hubs?
Primary focus is Azure Event Hubs and native Databricks sources. Kafka patterns are mentioned but not deeply explored—check course preview for exact scope.
Course by Mohit Batra on Pluralsight. Duration: 2h 27m. Last verified by AIU.ac: March 2026.
