Processing Streaming Data with Apache Spark on Databricks
Real-time data pipelines are no longer optional—they’re critical infrastructure. This course cuts through the complexity of Apache Spark streaming to show you exactly how to build, optimise, and deploy production-grade data pipelines on Databricks. You’ll move from theory to working code in just over 2 hours.
AIU.ac Verdict: Ideal for data engineers and analytics engineers who need to handle streaming workloads without months of trial-and-error. The course is practical and hands-on, though it assumes solid foundational knowledge of Spark and SQL—complete beginners may need prerequisite study.
What This Course Covers
You’ll explore structured streaming fundamentals, including how Spark processes unbounded data as micro-batches, and how to leverage DataFrames and SQL for streaming transformations. The course covers windowing operations, stateful processing, and handling late-arriving data—all critical for real-world scenarios like fraud detection, IoT telemetry, and clickstream analysis.
Beyond the mechanics, you’ll learn deployment patterns on Databricks, including checkpoint management, error handling, and performance tuning. Janani Ravi walks through practical examples that translate directly to production environments, so you’re not just learning concepts—you’re learning what actually works at scale.
Who Is This Course For?
Ideal for:
- Data Engineers: Building or maintaining real-time ETL pipelines and need Spark streaming expertise fast.
- Analytics Engineers: Transitioning from batch to streaming architectures and want hands-on Databricks experience.
- Platform/ML Engineers: Needing to ingest and process live data feeds for feature stores or real-time ML models.
May not suit:
- Spark Beginners: This assumes you’re already comfortable with RDDs, DataFrames, and SQL—start with Spark fundamentals first.
- Batch-Only Practitioners: If you’ve never worked with streaming concepts, the pacing may feel steep without prior exposure to event-time semantics.
Frequently Asked Questions
How long does Processing Streaming Data with Apache Spark on Databricks take?
The course is 2 hours and 1 minute. Most learners complete it in one or two sittings, though hands-on lab time may extend that depending on how deeply you experiment.
Do I need a Databricks account to take this course?
Yes. Pluralsight provides sandbox environments for labs, but you’ll benefit most from having a Databricks workspace to apply these patterns to your own data.
What Spark experience do I need beforehand?
You should be comfortable with Spark DataFrames, basic SQL, and transformations. If you’re new to Spark entirely, complete a Spark fundamentals course first.
Will this cover Kafka integration?
The course focuses on Spark Structured Streaming and Databricks-native patterns. Kafka integration is touched on conceptually, but the emphasis is on Databricks sources and sinks.
Course by Janani Ravi on Pluralsight. Duration: 2h 1m. Last verified by AIU.ac: March 2026.
