Spark Streaming – Stream Processing in Lakehouse – PySpark
Overview of Spark Streaming – Stream Processing in Lakehouse – PySpark Course on Udemy
Unlock the power of real-time data processing with the Spark Streaming – Stream Processing in Lakehouse – PySpark course on Udemy. This course teaches you how to master Spark Structured Streaming using Python (PySpark) on Azure Databricks Cloud, complete with an end-to-end capstone project. Designed for data engineers and developers, it provides a hands-on, example-driven approach to building real-time stream processing solutions in a Lakehouse architecture, leveraging Apache Spark and Databricks.
The course includes 22.5 hours of on-demand video, 10 downloadable resources, and is led by Prashant Kumar Pandey, founder of Learning Journal and a seasoned data engineering expert with over 18 years of IT experience. Enroll today with udemy coupon 25MAY2025 (valid until May 31, 2025—check the offer box below for the discount link!)
What to Expect from the Spark Streaming – Stream Processing in Lakehouse – PySpark Course
This 22-hour course offers a practical, hands-on learning experience, perfect for data engineers, software developers, and architects looking to master real-time stream processing. Prashant Kumar Pandey’s teaching style is engaging and live-coding focused, breaking down complex concepts into manageable steps. The course targets those with basic Spark or Python knowledge, aiming to build expertise in stream processing within a Lakehouse environment.
Key features include a comprehensive end-to-end capstone project, live coding sessions, and practical exercises using Azure Databricks Cloud with Spark 3.5. Hosted on Udemy’s flexible platform, you can learn at your own pace, revisit lessons, and access resources anytime, making it ideal for professionals balancing busy schedules.
What You Will Learn in Spark Streaming – Stream Processing in Lakehouse – PySpark
- Understand the fundamentals of Spark Structured Streaming and its role in real-time data processing.
- Build and deploy stream processing pipelines using PySpark on Azure Databricks.
- Implement an end-to-end streaming project within a Lakehouse architecture.
- Master key streaming concepts like event-time processing and watermarking.
- Integrate Spark with external systems for real-time data ingestion and processing.
- Apply CI/CD practices to deploy and manage streaming applications.
Why Choose This Spark Streaming – Stream Processing in Lakehouse – PySpark Course on Udemy
This course stands out due to Prashant Kumar Pandey’s extensive industry experience and his ability to deliver clear, practical instruction. The content is regularly updated to align with Apache Spark 3.5 and Databricks advancements, ensuring relevance in the fast-evolving big data landscape. With 22 hours of video and 10 downloadable resources, it offers immense value for mastering stream processing. The capstone project and live coding approach make it ideal for building job-ready skills in data engineering.
Use udemy coupon 25MAY2025 to get at a discount (see offer box)
Recommended Courses with Spark Streaming and Data Engineering Focus
Looking to expand your skills? Check out these related courses:
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- Apache Spark 3 – Spark Programming in Python for Beginners – Master PySpark fundamentals with hands-on Databricks projects.
- Data Engineering using Kafka and Spark Structured Streaming – Build streaming pipelines with Kafka and Spark on cloud platforms.
- Azure Databricks & Spark For Data Engineers: Hands-on Project – Learn PySpark and Spark SQL with real-world Formula 1 data projects.
Our Review of Spark Streaming – Stream Processing in Lakehouse – PySpark Course
As website admins, we’re impressed by the course’s practical, project-driven structure. Prashant Kumar Pandey’s live coding sessions and clear explanations make complex streaming concepts accessible, while the capstone project provides real-world applicability. The use of Azure Databricks and Spark 3.5 ensures learners work with cutting-edge tools, and the course’s focus on CI/CD practices adds professional relevance. It’s an excellent choice for data engineers aiming to specialize in stream processing.
Pros:
- Hands-on capstone project simulates real-world streaming scenarios.
- Up-to-date content with Spark 3.5 and Azure Databricks integration.
- Live coding approach enhances understanding of complex concepts.
Cons:
- Requires basic Spark or Python knowledge, which may challenge absolute beginners.
- Limited focus on advanced streaming integrations like Kafka, requiring supplementary learning.
With udemy coupon 25MAY2025, it’s a steal
Rating the Spark Streaming – Stream Processing in Lakehouse – PySpark Course
Content: 9/10 – Comprehensive coverage of Spark Structured Streaming with a practical capstone project.
Delivery: 8.8/10 – Prashant’s live coding is engaging, though pacing may be fast for some learners.
Value: 9.5/10 – Affordable with udemy coupon 25MAY2025.
Master real-time data processing with this hands-on PySpark course—enroll now and build cutting-edge streaming solutions today
