Real-Time Data Processing

Course Overview

Real‑Time Data Processing is a hands‑on, engineering‑focused course that teaches learners how to design, build, and operate streaming data pipelines that process information as it arrives. The course covers event‑driven architectures, message brokers, stream processing frameworks, low‑latency data ingestion, stateful computations, and real‑time analytics. Learners work with tools such as Apache Kafka, Apache Spark Structured Streaming, Apache Flink, and cloud‑native streaming services to build production‑grade pipelines.

Target Audience

This course is ideal for:

• Aspiring data engineers and streaming‑platform developers

• Big‑data engineers modernizing batch pipelines to real‑time architectures

• Cloud engineers working with event‑driven systems

• Data analysts and data scientists needing real‑time insights

• Anyone preparing for roles in data engineering, streaming analytics, or IoT data processing

Course Outcomes

By the end of this course, learners will be able to:

• Understand the fundamentals of real‑time vs. batch processing

• Build event‑driven architectures using message brokers like Kafka or Kinesis

• Implement streaming ETL pipelines using Spark Structured Streaming or Flink

• Work with concepts such as event time, watermarks, windows, and stateful processing

• Ingest and process high‑velocity data from logs, sensors, APIs, and IoT devices

• Design fault‑tolerant, scalable, and low‑latency streaming systems

• Integrate real‑time pipelines with data lakes, warehouses, and analytics tools

• Monitor, optimize, and troubleshoot streaming applications

• Apply real‑time processing to real‑world use cases such as fraud detection, monitoring, personalization, and alerting