• phone icon +44 7459 302492 email message icon support@uplatz.com
  • Register

BUY THIS COURSE (GBP 12 GBP 29)
4.7 (2 reviews)
( 10 Students )
 

Streaming Data Pipelines

Master real-time data streaming, event processing, and low-latency analytics using modern streaming architectures and production-grade pipelines.
( add to cart )
ALL COURSES Course URL
Save 59% Offer ends on 31-Dec-2026
Course Duration: 10 Hours
  Price Match Guarantee   Full Lifetime Access     Access on any Device   Technical Support    Secure Checkout   Course Completion Certificate
Bestseller
Highly Rated
Great Value
Coming soon (2026)

Students also bought -

Completed the course? Request here for Certificate. ALL COURSES

In today’s digital economy, data is no longer generated only in batches at scheduled intervals. Modern applications produce data continuously — from user interactions, IoT sensors, financial transactions, logs, events, and machine-to-machine communication. To extract value from this constant flow of information, organizations are rapidly shifting from traditional batch-based data systems to streaming data pipelines that process data in real time.
 
Streaming data pipelines enable businesses to react instantly to events as they happen. Whether it is detecting fraud in milliseconds, monitoring system health, personalizing user experiences, or powering real-time dashboards, streaming architectures form the backbone of modern, data-driven systems. Technologies such as Apache Kafka, Apache Flink, Spark Structured Streaming, cloud-native streaming services, and event-driven microservices have become essential tools for building scalable and resilient real-time platforms.
 
The Streaming Data Pipelines course by Uplatz provides a comprehensive, practical understanding of how to design, build, operate, and scale real-time data pipelines in production environments. This course goes beyond theory to explain how streaming systems work internally, how they differ from batch pipelines, and how to choose the right architecture for different business needs. Learners will gain hands-on exposure to core streaming concepts such as event streams, topics, partitions, offsets, windowing, stateful processing, and exactly-once semantics.
 
The course begins by explaining why batch processing alone is no longer sufficient for modern use cases. You will explore the limitations of traditional ETL pipelines and understand how streaming architectures address latency, scalability, and responsiveness challenges. The course then introduces the fundamental building blocks of streaming systems — producers, brokers, consumers, stream processors, and sinks — and explains how data flows through these components in real time.
 
A major focus of this course is event-driven architecture. You will learn how systems communicate through events rather than direct calls, enabling loose coupling, scalability, and fault tolerance. The course explains how event streams are designed, how schemas are managed, and how data contracts ensure compatibility between producers and consumers. You will also explore schema registries, serialization formats (Avro, Protobuf, JSON), and versioning strategies used in large-scale systems.
 
The course provides a deep dive into stream processing frameworks. You will learn how modern engines process unbounded data streams, handle out-of-order events, manage state, and compute results over time windows. Concepts such as tumbling windows, sliding windows, session windows, watermarks, and late data handling are explained clearly with real-world examples. These concepts are critical for building reliable analytics pipelines that work correctly under real production conditions.
 
Hands-on implementation is a key part of this course. Learners will understand how to:

  • Ingest data into streaming platforms

  • Process events in real time

  • Enrich, filter, and aggregate streams

  • Join streams with reference data

  • Write processed data to databases, data lakes, and dashboards

  • Monitor pipeline health and performance

The course also explores streaming data pipelines in the cloud. You will learn how managed services simplify deployment and scaling while preserving core streaming principles. Topics include autoscaling, fault tolerance, checkpointing, and disaster recovery. You will understand how streaming pipelines integrate with data warehouses, machine learning systems, and microservices.
 
Another important theme covered in this course is streaming for machine learning and AI. Real-time pipelines are increasingly used to feed feature stores, update models, detect anomalies, and trigger automated actions. The course explains how streaming data supports online learning, near-real-time inference, and event-driven AI systems.
 
The course also addresses operational challenges such as backpressure, message retention, throughput tuning, data quality, replayability, and observability. You will learn how to design pipelines that remain reliable under high load, recover from failures gracefully, and provide visibility into system behavior.
 
By the end of this course, learners will have a strong conceptual and practical understanding of streaming data pipelines and will be able to design systems that process data continuously, reliably, and at scale.
🔍 What Are Streaming Data Pipelines?
 
Streaming data pipelines are systems that ingest, process, and deliver data continuously as events occur, rather than processing data in fixed batches.
 
Key characteristics include:

  • Real-time or near-real-time processing

  • Event-driven communication

  • Low latency

  • Continuous data flow

  • Fault tolerance and scalability

Streaming pipelines are built using message brokers, stream processors, and real-time storage systems.
⚙️ How Streaming Data Pipelines Work
 
1. Data Producers
 
Applications, services, devices, or systems that generate events.
 
2. Message Brokers
 
Durable systems that store and distribute events (topics, partitions, offsets).
 
3. Stream Processing Engines
 
Systems that transform, aggregate, and analyze data in motion.
 
4. Stateful Processing
 
Maintains context and state across events.
 
5. Sinks
 
Destinations such as databases, dashboards, data lakes, or alerting systems.
 
6. Fault Tolerance & Recovery
 
Checkpointing, replay, and exactly-once guarantees.
🏭 Where Streaming Pipelines Are Used in Industry
 
1. Finance & Payments
 
Fraud detection, transaction monitoring, real-time risk scoring.
 
2. E-commerce & Retail
 
Personalized recommendations, clickstream analysis.
 
3. Telecommunications
 
Network monitoring, anomaly detection.
 
4. IoT & Smart Systems
 
Sensor data processing, predictive maintenance.
 
5. Cloud & DevOps
 
Log aggregation, system monitoring, alerting.
 
6. Media & Entertainment
 
Live analytics, user engagement tracking.
🌟 Benefits of Learning Streaming Data Pipelines

  • Ability to design real-time systems

  • Strong understanding of event-driven architecture

  • Skills in scalable, fault-tolerant pipelines

  • High demand across data engineering roles

  • Foundation for real-time AI and analytics systems

📘 What You’ll Learn in This Course
 
You will explore:

  • Streaming vs batch processing

  • Event-driven architecture

  • Message brokers and stream processing concepts

  • Windowing and stateful processing

  • Exactly-once semantics

  • Real-time analytics pipelines

  • Monitoring and observability

  • Streaming data for ML and AI

🧠 How to Use This Course Effectively

  • Start with streaming fundamentals

  • Practice designing simple pipelines

  • Understand failure and recovery scenarios

  • Apply concepts to real-world use cases

  • Complete the capstone project

👩‍💻 Who Should Take This Course

  • Data Engineers

  • Backend Engineers

  • Cloud Engineers

  • ML Engineers

  • DevOps Professionals

  • Students entering data engineering

🚀 Final Takeaway
 
Streaming data pipelines power the real-time digital world. By mastering streaming architectures, you gain the ability to build systems that react instantly to events, scale seamlessly, and deliver continuous insights — a critical skill set for modern data platforms.

Course Objectives Back to Top

By the end of this course, learners will:

  • Understand streaming architecture fundamentals

  • Design real-time data pipelines

  • Apply event-driven design principles

  • Handle state, windows, and late data

  • Build scalable and fault-tolerant systems

  • Integrate streaming with analytics and AI workflows

Course Syllabus Back to Top

Course Syllabus

Module 1: Introduction to Streaming Data

  • Batch vs streaming

  • Use cases

Module 2: Event-Driven Architecture

  • Producers and consumers

  • Topics and partitions

Module 3: Streaming Platforms

  • Brokers and message queues

Module 4: Stream Processing Concepts

  • Windows, state, watermarks

Module 5: Fault Tolerance & Guarantees

  • At-least-once vs exactly-once

Module 6: Real-Time Analytics

  • Aggregations and joins

Module 7: Streaming in the Cloud

  • Managed streaming services

Module 8: Streaming for ML & AI

  • Feature pipelines and inference

Module 9: Monitoring & Operations

  • Observability and tuning

Module 10: Capstone Project

  • Build an end-to-end streaming pipeline

Certification Back to Top

Learners receive a Uplatz Certificate in Streaming Data Pipelines, validating expertise in real-time data processing and event-driven systems.

Career & Jobs Back to Top

This course prepares learners for roles such as:

  • Data Engineer

  • Streaming Engineer

  • Real-Time Analytics Engineer

  • Backend Engineer

  • ML Infrastructure Engineer

  • Cloud Data Engineer

Interview Questions Back to Top

1. What is a streaming data pipeline?

A system that processes data continuously in real time as events occur.

2. How is streaming different from batch processing?

Streaming processes data continuously; batch processes data in fixed intervals.

3. What is an event-driven architecture?

An architecture where systems communicate through events rather than direct calls.

4. What is a message broker?

A system that stores and distributes event streams to consumers.

5. What are windows in streaming?

Time-based groupings of events for aggregation.

6. What is stateful stream processing?

Processing that maintains context across multiple events.

7. What is exactly-once processing?

Guaranteeing each event is processed once even during failures.

8. Why is backpressure important?

It prevents slow consumers from overwhelming the system.

9. Where are streaming pipelines used?

Fraud detection, IoT, monitoring, personalization, and analytics.

10. What skills are needed for streaming systems?

Data engineering, distributed systems, and system design skills.

Course Quiz Back to Top
Start Quiz
Back to Top



BUY THIS COURSE (GBP 12 GBP 29)