Introduction

In the ever-evolving world of big data, understanding and working with data streams in real-time has become a critical skill for data professionals. This course, "Real-Time Data Analytics and Stream Processing," aims to equip participants with the knowledge and tools necessary to design and implement effective stream processing solutions. Through hands-on workshops and case studies, participants will learn state-of-the-art techniques and best practices to transform real-time data into actionable insights.

Objectives

• Understand the fundamentals of real-time data processing and analytics.
• Learn about various streaming platforms and tools.
• Develop the ability to process and analyze data in real-time.
• Gain hands-on experience with popular stream processing frameworks.
• Apply real-time analytics to solve business problems and improve decision-making.

Course Outlines

Day 1: Introduction to Real-Time Data Analytics

• Overview of real-time data processing and its significance
• Components of a real-time analytics ecosystem
• Introduction to data streaming concepts
• Exploring use cases and industry applications
• Setting up the learning environment and tools overview

Day 2: Streaming Data Platforms and Tools

• Introduction to popular streaming platforms: Apache Kafka, Apache Flink, and others
• Setting up and configuring streaming platforms
• Understanding message brokers and pub/sub systems
• Hands-on lab: Building a simple data pipeline
• Real-world examples of streaming architectures

Day 3: Stream Processing Techniques and Frameworks

• Understanding stream processing models: Processing guarantees and fault tolerance
• Overview of processing frameworks: Spark Streaming, Flink, and Storm
• Hands-on lab: Implementing stream processing with Apache Spark
• Event time and processing time considerations
• Case studies on stream processing solutions

Day 4: Real-Time Data Analytics Applications

• Introduction to real-time analytics applications
• Real-time alerting and monitoring systems
• Integrating machine learning models in real-time pipelines
• Hands-on lab: Using Flink for real-time analytics
• Best practices for maintaining low-latency systems

Day 5: Advanced Topics and Capstone Project

• Working with stateful streaming applications
• Exploring advanced topics: Windowing and stream joins
• Optimizing and scaling stream processing solutions
• Capstone project work: Designing a custom real-time solution
• Presentation of projects and course wrap-up