Introduction

In the era of real-time data processing, businesses need to ingest, analyze, and act on data streams instantly. Whether for financial transactions, IoT sensor data, social media feeds, or AI-driven applications, data streaming technologies have become essential in modern data architectures.

This 5-day hands-on training course provides an in-depth understanding of data streaming concepts, architectures, and best practices using leading technologies such as Apache Kafka, Apache Flink, Apache Pulsar, AWS Kinesis, and Google Dataflow. Participants will learn how to design, implement, and optimize real-time data streaming pipelines to drive business intelligence, machine learning, and automation.

Objectives

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

• Understand data streaming fundamentals and architectures.
• Design scalable, fault-tolerant, and high-throughput real-time data pipelines.
• Work with Apache Kafka, Apache Flink, Apache Pulsar, AWS Kinesis, and Google Dataflow.
• Integrate streaming data with cloud data lakes and data warehouses.
• Implement event-driven microservices using streaming technologies.
• Optimize performance, reliability, and cost efficiency in streaming pipelines.
• Use stream processing for AI/ML, IoT, and business intelligence.
• Explore future trends in real-time analytics and edge computing.

Who Should Attend?

This course is ideal for:

• Data Engineers building real-time data pipelines.
• Software Engineers & Developers working on event-driven architectures.
• Data Architects designing scalable streaming solutions.
• DevOps & Cloud Engineers integrating cloud-native streaming services.
• BI & AI/ML Professionals leveraging real-time data for analytics.
• IT Managers & CTOs leading digital transformation initiatives.

Day 1: Introduction to Data Streaming & Real-Time Architectures

• Understanding Data Streaming

  • Batch vs. real-time data processing
  • Key use cases: IoT, financial transactions, fraud detection, social media analytics
  • Core concepts: Event-driven architecture, pub/sub messaging, stream processing

• Modern Data Streaming Architectures

  • Lambda vs. Kappa architecture: Pros & cons
  • Streaming vs. micro-batch processing
  • Streaming integration with data lakes, warehouses, and AI/ML

• Overview of Streaming Technologies

  • Apache Kafka, Apache Flink, Apache Pulsar
  • AWS Kinesis, Google Dataflow, Azure Event Hubs
  • Comparing open-source vs. cloud-native streaming solutions

• Hands-on Lab:

  • Setting up Apache Kafka and publishing/consuming real-time events

Day 2: Apache Kafka & Event-Driven Streaming

• Deep Dive into Apache Kafka

  • Kafka architecture: Brokers, topics, partitions, producers, consumers
  • Kafka Streams API for real-time processing
  • Kafka Connect for integrating with databases, cloud storage, and BI tools

• Building Scalable Kafka Pipelines

  • Partitioning and replication strategies
  • Optimizing Kafka for performance and reliability
  • Security in Kafka: Authentication, authorization, and encryption

• Kafka in the Cloud

  • Deploying Kafka on AWS MSK, Azure Event Hubs, and Google Pub/Sub
  • Comparing managed Kafka services vs. self-hosted Kafka

• Hands-on Lab:

  • Configuring and optimizing a Kafka cluster for high throughput

Day 3: Stream Processing with Apache Flink & Apache Pulsar

• Introduction to Apache Flink for Stream Processing

  • Flink vs. Spark Streaming: Key differences
  • Event time vs. processing time in streaming analytics
  • Stateful stream processing and windowing operations

• Real-time Data Pipelines with Apache Flink

  • Writing Flink jobs for event-driven processing
  • Using Flink SQL for real-time analytics
  • Integrating Flink with Kafka and cloud data lakes

• Apache Pulsar for Advanced Streaming

  • Pulsar vs. Kafka: When to use each
  • Multi-tenancy and geo-replication in Pulsar
  • Pulsar Functions for lightweight serverless processing

• Hands-on Lab:

  • Building a real-time analytics dashboard using Flink and Kafka

Day 4: Cloud Streaming with AWS Kinesis, Google Dataflow & Azure Stream Analytics

• AWS Kinesis for Real-time Streaming

  • Kinesis Data Streams vs. Kinesis Firehose
  • Integrating Kinesis with Lambda, S3, and Redshift
  • Scaling Kinesis for large-scale workloads

• Google Dataflow & Apache Beam

  • Understanding Apache Beam’s unified batch & streaming model
  • Writing real-time processing jobs in Google Dataflow
  • Optimizing Dataflow pipelines for performance and cost

• Azure Stream Analytics for Real-time Insights

  • Writing SQL-based stream queries
  • Integrating with Power BI and Azure Synapse
  • Event Hubs vs. Kafka on Azure: Key differences

• Hands-on Lab:

  • Deploying a real-time stream processing pipeline using Google Dataflow

Day 5: Future Trends, Best Practices & Final Project

• Optimizing Performance & Reliability in Streaming Pipelines

  • Handling exactly-once semantics in streaming
  • Managing late-arriving data and event reordering
  • Stream deduplication and stateful processing

• Streaming for AI/ML & IoT

  • Feature engineering in streaming ML workflows
  • Integrating streaming data with TensorFlow, PyTorch, and AutoML
  • Edge AI and real-time inferencing

• Future of Data Streaming

  • The rise of serverless stream processing
  • AI-driven automation in event-driven systems
  • Streaming data lakes & Lakehouse architectures (Delta Lake, Iceberg, Hudi)

• Final Project: End-to-End Streaming Solution

  • Build a real-world streaming data pipeline using Kafka, Flink, and a cloud service
  • Apply best practices in event-driven processing, security, and cost efficiency

• Course Wrap-Up & Certification

  • Review of key concepts
  • Q&A and discussions on real-world use cases
  • Certification of completion