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Data Engineering with Apache Kafka

Learn to create strong, scalable, and real-time data pipelines using Apache Kafka, the premier distributed event-streaming platform.

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Certificate :

After Completion

Start Date :

10-Jan-2025

Duration :

30 Days

Course fee :

$150

COURSE DESCRIPTION:

  1. Learn to create strong, scalable, and real-time data pipelines using Apache Kafka, the premier distributed event-streaming platform.
  2. This course covers essential Kafka architecture, stream processing, and data engineering techniques.
  3. Gain the skills to manage high-throughput, low-latency data workflows for contemporary applications.

CERTIFICATION:

  1. Obtain a Certified Data Engineer credential in Apache Kafka to demonstrate your skills in real-time data stream processing.

  2. Validate your expertise in creating data pipelines through certification.

  3. Enhance your professional profile with recognized credentials in data engineering.

LEARNING OUTCOMES:

By the conclusion of the course, participants will possess the skills to:

  1. Grasp the structure of Kafka, focusing on brokers, topics, partitions, and producers/consumers.

  2. Create and deploy scalable, resilient data pipelines. – Execute stream processing using Kafka Streams and Kafka Connect.

  3. Connect Kafka with databases, data lakes, and various tools within the data ecosystem.

  4. Oversee and resolve issues in Kafka clusters to maintain optimal performance and reliability.

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Course Curriculum

Introduction to Data Engineering and Apache Kafka
  1. Overview of Data Engineering
    • Key concepts: ETL, data pipelines, and data streaming.
    • Role of data engineering in modern analytics and AI.
  2. What is Apache Kafka?
    • History, architecture, and core components of Kafka.
    • Use cases: Real-time analytics, event-driven systems, and log aggregation.
Kafka Fundamentals
  1. Core Concepts
    • Topics, partitions, producers, consumers, and brokers.
    • Kafka clusters and fault tolerance.
  2. Messaging Models
    • Publish-subscribe vs. point-to-point messaging.
  3. Setting Up Kafka
    • Installation and configuration of Kafka and Zookeeper.
    • Managing Kafka clusters.
Working with Kafka APIs
  1. Producer API
    • Sending messages to Kafka topics.
    • Configuring delivery semantics (at least once, exactly once, at most once).
  2. Consumer API
    • Reading messages from Kafka topics.
    • Consumer groups and offset management.
  3. Streams API
    • Real-time data processing with Kafka Streams.
    • Filtering, transforming, and aggregating data.
  4. Connect API
    • Integrating Kafka with external systems (databases, file systems, etc.).
Data Pipeline Design with Kafka
  1. Building Real-Time Data Pipelines
    • Designing efficient and scalable pipelines.
    • Handling high-throughput and low-latency requirements.
  2. Integration with ETL Tools
    • Using Kafka with tools like Apache Nifi, Talend, or custom scripts.
  3. Data Transformation and Enrichment
    • Stream processing with Kafka Streams and ksqlDB.
Advanced Kafka Topics
  1. Kafka Security
    • Authentication and authorization with SASL and ACLs.
    • Encrypting data in transit with SSL/TLS.
  2. Monitoring and Performance Tuning
    • Metrics collection with JMX, Prometheus, and Grafana.
    • Optimizing Kafka performance: Partitioning, replication, and batching.
  3. Schema Management
    • Using Apache Avro and Schema Registry for data serialization.
  4. Kafka in the Cloud
    • Managed Kafka services (e.g., Confluent Cloud, AWS MSK, Azure Event Hubs).
Fault Tolerance and Reliability
  1. Ensuring Message Durability
    • Configuring replication and log retention policies.
  2. Error Handling and Retries
    • Dead-letter queues and retry mechanisms.
  3. High Availability
    • Configuring multi-broker clusters and leader election.
Real-World Applications of Kafka
  1. Use Cases
      • Building event-driven microservices.
      • Real-time analytics and dashboards.
      • Log aggregation and processing.
      • Fraud detection systems.
    • Integration with Big Data Ecosystems
      • Kafka with Hadoop, Spark, and Flink.
      • Data lakes and warehousing (e.g., Snowflake, Redshift).
Capstone Project
  1. End-to-End Data Pipeline with Kafka
    • Create a real-time data pipeline for a streaming application.
    • Use producers to ingest data, process streams using Kafka Streams or ksqlDB, and integrate with a database or analytics platform.
  2. Example: A real-time e-commerce dashboard showing live sales and user activity.

Training Features

Hands-On Practice

Interactive coding exercises, virtual labs, and real-world datasets.

Project-Based Learning

Build scalable pipelines and streaming applications.

Performance Optimization

Practical lessons in tuning and scaling Kafka for enterprise needs.

Industry-Relevant Tools

Kafka Streams, ksqlDB, Schema Registry, and integration with cloud platforms.

Career Support

Resume-building, interview preparation, and guidance for data engineering roles.

Certification

A professional certificate validating expertise in Kafka-based data engineering.

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