Big Data

Kafka

Kafka training: Apache Kafka is an open-source, distributed event streaming platform designed to handle high-throughput, fault-tolerant data…

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Kafka training:

Apache Kafka is an open-source, distributed event streaming platform designed to handle high-throughput, fault-tolerant data pipelines. It is used by top companies worldwide to build real-time applications, microservices architectures, event-driven systems, and big data processing frameworks. As businesses generate huge amounts of data, Kafka enables efficient ingestion, storage, and analysis of real-time event streams, ensuring low latency and high fault tolerance.

This comprehensive Kafka Training course is designed to take you from Kafka fundamentals to advanced real-time streaming architectures. Whether you are a software developer, data engineer, DevOps professional, or system architect, this Kafka Online Course will help you develop the essential skills needed to deploy, configure, and integrate Kafka with modern data platforms.

By the end of this Kafka Online Training, you’ll have a deep understanding of Kafka’s architecture, internal components, and real-world applications. You’ll gain hands-on experience in setting up Kafka clusters, managing producers and consumers, stream processing, and securing Kafka deployments. Additionally, you’ll learn how Kafka integrates with popular big data technologies such as Apache Spark, Flink, and Hadoop, as well as cloud and containerization platforms like Kubernetes, Databricks, AWS, Azure, and Google Cloud.

Whether you are looking to optimize data pipelines, scale enterprise messaging systems, or implement real-time analytics, this Kafka Training course provides the theoretical knowledge and practical expertise needed to become a Kafka expert.

What Will You Learn?

Module 1: Introduction to Kafka: What is Apache Kafka?, Kafka Use Cases & Industry Applications, Understanding Kafka’s Pub-Sub Model, Overview of Kafka Ecosystem (ZooKeeper, Kafka Brokers, Producers, Consumers)
Module 2: Kafka Architecture Deep Dive: Kafka Topics, Partitions & Offsets, Kafka Producers & Consumers, Brokers, Clusters, and Replication, ZooKeeper’s Role in Kafka
Module 3: Installing & Setting Up Kafka: Installing Kafka on Local and Cloud, Kafka Cluster Setup (Multi-Node), Configuring Brokers and Topics, Best Practices for Cluster Management
Module 4: Working with Kafka Producers & Consumers: Writing Kafka Producers in Java/Python, Implementing Kafka Consumers, Kafka Consumer Groups & Offsets Management, Understanding Kafka Message Delivery Semantics (At-Most-Once, At-Least-Once, Exactly-Once)
Module 5: Kafka Streams & Real-Time Processing: Introduction to Kafka Streams API, Building Real-Time Data Pipelines, Windowing, Aggregations, and State Stores; Stream Processing vs. Batch Processing
Module 6: Kafka Connect & Data Integration: Understanding Kafka Connect, Connecting Kafka to Databases, Elasticsearch, Hadoop, etc., Using Source & Sink Connectors, Schema Management with Confluent Schema Registry
Module 7: Kafka Security & Performance Tuning: Authentication & Authorization (SSL, SASL, ACLs), Kafka Performance Optimization Techniques, Monitoring & Logging Kafka Clusters, Handling Fault Tolerance & Failover Scenarios
Module 8: Kafka with Big Data & Cloud Integration: Integrating Kafka with Spark, Flink, and Databricks; Kafka on AWS, Azure, and Google Cloud; Deploying Kafka on Kubernetes & Docker
Module 9: Hands-On Projects: Building a Real-Time Streaming Pipeline, Processing IoT Sensor Data with Kafka & Spark, Implementing an Event-Driven Microservices Architecture

Course Curriculum

Module 1: Introduction to Data Engineering and Apache Kafka

1.1 Fundamentals of Data Engineering
:: What is Data Engineering?
:: The Role of a Data Engineer in Modern Organizations
:: Overview of Data Pipelines: Batch vs. Real-time Processing
1.2 Introduction to Apache Kafka
:: Kafka’s Place in the Data Engineering Ecosystem
:: Kafka as a Distributed Streaming Platform
:: Kafka Use Cases: Real-time Analytics, Log Aggregation, Event Sourcing, etc.
:: Kafka Ecosystem Overview: Kafka Streams, Kafka Connect, Schema Registry
1.3 Setting Up Your Environment
:: System Requirements and Pre-requisites
:: Installing Kafka Locally (Linux, Windows, MacOS)
:: Setting Up Kafka in Docker for Development
:: Overview of Kafka Managed Services (Confluent Cloud, AWS MSK)

Module 2: Kafka Core Concepts and Architecture

2.1 Kafka Architecture Deep Dive
:: Kafka Brokers, Topics, Partitions, and Offsets
:: Producers, Consumers, and Consumer Groups
:: The Role of ZooKeeper in Kafka
:: Data Replication and Fault Tolerance in Kafka
2.2 Understanding Kafka Topics and Partitions
:: Creating and Configuring Kafka Topics
:: Partitioning Strategies: Keyed vs. Round-Robin
:: Data Ordering and Guarantees
2.3 Working with Kafka Producers
:: Introduction to the Producer API
:: Sending Messages: Synchronous vs. Asynchronous Sends
:: Producer Configuration: Compression, Batching, Acknowledgments
:: Error Handling and Retry Strategies
2.4 Working with Kafka Consumers
:: Introduction to the Consumer API
:: Consuming Messages: Polling vs. Streaming
:: Consumer Groups, Offset Management, and Rebalancing
:: Handling Consumer Failures and Retries

Module 3: Real-time Data Ingestion with Kafka

3.1 Building Data Pipelines with Kafka
:: End-to-End Pipeline Overview: Ingestion, Processing, and Storage
:: Designing Data Pipelines for Scalability and Reliability
:: Real-time vs. Batch Processing in Kafka Pipelines
3.2 Kafka Connect for Data Ingestion
:: Introduction to Kafka Connect
:: Source and Sink Connectors: Connecting Kafka to Databases, File Systems, and More
:: Configuring and Managing Connectors
:: Building Custom Connectors for Specialized Data Sources
3.3 Data Serialization and Deserialization
:: Working with Avro, JSON, and Protobuf
:: Integrating with Kafka Schema Registry
:: Ensuring Data Consistency and Schema Evolution
3.4 Real-time Scenario: Ingesting Logs and Metrics
:: Setting Up a Log Aggregation Pipeline
:: Ingesting Application Logs into Kafka
:: Real-time Monitoring with Kafka and Grafana

Module 4: Real-time Data Processing with Kafka Streams

4.1 Introduction to Kafka Streams
:: What is Kafka Streams?
:: Stream Processing vs. Batch Processing
:: Kafka Streams API Overview
4.2 Building Stream Processing Applications
:: Stateless Transformations: Map, Filter, FlatMap
:: Stateful Transformations: Aggregations, Joins, and Windowing
:: Using KTables and GlobalKTables
4.3 Advanced Stream Processing
:: Windowed Joins and Aggregations
:: Handling Late-Arriving Data
:: Fault Tolerance and State Management in Kafka Streams
4.4 Real-time Scenario: Real-time Analytics Dashboard
:: Building a Real-time Analytics Dashboard
:: Processing User Activity Streams
:: Aggregating and Visualizing Data in Real-time

Module 5: Data Integration and ETL with Kafka

5.1 Real-time ETL with Kafka
:: Introduction to ETL Processes in Kafka
:: Extracting, Transforming, and Loading Data in Real-time
:: Handling Data Transformations on the Fly
5.2 Integrating Kafka with Databases
:: Streaming Data to/from Relational Databases (e.g., MySQL, PostgreSQL)
:: Change Data Capture (CDC) with Kafka Connect (Debezium)
:: NoSQL Integration (e.g., MongoDB, Cassandra)
5.3 Kafka with Big Data Technologies
:: Integrating Kafka with Apache Hadoop and HDFS
:: Using Kafka with Apache Spark for Streaming Analytics
:: Building Lambda and Kappa Architectures with Kafka
5.4 Real-time Scenario: Building a Data Lake
:: Ingesting Data from Multiple Sources into Kafka
:: Real-time ETL to Hadoop Data Lake
:: Querying Data Lake in Real-time with Presto or Hive

Module 6: Kafka Operations and Performance Tuning

6.1 Kafka Cluster Management
:: Managing Kafka Brokers and Clusters
:: Topic Management and Partition Rebalancing
:: Kafka Upgrades and Downtime Minimization
6.2 Performance Tuning for Kafka
:: Optimizing Kafka Producers and Consumers
:: Tuning Kafka for High Throughput and Low Latency
:: Disk, Network, and Memory Optimization
6.3 Monitoring Kafka Clusters
:: Key Metrics to Monitor: Consumer Lag, Broker Health, Topic Metrics
:: Using Prometheus and Grafana for Kafka Monitoring
:: Troubleshooting Common Kafka Issues
6.4 Real-time Scenario: Scaling Kafka for High Throughput
:: Scaling Kafka for a High-Traffic Website
:: Optimizing Data Ingestion and Processing
:: Monitoring and Debugging Performance Issues

Module 7: Kafka Security and Compliance

7.1 Kafka Security Essentials
:: Securing Kafka Brokers with SSL
:: Implementing Authentication with SASL (PLAIN, SCRAM, OAuth)
:: Configuring Kafka ACLs for Access Control
7.2 Data Encryption and Privacy
:: Encrypting Data at Rest and In Transit
:: Compliance Considerations: GDPR, HIPAA
:: Auditing Kafka Activity for Security Compliance
7.3 Real-time Scenario: Securing a Kafka Cluster
:: Implementing End-to-End Encryption in a Kafka Pipeline
:: Configuring Access Control for Multi-Tenant Kafka Clusters
:: Ensuring Compliance in Financial or Healthcare Data Pipelines

Module 8: Advanced Kafka Engineering

8.1 Multi-Cluster and Geo-Replication
:: Kafka Across Multiple Data Centers
:: Using MirrorMaker for Cross-Cluster Replication
:: Designing Disaster Recovery Strategies with Kafka
8.2 Kafka in the Cloud
:: Deploying Kafka on AWS, Azure, and GCP
:: Managed Kafka Services: AWS MSK, Confluent Cloud
:: Best Practices for Cloud-Based Kafka Deployments
8.3 Real-time Scenario: Multi-Region Data Streaming
:: Building a Globally Distributed Data Pipeline
:: Implementing Cross-Region Replication with MirrorMaker
:: Ensuring Data Consistency and Low Latency Across Regions

Module 9: Final Project and Certification

9.1 Project: Building a Real-Time Data Pipeline
:: Designing and Implementing a Complete Real-time Data Pipeline
:: Ingesting, Processing, and Storing Data Using Kafka
:: Integrating with External Systems (e.g., Databases, Analytics Tools)
9.2 Final Assessment
* Multiple-Choice Exam Covering Key Kafka Concepts
* Practical Assignment: Debugging and Optimizing a Kafka Pipeline
* Certification of Completion

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Kafka

Kafka training:

What Will You Learn?

Course Curriculum

Module 1: Introduction to Data Engineering and Apache Kafka

1.1 Fundamentals of Data Engineering

:: What is Data Engineering?

:: The Role of a Data Engineer in Modern Organizations

:: Overview of Data Pipelines: Batch vs. Real-time Processing

1.2 Introduction to Apache Kafka

:: Kafka’s Place in the Data Engineering Ecosystem

:: Kafka as a Distributed Streaming Platform

:: Kafka Use Cases: Real-time Analytics, Log Aggregation, Event Sourcing, etc.

:: Kafka Ecosystem Overview: Kafka Streams, Kafka Connect, Schema Registry

1.3 Setting Up Your Environment

:: System Requirements and Pre-requisites

:: Installing Kafka Locally (Linux, Windows, MacOS)

:: Setting Up Kafka in Docker for Development

:: Overview of Kafka Managed Services (Confluent Cloud, AWS MSK)

Module 2: Kafka Core Concepts and Architecture

2.1 Kafka Architecture Deep Dive

:: Kafka Brokers, Topics, Partitions, and Offsets

:: Producers, Consumers, and Consumer Groups

:: The Role of ZooKeeper in Kafka

:: Data Replication and Fault Tolerance in Kafka

2.2 Understanding Kafka Topics and Partitions

:: Creating and Configuring Kafka Topics

:: Partitioning Strategies: Keyed vs. Round-Robin

:: Data Ordering and Guarantees

2.3 Working with Kafka Producers

:: Introduction to the Producer API

:: Sending Messages: Synchronous vs. Asynchronous Sends

:: Producer Configuration: Compression, Batching, Acknowledgments

:: Error Handling and Retry Strategies

2.4 Working with Kafka Consumers

:: Introduction to the Consumer API

:: Consuming Messages: Polling vs. Streaming

:: Consumer Groups, Offset Management, and Rebalancing

:: Handling Consumer Failures and Retries

Module 3: Real-time Data Ingestion with Kafka

3.1 Building Data Pipelines with Kafka

:: End-to-End Pipeline Overview: Ingestion, Processing, and Storage

:: Designing Data Pipelines for Scalability and Reliability

:: Real-time vs. Batch Processing in Kafka Pipelines

3.2 Kafka Connect for Data Ingestion

:: Introduction to Kafka Connect

:: Source and Sink Connectors: Connecting Kafka to Databases, File Systems, and More

:: Configuring and Managing Connectors

:: Building Custom Connectors for Specialized Data Sources

3.3 Data Serialization and Deserialization

:: Working with Avro, JSON, and Protobuf

:: Integrating with Kafka Schema Registry

:: Ensuring Data Consistency and Schema Evolution

3.4 Real-time Scenario: Ingesting Logs and Metrics

:: Setting Up a Log Aggregation Pipeline

:: Ingesting Application Logs into Kafka

:: Real-time Monitoring with Kafka and Grafana

Module 4: Real-time Data Processing with Kafka Streams

4.1 Introduction to Kafka Streams

:: What is Kafka Streams?

:: Stream Processing vs. Batch Processing

:: Kafka Streams API Overview

4.2 Building Stream Processing Applications

:: Stateless Transformations: Map, Filter, FlatMap

:: Stateful Transformations: Aggregations, Joins, and Windowing

:: Using KTables and GlobalKTables

4.3 Advanced Stream Processing

:: Windowed Joins and Aggregations

:: Handling Late-Arriving Data

:: Fault Tolerance and State Management in Kafka Streams

4.4 Real-time Scenario: Real-time Analytics Dashboard

:: Building a Real-time Analytics Dashboard

:: Processing User Activity Streams

:: Aggregating and Visualizing Data in Real-time

Module 5: Data Integration and ETL with Kafka

5.1 Real-time ETL with Kafka

:: Introduction to ETL Processes in Kafka

:: Extracting, Transforming, and Loading Data in Real-time

:: Handling Data Transformations on the Fly

5.2 Integrating Kafka with Databases