Training Description
Specialized training dedicated to using Apache Kafka Connect for integrating diverse data sources. The program covers design, implementation, and management of connectors to external systems, with an emphasis on scalability and data flow reliability. Hands-on workshops allow participants to gain experience in configuring and monitoring various types of connectors and resolving common integration issues. The training uses real-world scenarios to present best practices in data integration.
Participant Profile
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Data integration engineers
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Data pipeline system developers
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ETL specialists
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Integration solution architects
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Distributed systems developers
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Data engineers
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System integration specialists
Agenda
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Kafka Connect architecture
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Framework and components
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Connector types
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Distributed mode
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Connector management
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Connector implementation
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Source connector configuration
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Sink connector configuration
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Data transformations
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Validation and monitoring
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Advanced scenarios
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Single Message Transforms
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Error handling and dead letter queues
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Schema management
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Performance optimization
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Operations and maintenance
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Connector health monitoring
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Troubleshooting
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Scaling and load balancing
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Backup and recovery strategies
Benefits
Practical knowledge of Kafka Connect architecture and capabilities. Ability to design efficient data source integrations. Experience in implementing and configuring various types of connectors. Capability to effectively monitor and manage data flow. Familiarity with integration troubleshooting techniques. Understanding of patterns ensuring integration reliability.
Required Participant Preparation
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Basic knowledge of Apache Kafka
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Experience in system integration
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Familiarity with data exchange formats
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Fundamentals of stream data processing
Topics
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Connector Architecture
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Data Integration Patterns
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Schema Management
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Error Handling
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Performance Optimization
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Monitoring Techniques
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Scalability Patterns
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Data Transformation
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Security Configuration
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Backup Strategies
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Resource Management
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Deployment Models
Read Also
- ‘Edge AI: data processing closer to the source - applications and benefits of artificial intelligence on edge devices’
- Identity and Access Management - How to Effectively Manage User Permissions and Control Access to Critical Systems and Data
- ‘Edge Computing: the future of computing closer to the source’
Read also
- Edge Computing: the future of computing closer to the source
- Edge AI: data processing closer to the source - applications and benefits of artificial intelligence on edge devices
- Identity and Access Management - How to Effectively Manage User Permissions and Control Access to Critical Systems and Data
Develop your skills
Want to deepen your knowledge in this area? Check out our training led by experienced EITT instructors.
➡️ Apache Kafka Connect - integration of data sources — EITT training
Frequently Asked Questions
What is the difference between a source connector and a sink connector in Kafka Connect?
A source connector reads data from an external system (such as a database or file system) and writes it into Kafka topics, while a sink connector reads data from Kafka topics and delivers it to an external destination. Together they form a complete data pipeline that enables real-time integration between diverse systems.
Do I need to write custom code to use Kafka Connect?
In most cases, no custom coding is required because Kafka Connect offers a rich ecosystem of pre-built connectors for popular databases, cloud services, and file formats. Configuration is typically done through JSON or properties files, though custom Single Message Transforms or entirely new connectors can be developed in Java when needed.
How does Kafka Connect handle failures and ensure data reliability?
Kafka Connect provides built-in fault tolerance through offset tracking, automatic task restart, and dead letter queues for messages that cannot be processed. In distributed mode, failed tasks are automatically reassigned to healthy workers, ensuring continuous data flow with minimal manual intervention.
What prerequisites should participants have before attending this training?
Participants should have basic knowledge of Apache Kafka concepts (topics, partitions, consumer groups), some experience with system integration, and familiarity with common data exchange formats like JSON and Avro. Prior hands-on experience with Kafka producers and consumers will help participants get the most from the workshop exercises.