Apache NiFi for Administrators Training Course

NiFi Fundamentals and Data Flow Concepts

• Distinguishing between data in motion and data at rest: key concepts and associated challenges
• Understanding NiFi architecture: cores, flow controller, provenance, and bulletin board
• Essential components: processors, connections, controllers, and provenance tracking

Big Data Context and System Integration

• The position of NiFi within Big Data ecosystems, including Hadoop, Kafka, and cloud storage
• Introduction to HDFS, MapReduce, and contemporary alternatives
• Practical applications: stream ingestion, log shipping, and event pipelines

Installation, Configuration, and Cluster Establishment

• Deploying NiFi in both single-node and cluster modes
• Cluster setup: defining node roles, utilizing ZooKeeper, and implementing load balancing
• Managing NiFi deployments using tools such as Ansible, Docker, or Helm

Creating and Administering Dataflows

• Techniques for routing, filtering, splitting, and merging data flows
• Configuring processors (e.g., InvokeHTTP, QueryRecord, PutDatabaseRecord)
• Managing schema handling, data enrichment, and transformation tasks
• Managing errors, retry relationships, and backpressure mechanisms

Integration Use Cases

• Establishing connections to databases, messaging systems, and REST APIs
• Streaming data to analytics platforms like Kafka, Elasticsearch, or cloud storage
• Integrating with monitoring and logging tools such as Splunk, Prometheus, and log pipelines

Monitoring, Recovery, and Provenance Management

• Leveraging the NiFi UI, metrics, and the provenance visualizer for oversight
• Engineering autonomous recovery mechanisms and graceful failure handling
• Managing backups, flow versioning, and change control

Performance Tuning and Optimization Strategies

• Adjusting JVM, heap, thread pools, and clustering parameters
• Refining flow design to eliminate performance bottlenecks
• Implementing resource isolation, flow prioritization, and throughput regulation

Best Practices and Governance

• Standards for flow documentation, naming conventions, and modular design
• Security measures: TLS, authentication, access control, and data encryption
• Maintaining change control, versioning, role-based access, and audit trails

Troubleshooting and Incident Response

• Addressing common issues such as deadlocks, memory leaks, and processor errors
• Conducting log analysis, error diagnostics, and root cause investigation
• Applying recovery strategies and executing flow rollbacks

Practical Laboratory: Implementing a Realistic Data Pipeline

• Constructing an end-to-end flow covering ingestion, transformation, and delivery
• Applying error handling, backpressure management, and scaling techniques
• Testing and tuning pipeline performance

Conclusion and Future Directions