Apache NiFi for Administrators Training Course

NiFi Fundamentals and Data Flow Mechanics

• Contrasting data in motion versus data at rest: underlying concepts and associated challenges
• NiFi architecture: examining cores, the flow controller, provenance, and the bulletin board
• Essential components: processors, connections, controllers, and provenance tracking

Contextualizing NiFi in Big Data and Integration

• The role of NiFi within Big Data ecosystems (including Hadoop, Kafka, and cloud storage)
• An overview of HDFS, MapReduce, and contemporary alternatives
• Practical applications: stream ingestion, log transmission, and event pipelines

Installation, Configuration, & Cluster Deployment

• Installing NiFi on single-node setups and in cluster mode
• Configuring clusters: defining node roles, integrating Zookeeper, and balancing load
• Orchestrating NiFi deployments using Ansible, Docker, or Helm

Designing and Managing Dataflows

• Routing, filtering, splitting, and merging data flows
• Configuring processors (such as InvokeHTTP, QueryRecord, PutDatabaseRecord, etc.)
• Managing schemas, enrichment tasks, and transformation operations
• Implementing error handling, retry relationships, and backpressure management

Integration Scenarios

• Establishing connections to databases, messaging systems, and REST APIs
• Streaming data to analytics platforms: Kafka, Elasticsearch, or cloud storage services
• Integrating with Splunk, Prometheus, or logging pipelines

Monitoring, Recovery, & Provenance Management

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

Performance Tuning & Optimization

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

Best Practices & Governance

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

Troubleshooting & Incident Response

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

Hands-on Lab: Realistic Data Pipeline Implementation

• Constructing an end-to-end flow: covering ingestion, transformation, and delivery
• Applying error handling, backpressure, and scaling techniques
• Performance testing and tuning the pipeline

Summary and Next Steps