Apache NiFi for Administrators Training Course

NiFi and Data Flow Fundamentals

• Understanding data in motion versus data at rest: key concepts and associated challenges
• NiFi architecture: exploring cores, flow controllers, provenance, and bulletin boards
• Essential components: processors, connections, controllers, and provenance tracking

Big Data Context and Integration

• NiFi's role within Big Data ecosystems, including Hadoop, Kafka, and cloud storage
• An overview of HDFS, MapReduce, and contemporary alternatives
• Practical use cases: stream ingestion, log shipping, and event pipelines

Installation, Configuration & Cluster Setup

• Deploying NiFi on both single nodes and in cluster mode
• Configuring clusters: defining node roles, integrating Zookeeper, and managing load balancing
• Orchestrating NiFi deployments using tools like Ansible, Docker, or Helm

Dataflow Design and Management

• Techniques for routing, filtering, splitting, and merging dataflows
• Configuring processors such as InvokeHTTP, QueryRecord, and PutDatabaseRecord
• Managing schemas, enrichment, and transformation operations
• Implementing error handling, retry mechanisms, and backpressure controls

Integration Scenarios

• Connecting 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, or other pipelines

Monitoring, Recovery & Provenance

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

Performance Tuning & Optimization

• Tuning JVM settings, heap size, thread pools, and clustering parameters
• Refining flow designs to minimize bottlenecks
• Applying resource isolation, flow prioritization, and throughput control

Best Practices & Governance

• Establishing flow documentation, naming conventions, and modular design standards
• Enhancing security through TLS, authentication, access controls, and data encryption
• Enforcing change control, versioning, role-based access, and audit trails

Troubleshooting & Incident Response

• Addressing common issues such as deadlocks, memory leaks, and processor errors
• Performing log analysis, error diagnostics, and root cause investigations
• Developing recovery strategies and flow rollback procedures

Hands-on Lab: Realistic Data Pipeline Implementation

• Constructing an end-to-end flow covering ingestion, transformation, and delivery
• Implementing error handling, backpressure, and scaling capabilities
• Conducting performance tests and tuning the pipeline

Summary and Next Steps