Apache NiFi for Administrators Training Course

NiFi and Data Flow Fundamentals

• Data in motion versus data at rest: core concepts and associated challenges.
• NiFi architecture: cores, flow controller, provenance, and bulletin board.
• Essential components: processors, connections, controllers, and provenance.

Big Data Context and Integration

• The role of NiFi in Big Data ecosystems (Hadoop, Kafka, cloud storage).
• An overview of HDFS, MapReduce, and modern alternatives.
• Application scenarios: stream ingestion, log shipping, and event pipelines.

Installation, Configuration & Cluster Setup

• Installing NiFi in both single-node and cluster modes.
• Configuring clusters: node roles, Zookeeper, and load balancing.
• Orchestrating NiFi deployments using Ansible, Docker, or Helm.

Designing and Managing Dataflows

• Managing flow routing, filtering, splitting, and merging.
• Configuring processors (e.g., InvokeHTTP, QueryRecord, PutDatabaseRecord).
• Handling schemas, enrichment, and transformation operations.
• Managing error handling, retry relationships, and backpressure.

Integration Scenarios

• Connecting to databases, messaging systems, and REST APIs.
• Streaming data to analytics systems such as Kafka, Elasticsearch, or cloud storage.
• Integrating with Splunk, Prometheus, or other logging pipelines.

Monitoring, Recovery & Provenance

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

Performance Tuning & Optimization

• Tuning JVM, heap, thread pools, and clustering parameters.
• Optimizing flow design to minimize bottlenecks.
• Implementing resource isolation, flow prioritization, and throughput control.

Best Practices & Governance

• Establishing flow documentation, naming standards, and modular design.
• Security measures: TLS, authentication, access control, and data encryption.
• Governance: 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.
• Developing recovery strategies and flow rollback procedures.

Hands-on Lab: Realistic Data Pipeline Implementation

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

Summary and Next Steps