Apache NiFi for Administrators Training Course

NiFi and Data Flow Fundamentals

• Comparing data in motion versus data at rest: underlying concepts and associated challenges
• Understanding NiFi architecture: cores, flow controller, provenance, and bulletin board
• Core components: processors, connections, controllers, and provenance

Big Data Context and Integration

• The role of NiFi within Big Data ecosystems (including Hadoop, Kafka, and cloud storage)
• An overview of HDFS, MapReduce, and their modern alternatives
• Practical use cases: stream ingestion, log shipping, and event pipelines

Installation, Configuration & Cluster Setup

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

Designing and Managing Dataflows

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

Integration Scenarios

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

Monitoring, Recovery & Provenance

• Utilizing the NiFi UI, system metrics, and the provenance visualizer
• Designing strategies for autonomous recovery 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 design to minimize bottlenecks
• Applying resource isolation, flow prioritization, and throughput control

Best Practices & Governance

• Establishing flow documentation, naming conventions, and modular design standards
• Implementing 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 such as deadlocks, memory leaks, and processor errors
• Performing log analysis, error diagnostics, and root cause investigations
• Applying 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 mechanisms
• Conducting performance testing and pipeline tuning

Summary and Next Steps