Apache NiFi for Administrators Training Course

NiFi Fundamentals and Data Flow Principles

• Distinguishing between data in motion and data at rest: underlying concepts and associated challenges
• NiFi architecture overview: core components, flow controller, provenance tracking, and bulletin board
• Essential elements: processors, connections, controllers, and provenance data

Context and Integration in Big Data Environments

• The position of NiFi within Big Data ecosystems, including Hadoop, Kafka, and cloud storage solutions
• Introduction to HDFS, MapReduce, and contemporary alternatives
• Application scenarios: stream ingestion, log forwarding, and event pipeline management

Setup, Configuration & Cluster Management

• Deploying NiFi on single nodes and in cluster modes
• Configuring clusters: defining node roles, utilizing Zookeeper, and implementing load balancing
• Orchestrating NiFi deployments via Ansible, Docker, or Helm

Constructing and Overseeing Dataflows

• Techniques for routing, filtering, splitting, and merging data flows
• Configuring specific processors (e.g., InvokeHTTP, QueryRecord, PutDatabaseRecord)
• Managing schemas, data enrichment, and transformation workflows
• Strategies for error handling, establishing retry relationships, and managing backpressure

Practical Integration Scenarios

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

Monitoring, Recovery & Provenance Management

• Leveraging the NiFi UI, system metrics, and the provenance visualizer
• Architecting autonomous recovery mechanisms and graceful failure management
• Executing backups, managing flow versions, and overseeing changes

Performance Tuning & Optimization

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

Best Practices & Governance Frameworks

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

Troubleshooting & Incident Management

• Addressing common issues: deadlocks, memory leaks, and processor errors
• Analyzing logs, diagnosing errors, and investigating root causes
• Deploying recovery strategies and executing flow rollbacks

Practical Lab: Implementing a Realistic Data Pipeline

• Constructing a complete flow from ingestion through transformation to final delivery
• Applying error handling, backpressure management, and scaling techniques
• Conducting performance tests and tuning the pipeline

Recap and Future Directions