Apache NiFi for Administrators Training Course

NiFi Fundamentals and Data Flow Concepts

• Differentiating data in motion from data at rest: underlying concepts and challenges.
• NiFi architecture: core components, flow controller, provenance, and bulletin board.
• Essential elements: processors, connections, controllers, and provenance tracking.

Big Data Context and Integration

• NiFi's role within Big Data ecosystems, including Hadoop, Kafka, and cloud storage solutions.
• 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-node and cluster modes.
• Configuring clusters: defining node roles, integrating Zookeeper, and establishing 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 schema handling, data enrichment, and transformation operations.
• Implementing error handling, retry mechanisms, and backpressure controls.

Integration Scenarios

• Connecting NiFi 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, and standard logging pipelines.

Monitoring, Recovery & Provenance

• Utilizing the NiFi UI, performance metrics, and the provenance visualizer.
• Designing for autonomous recovery and graceful failure management.
• Executing backups, managing flow versions, and controlling changes.

Performance Tuning & Optimization

• Tuning JVM settings, heap memory, thread pools, and clustering parameters.
• Optimizing flow design to minimize bottlenecks.
• Implementing resource isolation, prioritizing flows, and controlling throughput.

Best Practices & Governance

• Establishing flow documentation, naming conventions, and modular design principles.
• Enhancing security through TLS, authentication, access control, and data encryption.
• Managing change control, versioning, role-based access, and maintaining audit trails.

Troubleshooting & Incident Response

• Addressing common issues such as deadlocks, memory leaks, and processor errors.
• Performing log analysis, error diagnostics, and root cause investigation.
• 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.
• Implementing error handling, backpressure mechanisms, and scaling strategies.
• Conducting performance tests and tuning the pipeline.

Summary and Next Steps