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 Duration 21 hours

Course Outline

Foundations of NiFi and Data Flow

  • Concepts and challenges of data in motion versus data at rest
  • NiFi architecture: cores, flow controller, provenance, and bulletin
  • Core components: processors, connections, controllers, and provenance

Big Data Context and Integration

  • NiFi's role within Big Data ecosystems (Hadoop, Kafka, cloud storage)
  • Overview of HDFS, MapReduce, and contemporary alternatives
  • Application scenarios: stream ingestion, log shipping, and event pipelines

Installation, Configuration & Cluster Setup

  • Deploying NiFi in both single-node and cluster modes
  • Cluster settings: node roles, Zookeeper, and load balancing
  • Orchestrating deployments using Ansible, Docker, or Helm

Designing and Managing Dataflows

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

Integration Scenarios

  • Establishing connections to databases, messaging systems, and REST APIs
  • Streaming data to analytics platforms: Kafka, Elasticsearch, or cloud storage
  • Integration with Splunk, Prometheus, or logging pipelines

Monitoring, Recovery & Provenance

  • Leveraging the NiFi UI, metrics, and provenance visualizer
  • Designing autonomous recovery mechanisms and graceful failure handling
  • Strategies for backup, flow versioning, and change management

Performance Tuning & Optimization

  • Adjusting JVM, heap, thread pools, and clustering parameters
  • Refining flow design to minimize bottlenecks
  • Managing resource isolation, flow prioritization, and throughput control

Best Practices & Governance

  • Standards for flow documentation, naming, and modular design
  • Security measures: TLS, authentication, access control, and data encryption
  • Governance through 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
  • Applying recovery strategies and flow rollbacks

Hands-on Lab: Implementing a Realistic Data Pipeline

  • Constructing an end-to-end flow covering ingestion, transformation, and delivery
  • Implementing error handling, backpressure, and scaling mechanisms
  • Testing and tuning pipeline performance

Summary and Next Steps

Requirements

  • Familiarity with the Linux command line
  • Fundamental understanding of networking and data systems
  • Prior exposure to data streaming or ETL concepts

Target Audience

  • System administrators
  • Data engineers
  • Developers
  • DevOps professionals

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