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 Duration 21 hours

Course Outline

NiFi Fundamentals and Data Flow Concepts

  • Distinguishing data in motion from data at rest: underlying concepts and challenges
  • NiFi architecture: cores, flow controller, provenance, and bulletin board
  • Core components: processors, connections, controllers, and provenance tracking

Big Data Context and System Integration

  • The role of NiFi in Big Data ecosystems (Hadoop, Kafka, cloud storage)
  • Overview of HDFS, MapReduce, and contemporary alternatives
  • Practical use cases: stream ingestion, log shipping, and event pipelines

Installation, Configuration & Cluster Setup

  • Deploying NiFi in single-node and cluster modes
  • Configuring clusters: node roles, ZooKeeper integration, and load balancing
  • Orchestrating deployments using Ansible, Docker, or Helm

Designing and Managing Dataflows

  • Routing, filtering, splitting, and merging data flows
  • Processor configuration (including InvokeHTTP, QueryRecord, PutDatabaseRecord, etc.)
  • Managing schemas, data enrichment, and transformation operations
  • Implementing error handling, retry mechanisms, and backpressure control

Integration Scenarios

  • Connecting to databases, messaging queues, and REST APIs
  • Streaming data to analytics platforms: Kafka, Elasticsearch, or cloud storage
  • Integration with monitoring tools like Splunk, Prometheus, or logging pipelines

Monitoring, Recovery & Provenance

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

Performance Tuning & Optimization

  • Optimizing JVM settings, heap size, thread pools, and cluster parameters
  • Refining flow design to minimize bottlenecks
  • Managing resource isolation, flow prioritization, and throughput

Best Practices & Governance

  • Documentation standards, naming conventions, and modular design principles
  • Security measures: TLS, authentication, access control, and data encryption
  • Change management, versioning, role-based access, and audit trails

Troubleshooting & Incident Response

  • Addressing common issues: deadlocks, memory leaks, and processor errors
  • Performing log analysis, error diagnostics, and root cause investigation
  • Executing recovery strategies and flow rollbacks

Hands-on Lab: Realistic Data Pipeline Implementation

  • Constructing an end-to-end flow: ingestion, transformation, and delivery
  • Implementing error handling, backpressure, and scaling capabilities
  • Conducting performance testing and pipeline tuning

Summary and Future Directions

Requirements

  • Proficiency with the Linux command line
  • Fundamental knowledge of networking and data systems
  • Familiarity with data streaming or ETL principles

Intended Audience

  • System administrators
  • Data engineers
  • Software developers
  • DevOps practitioners

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