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Course Outline

NiFi and Data Flow Foundations

  • Contrasting data in motion versus data at rest: key concepts and associated challenges
  • NiFi architecture overview: cores, flow controller, provenance, and bulletin board
  • Core components: processors, connections, controllers, and provenance tracking

Big Data Ecosystem Context and Integration

  • NiFi's role within Big Data environments (including Hadoop, Kafka, and cloud storage)
  • Introduction to HDFS, MapReduce, and contemporary alternatives
  • Practical applications: stream ingestion, log transmission, and event pipelines

Deployment, Configuration, and Cluster Establishment

  • Setting up NiFi on single-node systems and in cluster modes
  • Cluster setup: defining node roles, Zookeeper integration, and load balancing
  • Managing NiFi deployments via Ansible, Docker, or Helm

Dataflow Design and Management

  • Techniques for routing, filtering, splitting, and merging flows
  • Configuring processors (such as InvokeHTTP, QueryRecord, PutDatabaseRecord, etc.)
  • Managing schemas, enrichment, and transformation tasks
  • Strategies for error handling, retry mechanisms, and backpressure management

Integration Scenarios

  • Linking with databases, messaging platforms, and REST APIs
  • Directing streams to analytics platforms like Kafka, Elasticsearch, or cloud storage
  • Connecting with tools such as Splunk, Prometheus, or logging pipelines

Monitoring, Recovery, and Provenance

  • Utilizing the NiFi UI, performance metrics, and provenance visualizer
  • Establishing autonomous recovery protocols and graceful failure handling
  • Implementing backups, flow versioning, and change management processes

Performance Tuning and Optimization

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

Best Practices and Governance

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

Troubleshooting and Incident Response

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

Practical Lab: Implementing a Realistic Data Pipeline

  • Constructing an end-to-end flow covering ingestion, transformation, and delivery
  • Applying error handling, backpressure controls, and scaling techniques
  • Conducting performance testing and pipeline tuning

Conclusion and Future Directions

Requirements

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

Intended Audience

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

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