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 Duration 21 hours

Course Outline

NiFi and Data Flow Fundamentals

  • Data in motion versus 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 (Hadoop, Kafka, cloud storage)
  • Introduction to HDFS, MapReduce, and contemporary alternatives
  • Application cases: stream ingestion, log shipping, and event pipelines

Installation, Configuration, and Cluster Setup

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

Designing and Managing Dataflows

  • Implementing routing, filtering, splitting, and merging operations
  • Processor configuration (e.g., InvokeHTTP, QueryRecord, PutDatabaseRecord)
  • Managing schema, data enrichment, and transformation tasks
  • Error management, retry mechanisms, and backpressure handling

Integration Scenarios

  • Connecting to databases, messaging systems, and REST APIs
  • Streaming data to analytics platforms like Kafka, Elasticsearch, or cloud storage
  • Integrating with Splunk, Prometheus, or other logging pipelines

Monitoring, Recovery, and Provenance

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

Performance Tuning and Optimization

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

Best Practices and Governance

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

Troubleshooting and Incident Response

  • Addressing common issues such as deadlocks, memory leaks, and processor errors
  • Analyzing logs, diagnosing errors, and investigating root causes
  • Recovery techniques and flow rollback procedures

Hands-on Lab: Realistic Data Pipeline Implementation

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

Summary and Next Steps

Requirements

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

Target Audience

  • System administrators
  • Data engineers
  • Developers
  • DevOps professionals

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