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 Duration 21 hours

Course Outline

NiFi Fundamentals and Data Flow Concepts

  • Distinguishing between data in motion and data at rest: key concepts and associated challenges
  • NiFi architecture: core components, flow controller, provenance, and bulletin board
  • Essential elements: processors, connections, controllers, and provenance tracking

Big Data Context and Integration

  • The position of NiFi within Big Data ecosystems (including Hadoop, Kafka, and cloud storage)
  • An overview of HDFS, MapReduce, and contemporary alternatives
  • Application cases: stream ingestion, log transmission, and event pipelines

Installation, Configuration & Cluster Setup

  • Setting up NiFi in both single-node and cluster modes
  • Configuring clusters: node roles, Zookeeper integration, and load balancing strategies
  • Managing NiFi deployments using tools like Ansible, Docker, or Helm

Designing and Managing Dataflows

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

Integration Scenarios

  • Connecting to databases, messaging systems, and REST APIs
  • Streaming data to analytics platforms like Kafka, Elasticsearch, or cloud storage services
  • Integrating with monitoring and logging tools such as Splunk, Prometheus, or custom logging pipelines

Monitoring, Recovery & Provenance

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

Performance Tuning & Optimization

  • Adjusting JVM settings, heap size, thread pools, and clustering parameters
  • Refining flow designs to minimize bottlenecks
  • Implementing resource isolation, flow prioritization, and throughput regulation

Best Practices & Governance

  • Establishing flow documentation, naming conventions, and modular design standards
  • Security measures: TLS, authentication, access control, and data encryption
  • Enforcing change control, versioning, role-based access, and maintaining 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 rollback procedures

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 capabilities
  • Conducting performance testing and pipeline tuning

Summary and Next Steps

Requirements

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

Target Audience

  • System administrators
  • Data engineers
  • Developers
  • DevOps professionals

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