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Course Outline

Foundations of NiFi and Data Flow

  • Data in motion versus data at rest: underlying 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 role of NiFi within Big Data ecosystems (Hadoop, Kafka, cloud storage)
  • Introduction to HDFS, MapReduce, and contemporary alternatives
  • Practical applications: stream ingestion, log shipping, and event pipelines

Installation, Configuration & Cluster Setup

  • Deploying NiFi on single nodes and in cluster modes
  • Configuring clusters: defining node roles, utilizing Zookeeper, and implementing load balancing
  • Automating NiFi deployments using Ansible, Docker, or Helm

Designing and Managing Dataflows

  • 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 relationships, and managing backpressure

Integration Scenarios

  • Establishing connections with databases, messaging systems, and REST APIs
  • Streaming data to analytics platforms like Kafka, Elasticsearch, or cloud storage solutions
  • Integration with monitoring and logging tools such as Splunk, Prometheus, or logging pipelines

Monitoring, Recovery & Provenance

  • Leveraging the NiFi UI, metrics, and the provenance visualizer
  • Creating strategies for autonomous recovery and graceful failure management
  • Implementing backups, flow versioning, and change management protocols

Performance Tuning & Optimization

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

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 audit trails

Troubleshooting & Incident Response

  • Addressing common issues such as deadlocks, memory leaks, and processor errors
  • Conducting log analysis, error diagnostics, and root cause investigations
  • Developing recovery strategies and implementing flow rollbacks

Hands-on Lab: Realistic Data Pipeline Implementation

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

Summary and Next Steps

Requirements

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

Target Audience

  • System administrators
  • Data engineers
  • Developers
  • DevOps professionals
 21 Hours

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