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Course Outline

Fundamentals of NiFi and Data Flow

  • Data in motion versus data at rest: concepts and associated challenges
  • NiFi architecture: cores, flow controller, provenance, and bulletin
  • Essential components: processors, connections, controllers, and provenance

Big Data Context and Integration

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

Installation, Configuration & Cluster Setup

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

Designing and Managing Dataflows

  • Routing, filtering, splitting, and merging flows
  • Configuring processors (e.g., InvokeHTTP, QueryRecord, PutDatabaseRecord)
  • Managing schema, enrichment, and transformation operations
  • Implementing error handling, retry relationships, and backpressure mechanisms

Integration Scenarios

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

Monitoring, Recovery & Provenance

  • Utilizing the NiFi UI, metrics, and the provenance visualizer
  • Designing autonomous recovery and graceful failure handling
  • Backup strategies, flow versioning, and change management

Performance Tuning & Optimization

  • Adjusting JVM, heap, thread pools, and clustering parameters
  • Optimizing flow architecture to minimize bottlenecks
  • Resource isolation, flow prioritization, and throughput control

Best Practices & Governance

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

Troubleshooting & Incident Response

  • Addressing common issues: deadlocks, memory leaks, and processor errors
  • Log analysis, error diagnostics, and root cause investigation
  • Recovery strategies and flow rollback procedures

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 tests and pipeline tuning

Summary and Next Steps

Requirements

  • Proficiency with the Linux command line
  • Fundamental understanding 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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