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Course Outline

NiFi and Data Flow Fundamentals

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

Big Data Context and Integration

  • The role of NiFi within Big Data ecosystems, including Hadoop, Kafka, and cloud storage
  • An overview of HDFS, MapReduce, and modern alternatives
  • Practical use cases: stream ingestion, log shipping, and event pipelines

Installation, Configuration, and Cluster Setup

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

Designing and Managing Dataflows

  • Techniques for routing, filtering, splitting, and merging flows
  • Configuring processors (e.g., InvokeHTTP, QueryRecord, PutDatabaseRecord)
  • Managing schemas, 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 systems such as Kafka, Elasticsearch, or cloud storage
  • Integrating with Splunk, Prometheus, or logging pipelines

Monitoring, Recovery, and 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 and Optimization

  • Tuning JVM settings, heap memory, thread pools, and clustering parameters
  • Optimizing flow design to minimize bottlenecks
  • Applying resource isolation, flow prioritization, and throughput control

Best Practices and Governance

  • Documenting flows, establishing naming standards, and adopting modular design
  • Security measures: TLS, authentication, access control, and data encryption
  • Implementing change control, versioning, role-based access, and audit trails

Troubleshooting and Incident Response

  • Addressing common issues such as deadlocks, memory leaks, and processor errors
  • Performing log analysis, error diagnostics, and root cause investigations
  • Developing recovery strategies 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 capabilities
  • Conducting performance tests and tuning the pipeline

Summary and Next Steps

Requirements

  • Proficiency with the Linux command line
  • A 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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