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Course Outline

Fundamentals of NiFi and Data Flow

  • Concepts and challenges of data in motion versus data at rest.
  • NiFi architecture: cores, flow controller, provenance, and bulletin.
  • Key components: processors, connections, controllers, and provenance.

Big Data Context and Integration

  • The role of NiFi within Big Data ecosystems (Hadoop, Kafka, cloud storage).
  • Overview of HDFS, MapReduce, and modern alternatives.
  • Use cases: stream ingestion, log shipping, and event pipelines.

Installation, Configuration & Cluster Setup

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

Designing and Managing Dataflows

  • Routing, filtering, splitting, and merging flows.
  • Processor configuration (InvokeHTTP, QueryRecord, PutDatabaseRecord, etc.).
  • Handling schema, enrichment, and transformation operations.
  • Error handling, retry relationships, and backpressure.

Integration Scenarios

  • Connecting to databases, messaging systems, and REST APIs.
  • Streaming to analytics systems such as Kafka, Elasticsearch, or cloud storage.
  • Integrating with Splunk, Prometheus, or logging pipelines.

Monitoring, Recovery & Provenance

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

Performance Tuning & Optimization

  • Tuning JVM, heap, thread pools, and clustering parameters.
  • Optimizing flow design to minimize bottlenecks.
  • Resource isolation, flow prioritization, and throughput control.

Best Practices & Governance

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

Troubleshooting & Incident Response

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

Hands-on Lab: Realistic Data Pipeline Implementation

  • Building an end-to-end flow: ingestion, transformation, and delivery.
  • Implementing error handling, backpressure, and scaling.
  • Performance testing and tuning the pipeline.

Summary and Next Steps

Requirements

  • Experience with the Linux command line.
  • Basic understanding of networking and data systems.
  • Familiarity with data streaming or ETL concepts.

Audience

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

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