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Course Outline

Core Concepts of NiFi and Data Flow

  • Contrasting data in motion with data at rest: underlying concepts and associated challenges
  • The NiFi architecture: examining cores, the flow controller, provenance tracking, and the bulletin board
  • Essential components: processors, connections, controllers, and provenance records

Context within Big Data and Integration

  • NiFi's role in Big Data ecosystems, including Hadoop, Kafka, and cloud storage solutions
  • Introduction to HDFS, MapReduce, and contemporary alternatives
  • Practical applications: stream ingestion, log transport, and event pipelines

Setup, Configuration & Cluster Deployment

  • Installing NiFi in both single-node and clustered modes
  • Configuring clusters: defining node roles, integrating Zookeeper, and balancing loads
  • Managing NiFi deployments through Ansible, Docker, or Helm

Creating and Overseeing Dataflows

  • Managing flow operations: routing, filtering, splitting, and merging
  • Configuring processors (such as InvokeHTTP, QueryRecord, and PutDatabaseRecord)
  • Managing schemas, data enrichment, and transformation processes
  • Addressing errors, managing retry relationships, and controlling backpressure

Integration Use Cases

  • Establishing connections to databases, messaging platforms, and REST APIs
  • Streaming data to analytics tools like Kafka, Elasticsearch, or cloud storage
  • Connecting with Splunk, Prometheus, or centralized logging pipelines

Oversight, Recovery & Provenance

  • Utilizing the NiFi interface, metrics, and the provenance visualizer
  • Designing strategies for autonomous recovery and graceful failure management
  • Managing backups, flow version control, and change administration

Performance Optimization & Tuning

  • Adjusting JVM settings, heap memory, thread pools, and cluster parameters
  • Refining flow architecture to minimize bottlenecks
  • Implementing resource isolation, flow prioritization, and throughput regulation

Best Practices & Governance

  • Standardizing flow documentation, naming conventions, and modular design
  • Security measures: TLS, authentication, access controls, and data encryption
  • Enforcing change control, versioning, role-based access, and audit logging

Diagnostics & Incident Management

  • Addressing common problems: deadlocks, memory leaks, and processor failures
  • Analyzing logs, diagnosing errors, and investigating root causes
  • Executing recovery plans and flow rollbacks

Practical Lab: Implementing a Realistic Data Pipeline

  • Constructing a complete flow from ingestion through transformation to delivery
  • Applying error handling, backpressure management, and scaling techniques
  • Conducting performance tests and tuning the pipeline

Recap and Future Pathways

Requirements

  • Familiarity with the Linux command line
  • Foundational knowledge of networking protocols and data systems
  • Previous exposure to data streaming or ETL methodologies

Target Audience

  • System Administrators
  • Data Engineers
  • Developers
  • DevOps Specialists
 21 Hours

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