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 Duration 21 hours

Course Outline

NiFi Fundamentals and Data Flow

  • Contrasting data in motion versus data at rest: concepts and associated challenges
  • NiFi architecture: exploring cores, flow controllers, provenance, and bulletins
  • Essential components: processors, connections, controllers, and provenance tracking

Big Data Context and Integration

  • The position 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 in both single-node and cluster modes
  • Configuring clusters: defining node roles, managing Zookeeper, and balancing loads
  • Orchestrating NiFi deployments using Ansible, Docker, or Helm

Designing and Managing Dataflows

  • Managing flow operations: routing, filtering, splitting, and merging
  • Configuring processors (e.g., InvokeHTTP, QueryRecord, PutDatabaseRecord)
  • Executing schema handling, enrichment, and transformation tasks
  • Managing errors, setting up retry relationships, and applying backpressure

Integration Scenarios

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

Monitoring, Recovery & Provenance

  • Utilizing the NiFi UI, metrics, and the provenance visualizer
  • Designing for autonomous recovery and graceful handling of failures
  • Implementing backup strategies, flow versioning, and change management

Performance Tuning & Optimization

  • Adjusting JVM settings, heap size, thread pools, and clustering parameters
  • Refining flow designs to minimize bottlenecks
  • Managing resource isolation, flow prioritization, and throughput control

Best Practices & Governance

  • Ensuring flow documentation, adhering to naming standards, and adopting modular design
  • Security measures: TLS, authentication, access control, and data encryption
  • Enforcing change control, versioning, role-based access, and maintaining audit trails

Troubleshooting & Incident Response

  • Addressing common issues: deadlocks, memory leaks, and processor errors
  • Conducting log analysis, error diagnostics, and root cause investigations
  • Applying recovery strategies and executing flow rollbacks

Hands-on Lab: Realistic Data Pipeline Implementation

  • Constructing an end-to-end flow covering ingestion, transformation, and delivery
  • Implementing error handling, backpressure mechanisms, and scaling capabilities
  • Testing performance and tuning the pipeline for optimal results

Summary and Next Steps

Requirements

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

Target Audience

  • System administrators
  • Data engineers
  • Developers
  • DevOps specialists

Custom Corporate Training

Training solutions designed exclusively for businesses.

  • Customized Content: We adapt the syllabus and practical exercises to the real goals and needs of your project.
  • Flexible Schedule: Dates and times adapted to your team's agenda.
  • Format: Online (live), In-company (at your offices), or Hybrid.
Investment

Price per private group, online live training, starting from 4800 € + VAT*

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