Apache NiFi
Introduction to Apache NiFi
Apache NiFi is a powerful data integration tool designed to automate the flow of data between systems. This module introduces Apache NiFi, covering its core features, architecture, and use cases in data ingestion, processing, and integration.
Setting Up Apache NiFi
Learn how to install and configure Apache NiFi. This section covers system requirements, installation procedures, and initial setup. Explore how to configure NiFi instances, and understand the basics of NiFi’s user interface.
NiFi Architecture and Components
Discover the architecture of Apache NiFi, including its key components such as processors, flowfiles, and connections. Learn how NiFi’s architecture supports data flow management and integration, and how to design data flow pipelines.
Creating and Managing Data Flows
Gain insights into creating and managing data flows in Apache NiFi. Learn how to design flow pipelines, configure processors, and route data between different systems. Explore how to handle data transformation, enrichment, and aggregation.
Monitoring and Troubleshooting
Learn how to monitor and troubleshoot Apache NiFi. Explore NiFi’s monitoring tools, logs, and performance metrics. Understand techniques for diagnosing issues, managing system health, and ensuring data flow reliability.
Integration with Other Systems
Discover how to integrate Apache NiFi with other systems and technologies. Learn about NiFi’s connectors and integrations with databases, message queues, cloud services, and big data platforms. Explore how to use NiFi for end-to-end data integration.
Data Security and Access Control
Understand data security and access control in Apache NiFi. Learn about authentication, authorization, and encryption. Explore how to secure data flows, manage user access, and ensure compliance with security policies.
Performance Tuning and Optimization
Learn about performance tuning and optimization for Apache NiFi. Explore techniques for improving data flow efficiency, managing system resources, and handling large volumes of data. Understand best practices for configuring and maintaining NiFi instances.
Advanced Features and Customization
Explore advanced features and customization options in Apache NiFi. Learn how to extend NiFi with custom processors, controllers, and extensions. Understand how to adapt NiFi to meet specific data integration needs and use cases.
Apache NiFi Syllabus
Module 1: Introduction to Apache NiFi
- What is Apache NiFi?
- Fundamental Design Concepts of NiFi
- NiFi Architecture
- Performance Expectations and Characteristics of NiFi
- NiFi Features
- Terminology
- User Interface
Module 2: Building a DataFlow
- Adding Components to the Canvas
- Component Versions
- Configuring
- Processor
- Process Group
- Parameters
- Using Custom Properties with Expression Language
- Controller Services
- Reporting Tasks
- Connecting Components
- Processor Validation
- Site-to-Site
Module 3: Command and Control of the DataFlow
- Starting a Component
- Stopping a Component
- Terminating a Component’s Tasks
- Enabling/Disabling a Component
- Remote Process Group Transmission
Module 4: Navigating within a DataFlow
- Component Linking
- Component Alignment
- Search Components in DataFlow
Module 5: Monitoring of DataFlow
- Anatomy of
- Processor
- Process Group
- Remote Process Group
- Queue Interaction
- Historical Statistics of a Component
Module 6: Versioning a DataFlow
- Connecting to a NiFi Registry
- Version States
- Import a Versioned Flow
- Start Version Control
- Managing Local Changes
- Change Version
- Stop Version Control
- Nested Versioned Flows
- Parameters in Versioned Flows
- Variables in Versioned Flows
- Restricted Components in Versioned Flows
Module 7: Templates
- Creating a Template
- Importing a Template
- Instantiating a Template
- Managing Templates
Module 8: Data Provenance
- Provenance Events
- Searching for Events
- Details of an Event
- Replaying a FlowFile
- Viewing FlowFile Lineage
- Write Ahead Provenance Repository
Module 9: Repository Encryption
- Repository Encryption Protocol
- Repository Encryption Configuration
- Experimental Warning
- Other Management Features
Training
Basic Level Training
Duration : 1 Month
Advance Level Training
Duration : 1 Month
Project Level Training
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Total Training Period
Duration : 3 Months
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