Introduction

Apache NiFi is an open-source data integration tool designed for automating the movement and transformation of data between various systems. It provides an intuitive, drag-and-drop interface for creating data pipelines and supports integration with a wide range of data sources and sinks, including databases, messaging systems, cloud storage, and more. NiFi enables organizations to manage, process, and route data securely and efficiently across complex data flows in real time.

This training course will provide participants with a comprehensive understanding of data orchestration concepts, NiFi’s architecture, key features, and practical use cases. Participants will learn how to design, implement, and optimize data pipelines using Apache NiFi for various data integration, processing, and monitoring tasks.

Objectives

By the end of this course, participants will:

• Understand the key principles of data orchestration and integration.
• Learn how Apache NiFi works and how to install and configure it.
• Explore NiFi’s architecture and its components, such as processors, controllers, and connections.
• Design and build data flows using NiFi’s drag-and-drop interface.
• Integrate NiFi with different data sources, including databases, REST APIs, and cloud storage.
• Learn how to route, transform, and filter data within NiFi.
• Implement data security, error handling, and data lineage tracking.
• Understand performance optimization, scaling NiFi, and best practices for production environments.
• Gain hands-on experience by working on real-world data integration projects.

Who Should Attend?

This course is ideal for:

• Data engineers, system architects, and IT professionals responsible for data integration and orchestration.
• Data scientists and analysts who need to automate data workflows and processing.
• Cloud engineers and professionals working with big data or real-time data systems.
• Business intelligence and data pipeline managers seeking to improve data integration and workflows.
• Developers interested in learning about data integration and orchestration tools.

Day 1: Introduction to Apache NiFi and Data Orchestration

Morning Session: Overview of Data Orchestration

• Introduction to data orchestration and integration.
• Understanding data flows: What is data movement, routing, and transformation?
• Use cases for data orchestration in modern data architectures: ETL, ELT, real-time data processing, and cloud data pipelines.
• Overview of Apache NiFi: Core components and features.
• NiFi vs other integration tools: How NiFi compares to traditional ETL tools like Informatica or Talend.

Afternoon Session: Apache NiFi Architecture and Components

• NiFi architecture: FlowFile, processors, flow controllers, and NiFi registry.
• Overview of NiFi’s core components:
  • Processors: The workhorse of NiFi – ingest, process, and route data.
  • Process Groups: Logical grouping of processors for better organization.
  • Connections: Data flows between processors.
  • Controller Services: Centralized configuration for resources like databases or message queues.
  • Remote Process Groups: Integration with other NiFi instances and remote systems.
• Hands-on: Installing Apache NiFi and setting up the NiFi web UI.

Day 2: Building Data Flows and Integrating Data Sources

Morning Session: Creating and Managing Data Flows

• Building simple data flows: Understanding the flow structure and data movement.
• Setting up data ingestion pipelines: Reading from files, databases, and APIs.
• Connecting processors and using queues: Data routing and batch processing.
• Working with NiFi templates for reusability and deployment.
• Hands-on: Create a basic NiFi data flow to read from a file system and send data to a database.

Afternoon Session: Data Source Integration

• Integrating with different data sources:
  • Databases: Using NiFi processors to connect to SQL and NoSQL databases (e.g., MySQL, MongoDB).
  • APIs: Pulling data from REST APIs using HTTP processors.
  • Cloud Storage: Reading and writing data to cloud services like Amazon S3, Azure Blob Storage.
• Real-time streaming integration: Using processors for message queues and streaming platforms (e.g., Kafka).
• Hands-on: Integrate NiFi with a REST API and store the data in a cloud storage solution.

Day 3: Data Transformation, Routing, and Advanced Processing

Morning Session: Data Transformation and Filtering

• Transforming data with NiFi:
  • Expression Language: Dynamically manipulating data values.
  • Processors for transformation: ReplaceText, UpdateAttribute, ExecuteScript.
  • Filtering data: Use processors like RouteOnAttribute and EvaluateJsonPath to filter and route data.
• Building complex data transformation flows: Combining multiple processors for complex data flows.
• Hands-on: Build a flow that extracts data from a JSON file, transforms it, and routes it based on certain criteria.

Afternoon Session: Advanced NiFi Features

• Data enrichment: Adding additional data from external sources (APIs, databases) using LookupProcessors.
• Handling errors and retries: NiFi’s error handling capabilities (Retry, backpressure, prioritizers).
• Real-time processing: Implementing real-time streaming analytics with NiFi.
• Data lineage and audit trails: Using NiFi to track and log data provenance and lineage.
• Hands-on: Set up error handling and retry logic for a data flow and track data provenance.

Day 4: Data Security, Monitoring, and Performance Optimization

Morning Session: Securing NiFi Data Flows

• Data encryption and securing sensitive data in NiFi flows.
• Implementing role-based access control (RBAC) and securing NiFi components.
• Using SSL/TLS for secure communication between NiFi nodes.
• Authentication and authorization using LDAP, Kerberos, or NiFi’s internal user management.
• Hands-on: Set up user roles and permissions for a NiFi instance.

Afternoon Session: Performance Optimization and Monitoring

• Optimizing NiFi performance: Tuning processor settings, managing backpressure, and handling large datasets.
• Monitoring NiFi: Using NiFi’s built-in monitoring tools and external solutions (e.g., Prometheus, Grafana).
• Best practices for scaling NiFi clusters and handling high-throughput data flows.
• Hands-on: Optimize an existing NiFi flow for better performance under heavy load.

Day 5: NiFi Advanced Topics and Real-World Use Cases

Morning Session: NiFi in Distributed Environments

• Clustered NiFi: Understanding NiFi clusters and high availability.
• Configuring NiFi for fault tolerance, load balancing, and horizontal scaling.
• Working with NiFi Registry for version control and managing flow deployments.
• Hands-on: Set up a NiFi cluster and deploy a flow across multiple nodes.

Afternoon Session: Real-World Use Cases and Best Practices

• Common use cases for Apache NiFi in the enterprise: ETL/ELT, data migration, real-time analytics, IoT data ingestion, and cloud data pipelines.
• Case studies: Implementing NiFi for data synchronization, data lakes, and real-time processing in industries like finance, healthcare, and e-commerce.
• Best practices for maintaining and managing NiFi in production environments.
• Hands-on: Develop an end-to-end real-world data flow for an industry-specific use case.

Materials and Tools:

• Software: Apache NiFi, Apache Kafka, NiFi Registry, NiFi processors for integration with databases, APIs, cloud storage, etc.
• Programming Languages: NiFi Expression Language, basic scripting (Groovy, Python, etc.).
• Datasets: Sample datasets for integration (JSON, CSV, XML, relational data).
• Recommended Reading: “Apache NiFi: The Definitive Guide” by Matt Ward.

Post-Course Support:

• Access to course materials, recorded sessions, and a community forum for ongoing support.
• Practical assignments and a final project to implement a fully integrated NiFi pipeline.
• Continuing access to NiFi resources and documentation for further learning.