Apache NiFi: Guide for Beginners to Experts (2025)

In this lesson, we will walk you through the process of downloading and preparing the necessary tools to start working with Apache NiFi. Here's what we'll cover:

• Downloading Apache NiFi: Where to find the correct version of NiFi and how to download it safely.

• Installing Java JDK: Setting up the Java Development Kit (JDK) required to run NiFi.

• System Requirements: Verifying your system is ready for installation.

• Basic Configuration: Preparing your environment for NiFi to ensure smooth operations.

By the end of this lesson, you'll have Apache NiFi and JDK installed and ready to use on your Windows system. Let’s get started!

In this lesson, we will walk you through the Apache NiFi user interface (UI) and help you understand its core components. Here's what we'll cover:

• Navigating the UI: Understanding the layout, toolbar, and canvas.

• Processors and Process Groups: How to add, configure, and manage components.

• Connections and Queues: Managing data flow between processors.

• Monitoring and Logs: Tracking data movement and identifying issues.

By the end of this lesson, you'll be familiar with the NiFi UI and ready to start designing your first data flow. Let’s dive in!

In this lesson, we will explore Apache NiFi Templates, a powerful feature that allows you to save, share, and reuse workflows efficiently. Templates help streamline data flow development and make it easier to replicate successful processes across multiple environments.

Understanding what NiFi Templates are and how they work.
Creating a template from an existing data flow.
Exporting templates to share workflows with others.
Importing templates to quickly set up pre-configured workflows.
Best practices for organizing and managing templates in large-scale NiFi projects.

By the end of this lesson, you’ll be able to create, manage, and reuse NiFi templates to speed up workflow development and improve efficiency. Let’s get started!

In this lesson, we will learn how to establish a connection between Apache NiFi and PostgreSQL, enabling seamless data integration between the two systems. Connecting NiFi to databases is crucial for ETL workflows, data ingestion, and automation.

Setting up JDBC drivers for PostgreSQL in NiFi.
Configuring the DBCPConnectionPool to enable database connectivity.
Using the ExecuteSQL and PutDatabaseRecord processors for querying and inserting data.
Best practices for error handling and performance optimization in database operations.

By the end of this lesson, you will be able to connect NiFi to PostgreSQL, execute SQL queries, and manage database records efficiently within your data workflows. Let’s dive in!

In this lesson, we will learn how to establish a connection between Apache NiFi and Microsoft SQL Server, enabling seamless data integration between the two systems. Connecting NiFi to databases is essential for ETL workflows, data ingestion, and automation. Here's what we’ll cover:

• Setting up JDBC drivers for Microsoft SQL Server in NiFi.

• Configuring the DBCPConnectionPool to enable database connectivity.

• Using the ExecuteSQL and PutDatabaseRecord processors for querying and inserting data.

• Best practices for error handling and performance optimization in database operations.

By the end of this lesson, you will be able to connect NiFi to Microsoft SQL Server, execute SQL queries, and manage database records efficiently within your data workflows. Let’s dive in!

In this lesson, we’ll take our first hands-on step into Apache NiFi by exploring the GetFile processor. This processor is the foundation for many workflows as it allows you to bring files into NiFi from your local system.

Here’s what we’ll cover:

• Understanding the role of the GetFile processor in NiFi workflows.

• Configuring GetFile to read files from a local directory.

• Setting up basic properties to ensure smooth file ingestion.

• Running the processor to test its functionality.

By the end of this lesson, you’ll have a clear understanding of how to use GetFile to start your NiFi workflows and bring data into the system. Let’s get started!

In this lesson, we’ll dive into the GenerateFlowFile processor, a powerful tool for creating test data and experimenting with workflows in Apache NiFi. This processor is especially useful for learning and troubleshooting.

Here’s what we’ll cover:

• Understanding the purpose of GenerateFlowFile in NiFi workflows.

• Configuring the processor to create custom data payloads.

• Using GenerateFlowFile to simulate real-world data scenarios.

• Running and inspecting the processor's output for testing purposes.

By the end of this lesson, you’ll know how to use GenerateFlowFile to generate data dynamically, helping you build and test workflows efficiently. Let’s get started!

In this lesson, we’ll explore the InvokeHTTP processor, a powerful tool in Apache NiFi for interacting with REST APIs and external HTTP endpoints. This processor enables seamless integration with web services to send or retrieve data.

Here’s what we’ll cover:

• Understanding the role of InvokeHTTP in API communication.

• Configuring InvokeHTTP to make GET and POST requests.

• Sending requests to an API and handling responses effectively.

• Demonstrating how to use headers, authentication, and parameters with HTTP requests.

By the end of this lesson, you’ll be able to configure and use InvokeHTTP to connect to external web services and integrate them into your data workflows. Let’s dive in!

In this lesson, we’ll learn how to use the ConvertExcelToCSVProcessor in Apache NiFi to transform Excel files into a CSV format. This processor is incredibly useful for handling structured data stored in spreadsheets and preparing it for further processing in workflows.

Here’s what we’ll cover:

• Understanding the purpose of ConvertExcelToCSVProcessor in NiFi workflows.

• Configuring the processor to read Excel files and convert them into CSV format.

• Handling multi-sheet Excel files and selecting specific sheets for conversion.

• Using the output CSV files in downstream processors for further data processing.

By the end of this lesson, you’ll know how to efficiently convert Excel files into CSV, making it easier to integrate spreadsheet data into your automated workflows. Let’s get started!

In this lesson, we’ll explore the ExecuteSQLRecord processor in Apache NiFi, a powerful tool for executing SQL queries directly against a database and retrieving structured data for further processing.

Here’s what we’ll cover:

• Understanding the role of ExecuteSQLRecord in NiFi workflows.

• Configuring the processor to connect to a database using a connection pool.

• Writing and executing SQL queries to retrieve data efficiently.

• Handling and transforming the output data using Record Readers and Writers.

• Managing success and failure paths for better workflow control.

By the end of this lesson, you’ll be able to use ExecuteSQLRecord to query databases seamlessly and integrate the results into your data pipelines. Let’s dive in!

In this lesson, we will explore the ReplaceText processor in Apache NiFi, a powerful tool for modifying and transforming text content within FlowFiles. This processor allows you to manipulate data dynamically using regular expressions, string replacements, and text formatting.

Here’s what we’ll cover:

• Understanding the purpose of ReplaceText in data transformation.

• Configuring the processor to modify FlowFile content based on patterns.

• Using regular expressions to replace or format text dynamically.

• Practical examples of replacing, appending, and restructuring text data.

By the end of this lesson, you’ll be able to efficiently modify text-based data in NiFi workflows, making it easier to clean, structure, and process information. Let’s dive in!

In this lesson, we will explore the UpdateRecord processor in Apache NiFi, a powerful tool for modifying and enriching structured data within FlowFiles. This processor enables you to update, rename, or add new fields to your records dynamically without altering the original schema.

Here’s what we’ll cover:

• Understanding the purpose and use cases of UpdateRecord.

• Configuring the processor to modify specific fields within structured data.

• Using expression language to apply dynamic updates.

• Applying changes to JSON, CSV, and Avro formats with record-based processing.

By the end of this lesson, you’ll be able to use UpdateRecord to efficiently transform and enhance data within NiFi workflows. Let’s get started!

In this lesson, we will explore the ExecuteScript processor in Apache NiFi, a powerful tool that allows you to run custom scripts in languages like Python, Groovy, and JavaScript to manipulate FlowFiles and enhance data processing workflows.

Here’s what we’ll cover:

• Understanding the purpose of ExecuteScript in NiFi workflows.

• Writing and executing scripts to process data dynamically.

• Configuring script properties and handling FlowFiles within scripts.

• Practical examples of using Python or Groovy to modify and transform data.

By the end of this lesson, you’ll be able to use ExecuteScript to introduce custom logic into your NiFi pipelines, enabling more advanced data processing capabilities. Let’s dive in!

In this lesson, we explore how to dynamically construct SQL queries in NiFi using SplitText and attribute substitution in the WHERE clause.

Key topics covered:
Splitting text data into multiple FlowFiles using the SplitText processor.
Extracting dynamic values and storing them as FlowFile attributes.
Using these attributes to construct SQL queries dynamically in the ExecuteSQL processor.
Practical implementation of attribute-based filtering in the WHERE clause.

By the end of this lesson, you'll understand how to make your SQL queries more flexible and adaptive by leveraging NiFi’s powerful attribute-based processing.

In this lesson, we will learn how to process CSV files and convert them into JSON format using Apache NiFi. Working with structured data in different formats is essential for ETL pipelines, data integration, and API interactions.

Setting up Record Readers and Writers for CSV and JSON formats.

Using the ConvertRecord processor to transform CSV into JSON.

Configuring schema handling for structured data processing.

Writing the transformed JSON output to a file system, API, or database.

Best practices for handling large CSV files efficiently.

By the end of this lesson, you’ll know how to seamlessly convert CSV data into JSON format and use it in real-world NiFi workflows. Let’s get started!

In this lesson, we will learn how to retrieve, process, and manage files using Apache NiFi’s built-in processors. File ingestion is a crucial step in ETL workflows, automation, and data processing, allowing NiFi to efficiently read, modify, and route files based on specific conditions.

Here’s what we’ll cover:

Using the GetFile processor to retrieve files from a directory.
Processing file content using transformation processors.
Modifying metadata with UpdateAttribute for better tracking.
Handling errors and failures in file ingestion workflows.
Routing files dynamically based on conditions and attributes.

By the end of this lesson, you’ll be able to ingest, process, and manage file-based data in NiFi workflows efficiently. Let’s get started!

In this lesson, we will explore how to establish effective connections between processors in Apache NiFi and utilize its powerful components to create seamless data workflows. You will learn to work with essential processors, including:

• GetFile: Retrieve files from a defined source for processing.

• UnpackContent: Extract content from files for further processing.

• UpdateAttribute: Dynamically update specific file or flow attributes.

• RouteOnAttribute: Route data flow based on defined attribute conditions.

• QueryRecord: Perform record-level querying for data validation.

• PutDatabaseRecord: Insert or update records in a database system.

• EvaluateJsonPath: Evaluate JSON content to extract or validate fields.

• MergeContent: Combine multiple content files into a single flow file.

• InvokeHTTP: Make HTTP calls for external API interactions.

By the end of the lesson, you will be able to connect these processors efficiently to create a robust, scalable data processing pipeline tailored to real-world scenarios.

This lesson will guide you through a detailed Apache NiFi workflow designed for orchestrating data processing, transformation, and storage. You will learn how to integrate and configure various processors to build efficient, scalable pipelines that handle complex data scenarios.

What We Will Use:

• InvokeHTTP: To make HTTP calls and integrate with external APIs.

• UpdateAttribute: To dynamically modify flow file attributes based on data logic.

• EvaluateJsonPath: To extract or validate specific fields from JSON content.

• MergeContent: To combine multiple flow files into a single file for efficient processing.

• PutDatabaseRecord: To insert or update records into a database system seamlessly.

• RouteOnAttribute: To direct the flow based on defined conditions and ensure accurate processing.

• QueryRecord: To validate and filter data at the record level.

• UnpackContent: To extract or unpack the content of compressed files for further processing.

• GetFile: To retrieve source files for processing.

How This Will Help:

• Efficiency: Build workflows that minimize manual intervention while automating data handling.

• Error Handling: Implement retry logic and error routes for robust failure management.

• Scalability: Design reusable and scalable pipelines for real-world applications.

• Data Accuracy: Ensure data integrity with JSON validation and record-level querying.

• Integration: Connect with external APIs, databases, and systems to enable end-to-end processing.

By the end of this lesson, you will have hands-on experience creating and managing NiFi workflows that are tailored to real-world data challenges.

In this lesson, we will explore how to use the FlattenJson processor in Apache NiFi to simplify and normalize nested JSON structures. Flattening JSON is a critical step in preparing complex data for processing, querying, and integration. Here's what we'll cover:

• Understanding the purpose and use cases of the FlattenJson processor.

• Configuring the processor to handle various nested JSON structures.

• Practical examples of transforming JSON for further processing or storage.

• Best practices for efficient and error-free JSON flattening.

By the end of this lesson, you will be able to use FlattenJson effectively, making it easier to work with structured data in your NiFi workflows. Let’s get started!

In this lesson, we will explore how to effectively use Output Ports within Processor Groups in Apache NiFi. You'll learn how to structure data flows, enable communication between different groups, and streamline data routing efficiently.

Understanding Input & Output Ports in NiFi
Configuring Output Ports inside Processor Groups
Connecting Processor Groups for modular workflows
Best practices for optimizing data flow design

By the end of this lesson, you will be able to configure and use Output Ports to enhance data processing efficiency in Apache NiFi. Let’s get started!

In this lesson, we will explore how to effectively use SQL functions within Apache NiFi workflows to perform data transformation, filtering, and aggregation. You'll learn how to configure QueryRecord, ExecuteSQL, and PutDatabaseRecord processors to apply SQL logic directly within NiFi.

Here's what we'll cover:
Understanding how SQL functions can enhance NiFi data flows
Using string manipulation, mathematical operations, date formatting, and conditional expressions
Implementing best practices for optimizing SQL execution in NiFi
Handling common challenges and troubleshooting errors in SQL-based processing

By the end of this lesson, you will be able to apply SQL functions in NiFi workflows, enabling efficient and automated data transformations. Let’s get started!

In this lesson, we will focus on leveraging Apache NiFi to facilitate seamless data migration from a PostgreSQL database to a MySQL database. You will gain hands-on experience with processors like ExecuteSQLRecord and PutDatabaseRecord to extract, transform, and load data between these two systems.

Here's what we'll cover:

• Configuring connections for PostgreSQL and MySQL in Apache NiFi.

• Using processors to read data from PostgreSQL and map it to MySQL tables.

• Addressing schema differences and ensuring data integrity during the migration process.

• Handling errors and optimizing performance in ETL workflows.

By the end of this lesson, you will have the skills to effectively migrate data between PostgreSQL and MySQL using Apache NiFi, ensuring accurate and efficient transfer for your data integration needs. Let’s dive in!

In this lesson, we will dive into a real-world issue that occurred due to scheduling conflicts in a NiFi pipeline. A cron job was set up to fetch data every day at 7 a.m., but the table being queried wasn’t updated until 9 a.m., resulting in empty data fetches. This caused confusion and delays in the data processing pipeline.

We will explore how such issues can be identified and resolved. Specifically, you’ll learn how to:

• Use conditional routing to detect and handle empty data (record.count:equals(0)).

• Synchronize pipeline execution with data availability.

• Ensure that your workflows process data accurately without missing updates.

By the end of this lesson, you’ll understand how to avoid fetching incomplete or zero data in your NiFi pipelines and implement practical solutions to improve your data workflows.

In this section, we will focus on enhancing the visual clarity and organization of your NiFi workflows. You will learn how to effectively arrange and align processors, use color coding, and structure your flow for better readability and collaboration. By mastering these design techniques, you’ll create workflows that are not only functional but also visually intuitive for team members and stakeholders.

What You Will Learn:

• Processor Alignment: Techniques to align processors vertically and horizontally for a clean layout.

• Color Coding: How to apply colors to processors and connections for easier differentiation.

• Workflow Organization: Structuring the layout to reflect the logical flow of data.

• Clarity and Presentation: Tips for creating workflows that are easy to understand at a glance.

By the end of this section, your workflows will not only perform efficiently but will also look polished and professional, making collaboration and troubleshooting significantly easier.

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