Apache Hop VS Kettle: A Brief Comparison

Kettle & Apache Hop

Kettle and Apache Hopare two prominent names in the realm of data integration.

Kettle, also known as Pentaho Data Integration (PDI), has long been recognized as a versatile and feature-rich ETL (Extract, Transform, Load) tool, known for its flexibility, scalability, and extensive community support.

On the other hand, Hop represents a newer entrant in the data integration landscape, developed as a modern fork, lightweight alternative with a focus on simplicity, performance, and extensibility.

Note: If you're familiar with Kettle but haven't yet explored Apache Hop, the Apache Hop Fundamentals course is perfect for you.

What will you find here?

• A Brief Comparison
  • Concepts
  • Tools
  • Configuration and Environment Setup
  • Engines and Execution
  • Features and Functionalities
  • Not in Apache Hop
  • New Metadata Types in Apache Hop

• Compatibility
  • Should I transfer my projects to Apache Hop?

• Conclusion
  • Resources

A Brief Comparison

While they share some similarities in terms of basic data integration functionalities, there are also significant differences in terms of features, support, and future outlook.

In this section, we will explore in detail how Apache Hop and Kettle compare in areas such as scalability, flexibility, ease of use, and compatibility with emerging technologies.

Concepts

First, it's crucial to understand the terminology used in both environments. While some terms may overlap, others might have different meanings or implementations. Here are some key terms commonly used:

Tools

From graphical user interfaces to scripts for running pipelines and workflows, we'll examine how each tool serves a unique purpose in both platforms.

By understanding the differences and similarities, you can decide about which platform best suits your data projects needs.

Configuration and Environment Setup

One of the primary distinctions lies in how Apache Hop manages projects and environments.

Projects serve as a container for related data integration workflows and pipelines, offering a logical separation between different project scopes. Environments, on the other hand, define the execution context for a project, encompassing database connections, file locations, and other configuration settings.

However, project files alone may not include the necessary metadata settings and variable values for optimal project performance in a specific environment. To address this, environments are utilized to store configurations tailored to different project lifecycle phases, such as Development, Testing, or Production. 

Engines and Execution

Apache Hop's pluggable architecture allows users to leverage a wider range of runtime engines, including Apache Spark, Apache Flink, and Google Cloud DataFlow, for optimized data processing.

Features and Functionalities

From project management to metadata handling and graphical user interface capabilities, we'll explore how each tool addresses various aspects of the data integration process. Let's explore the key features and functionalities of Kettle and Hop side by side.

Not in Apache Hop

Now, let's explore the functionalities present in Kettle that are not available in Apache Hop.

• The Java Naming and Directory Interface (JNDI): In Kettle/PDI, JNDI relies on an open-source project that hasn't seen updates in roughly a decade. Given its lack of relevance to Hop, this functionality was discontinued.

• Repositories: In today's landscape, code repositories are best suited for version control systems (VCS). Therefore, we've moved away from utilizing file, database, and PDI EE repositories.

• Formula step: It has been replaced with a more efficient transform with the same name.

New Metadata Types in Apache Hop

Apache Hop introduces a range of new metadata types that expand the capabilities of data integration projects. Let's explore some of the new metadata types available in Apache Hop:

• Pipeline Log: It stores information about the execution of a pipeline, including the start time, end time, and duration of the pipeline run. It also records the number of rows processed and the status of each transform within the pipeline.
• Workflow Log: It is similar to the Pipeline Log. It is a record of the activities and operations performed during the execution of a workflow. It stores information about each action executed within the workflow, such as the start and end time of each action, the status of the action, and any error messages or warnings that occurred during execution.
• Pipeline Prob: It is a mechanism to retrieve metadata from a pipeline without actually executing the pipeline. It allows you to view and validate metadata before the pipeline runs, helping to identify potential issues and errors. The metadata retrieved from the Pipeline Probe can be used in subsequent steps or for debugging purposes.
• Unit Test: It allows users to define tests for their pipelines. It provides an easy and efficient way to test the functionality and behavior of a pipeline by specifying expected results for certain inputs.

Compatibility

Important question: Is the ETL code compatible between Kettle and Apache Hop?

No, the ETL code is not directly compatible between Kettle and Apache Hop. However, Apache Hop provides an import tool that allows you to migrate your existing ETL code from Kettle to Apache Hop.

The Apache Hop import tool executes the following conversions:

These conversions ensure the transition from Kettle to Apache Hop while maintaining the integrity and functionality of ETL workflows.

Should I transfer my projects to Apache Hop?

When it comes to choosing between Apache Hop and Kettle, it's essential to consider your specific project requirements and use cases. Here are some questions you can use to decide what is the best option for your project:

• What are the primary factors influencing your decision between sticking with Kettle or migrating to Apache Hop?
• Have you encountered any limitations or challenges with Kettle that could potentially be addressed by switching to Apache Hop?
• How important is long-term support and ongoing development for your data integration solution?
• Are there specific features or functionalities in Apache Hop that you find compelling or advantageous compared to Kettle or vice-verse?
• What level of flexibility and customization do you require in your data integration workflows?
• Have you evaluated the potential impact of migrating from Kettle to Apache Hop on your existing projects and workflows?
• What role does compatibility with legacy systems play in your decision-making process?

For additional insights into the steps and considerations for migrating to Apache Hop, you can review our article: Breaking Free from Kettle/PDI: Your Transition to Apache Hop.

Conclusion

After examining the various aspects of Hop and Kettle, it's evident that while both tools share similarities in their core functionalities such as pipelines (transformations) and workflow (jobs) management, they also exhibit notable differences in terms of project organization, runtime engine flexibility and support. 

• Project management: If your project demands a structured approach with clear lifecycle management and configuration control, Hop is the way to go. Its project and environment setup provides better organization and control over configuration settings.
• Support and updates: One crucial factor to consider when choosing between Hop and Kettle is the level of support and updates provided for each tool. Here's how they compare in terms of support and ongoing development:
  • Apache Hop: As a community-driven project with an active open-source community, Hop benefits from continuous updates, bug fixes, and feature enhancements contributed by developers worldwide. The collaborative nature of Hop's development ensures that issues are addressed promptly, and new features are regularly introduced to meet evolving user needs. Additionally, being part of the Apache Software Foundation ensures a robust governance model and long-term support for the project.
  • Kettle: While Kettle has been a widely-used data integration tool for many years, it is no longer actively maintained and supported. While some legacy support may still be available, users may encounter limitations in terms of updates, bug fixes, and compatibility with newer technologies. Organizations relying on Kettle for critical workflows may face challenges in accessing timely support and ensuring the longevity of their data integration solutions.
• Runtime Engine Flexibility: Hop stands out with its pluggable runtime engines, enabling users to choose the most suitable engine for their specific requirements, a feature absent in Kettle.

Resources

1. Apache Hop Fundamentals Course: Discover the fundamentals of Apache Hop with our online course, covering essential concepts, features, and practical applications.

2. Datavin3 on LinkedIn: Follow our LinkedIn page for new posts, tutorials and announcements about upcoming events or courses.

3. Apache Hop Documentation: Explore the official documentation for Apache Hop to learn about its architecture, components, and usage guidelines.

4. Mattermost Chat Server: Engage with the Apache Hop community in real-time on the Mattermost chat server, where you can chat with developers, share insights, and get support.

5. Apache Hop Mailing Lists: Through these mailing lists, you'll receive updates on project developments, feature announcements, and discussions on various topics related to Apache Hop.