Breaking Free from Kettle/PDI: Your Transition to Apache Hop

Embrace the leap to Apache Hop from Kettle/PDI seamlessly. Unlock the potential of data integration with expert insights and a smooth transition guide.

Introduction

Apache Hop is an open-source data integration platform that is designed to help organizations build data pipelines for their business needs. It is built on the foundation of the popular data integration tool, Kettle/PDI.

Migrating from Kettle/PDI to Apache Hop is a relatively straightforward process, although it can be time-consuming, depending on the complexity of the existing data integration code. Here are some steps that you can follow to make the transition as smooth as possible:

1. Evaluate your existing Kettle/PDI code: The first step is to review your current data integration transformations to understand their complexity, dependencies, and overall design. This will help you identify any potential challenges that you may face during the migration process.

2. Install Apache Hop: Once you have evaluated your existing pipelines, the next step is to download and install Apache Hop on your system. You can download Apache Hop from the official website, and installation instructions are available in the documentation.

3. Convert Kettle/PDI transformations to Hop pipelines: The next step is to convert your existing Kettle/PDI transformations to Hop pipelines and the jobs to Hop workflows. This can be done using the Hop Import tool, which is included in the Apache Hop installation.

4. Check possible issues: Migrating from one platform or tool to another can be a daunting task, especially when it comes to complex systems. While migration offers many benefits, such as access to new features and improved performance, it can also introduce a range of issues that need to be resolved. Some of the common migration issues include compatibility issues, data loss, incorrect data mapping, performance issues, and system downtime. It's important to identify these potential issues and plan for them to ensure a smooth migration process. By understanding the potential issues, you can take steps to minimize their impact and successfully transition to your new platform or tool.

5. Verify Hop pipelines/workflows: After converting your existing code to Apache Hop, you should verify that they work as expected. This can be done by running test cases and comparing the results with the original Kettle/PDI code.

6. Optimize Hop pipelines/workflows: Once you have verified that your Hop pipelines/workflows are working correctly, you can start optimizing them to take advantage of the features and capabilities Apache Hop offers. This may involve changing the design of your pipelines/workflows to use new Hop components or taking advantage of new features like data lineage tracking.

In conclusion, migrating from Kettle/PDI to Apache Hop is a relatively straightforward process that requires careful planning and execution. By following these steps, you can ensure that your transition to Apache Hop is smooth and successful.

Evaluate your existing Kettle/PDI transformations

In Kettle/PDI, the term used for a data integration flow is "transformation", while in Apache Hop it is "pipeline". When migrating from Kettle/PDI to Apache Hop, it's important to note that transformations will be converted to pipelines and jobs will be converted to workflows.

So, when evaluating your existing Kettle/PDI transformations, you will need to review the following aspects:

1. Project execution: This includes identifying the different components, their order of execution, and how they are interconnected. Knowing or checking the structure of your project is essential for identifying any potential challenges that you may face during the migration process. For example, having the order of execution of the main jobs/transformations or a priority list to be migrated, depending on the size of the project.

2. Data sources and destinations: Your transformations may be reading data from, or writing data to, specific data sources or destinations. Understanding the data sources and destinations used by your transformations will help you identify any necessary changes required during the migration process. It's important to consider that your existing Kettle/PDI project may contain duplicate or unused database connections. This is a good opportunity to remove them and tidy up your project.

3. Business rules: Your transformations may contain business rules that govern how data is transformed and processed. Knowing/understanding these rules will help you identify any necessary changes required to ensure that the same business logic is applied in the new environment and verify the needed changes. For example, depending on the project environment (development, testing, etc) you may need to use different configurations for the notification emails.

4. Performance: Finally, it's important to evaluate the performance of your existing transformations. This includes identifying any bottlenecks, tuning parameters, and other factors that impact performance. Knowing the performance characteristics of your transformations is essential for ensuring that the migration does not negatively impact overall system performance.

By thoroughly evaluating your existing Kettle/PDI code, you can identify potential challenges and develop a comprehensive migration plan considering all the necessary factors. This will help ensure that your transition to Apache Hop is smooth and successful.

Install Apache Hop

Installing Apache Hop is a relatively straightforward process. Here are the general steps to follow:

1. Download Apache Hop: The first step is to download the Apache Hop distribution package from the official Apache Hop website (Download). Select the version that is appropriate for your operating system and download the package to your local system.

2. Extract the package: Once the download is complete, extract the package to a directory of your choice. This can be done using any standard file compression utility.

3. Set up Java: Apache Hop requires Java to run. If you do not already have Java installed on your system, you will need to download and install it before proceeding. Make sure that you have the appropriate version of Java installed, as specified in the Apache Hop documentation.

4. Launch Apache Hop: Once the environment variables are configured, you can launch Apache Hop by running the hop.sh (on Unix-based systems) or hop.bat (on Windows) script. This will start the Apache Hop GUI, which you can use to create and manage your data integration pipelines.

By following these steps, you should be able to install Apache Hop on your system and start using it to create and manage your data integration pipelines.

Convert Kettle/PDI code to Apache Hop code

Converting Kettle/PDI transformations/jobs to Apache Hop pipelines/workflows requires careful planning and execution to ensure that the migrated code functions correctly. Here are the general steps to follow:

Migrate the code

1. Clone/backup your Kettle/PDI project: Make a backup of your Kettle/PDI project directory, including all files and folders. It's important to consider that you may require access to input/output/config files outside of your project code. It's recommended to verify that you have them or have appropriate access to them.

2. Open the Hop Import wizard: Open the Hop Import wizard by selecting "Hop" > "Import from Kettle/PDI" from the Hop menu.

   

3. Select the Kettle/PDI project directory: In the Import code to Hop view, select the Kettle/PDI project directory that you want to import.

4. Map the Kettle/PDI repository to an Apache Hop project: You can create a new Hop project or select an existing one. You can also import to a folder instead of to a Hop project.

5. Select the kettle.properties file from your Kettle/PDI project. This will import your project variables.

6. Select the shared.xml: It's possible to extract relational database connections as Hop relational database connection metadata objects.

7. Find the jdbc.properties: With this option, you can convert JNDI (simple-jndi) relational database connections into Hop relational database connection metadata objects. JNDI connections are not supported in Apache Hop. The JNDI functionality in Kettle/PDI relies on an open-source project that hasn't received updates in approximately ten years. Since there was no justification for retaining this feature in Apache Hop, it was discontinued.

8. You can skip existing target files, hidden files and folders, and folders in the source.

9. Keep the project config file name generated by default or specify a name.

10. You can select a Pipeline Run COnfiguration and a Workflow Run Configuration to be used by default.

11. Validate the import: Once the mapping is complete, validate the import to ensure that everything has been imported correctly. Within a few seconds, even when importing large projects, you'll receive a migration summary. This allows you to verify the number and type of migrated files.

Create an environment

Once you have completed the code migration, you can create an environment for your project or multiple environments as needed. This will enable you to manage the necessary metadata configurations and variable values to ensure that the project runs correctly in a specific environment.

You can add, for example, an environment-specific configuration file, and a config file per connection or metadata object you need in your environment.

Check possible issues

Migrating from one data integration tool to another can be a challenging process, and it's important to anticipate and address any potential issues that may arise. During the migration process, it's common to encounter problems such as incompatible data types, versioning conflicts, performance issues, and other unexpected errors. In addition, there may be legacy code or outdated practices that need to be refactored or replaced. Identifying and addressing these issues early on in the migration process can save time and resources down the line, and ensure a smooth transition to the new platform.

Check the connections

Although migrating to Hop is not a complete solution for all the issues that your current Kettle/PDI project may have (you are migrating what you have), it is an opportune moment to verify some common problems such as the presence of duplicate connections, repeated code, performance issues, underutilization of variables and parameters.

Check your connections, verify the connectivity, and the needed drivers, use variables for the credentials, and add environment files for your connections.

Update transforms

You may need to update pipelines to use different transforms in some cases. For example, the Pentaho Reporting Output is not a transform in Apache Hop, so you will need to use another transform in your new project.

Another check you may need is for any Pentaho-specific Java Script code/function. Just change it to the Apache Hop code.

Verify hardcoded paths

After migrating, having hardcoded paths in the Kettle project can be another issue that needs to be addressed. Although it's considered a bad practice, if you have used any hardcoded paths in your old project, it's something that you'll likely need to verify. You can perform a code search or execute the code and check for errors or incorrect data.

It's important to note that the exact process may vary depending on the complexity of your pipelines and the specific requirements of your data integration tasks. It's also a good idea to consult the Apache Hop documentation and community forums for additional guidance and support.

Verify Hop pipelines/workflows

After converting Kettle/PDI pipelines to Apache Hop, it's important to verify or test the converted pipelines and workflows to ensure that they function correctly. This involves testing the pipelines and workflows execution, verifying the data flow, and checking the accuracy of the output data.

1. Check for errors: First, check the logs and error messages generated by the pipeline or workflow to ensure that there are no errors or warnings. Address any issues that are identified.

2. Validate the data: Validate the data output to ensure that it matches the expected results. Use tools such as data profiling and data validation to ensure that the data is accurate and consistent.

3. Test the pipelines and workflows end-to-end: Test the pipelines and workflows end-to-end to ensure that it performs as expected. This may involve testing individual steps and components, as well as testing the pipeline or workflow as a whole.

4. Verify the connections and settings: Verify that the connections and other system settings are correct and functioning properly. Ensure that the pipeline or workflow is configured to use the correct connections and settings.

5. Perform load testing: Perform load testing to ensure that it can handle large volumes of data and workloads.

6. Implement unit testing: Apache Hop's testing framework provides several benefits, including the ability to automate testing, run tests as part of a continuous integration process, and catch errors early in the development cycle. Additionally, unit tests provide a safety net when making changes to existing code, as they help to ensure that the changes do not break existing functionality. To create a unit test, you start by defining the expected results for a particular pipeline. You then create a test case that runs the pipeline and compares the actual results to the expected results. If the actual results match the expected results, the test case passes; otherwise, it fails.

Optimize Hop pipelines

Optimizing Hop pipelines can help to improve their performance and efficiency, and ensure that they can handle large volumes of data and workloads. This is not part of the migration but after finishing, it's useful to take the time to improve your ETL code. Here are some strategies for optimizing Hop pipelines:

Simplify the code

Simplifying the code can greatly enhance the performance and reduce the overall complexity of the pipeline. It involves the removal of unnecessary transforms and actions in workflows or the replacement of transforms or actions for better performance. By doing so, you can streamline the pipeline and make it more efficient. This can also help in improving the readability and maintainability of the code. So, it's always a good practice to review your pipeline and remove any redundant transforms and actions.

Optimize transforms

Optimize individual transforms in the pipeline by adjusting their configurations and settings. For example, you can adjust the batch sizes, buffer sizes, and other performance settings to improve the efficiency of the step.

Use variables

Variables offer a convenient solution to avoid hard-coding various settings within your system, environment, or project. Place environment-specific configurations in a dedicated environment configuration file.

Create an environment within Apache Hop for this purpose and when referring to file locations, use ${PROJECT_HOME} instead of expressions such as ${Internal.Entry.Current.Directory} or ${Internal.Pipeline.Filename.Directory}.

Logging and monitoring

Take the time to capture the logging of your workflows and pipelines in Apache Hop. It's essential to have a trace of every run so that when things go wrong unexpectedly, you can easily identify what happened. You can refer to the Logging Basics for further information or opt for logging into a Neo4j graph database.

Monitor the performance of the pipeline and tune its settings as needed. Use tools such as performance monitoring and profiling to identify bottlenecks and other issues, and adjust the settings accordingly.

Modular code

Consider using Metadata Injection to create reusable pipeline templates if you need to create similar pipelines frequently. This approach eliminates the need for a manual population of dialogs and supports dynamic ETL and data streaming. You can also use Pipeline and Workflow executors.

To loop over a set of values, rows, or files, one of the easiest ways is to use an Executor transform in Hop. The Pipeline Executor can be used to run a pipeline for each input row, while the Workflow Executor can be used to run a workflow for each input row. With this approach, it is easy to map field values to the parameters of the pipeline or workflow, making loops a breeze. 

By optimizing your Hop pipelines, you can ensure that they can handle the demands of your data processing workflows and deliver fast and efficient results.

Conclusion

In conclusion, migrating from Kettle/PDI to Apache Hop requires some planning and execution to ensure a smooth transition but it's quite simple. The first step is to evaluate your existing Kettle/PDI code and identify any compatibility issues that may arise. Then, you can install Apache Hop and convert your Kettle/PDI pipelines to Hop pipelines. It's important to verify and test the new pipelines and workflows to ensure that they function correctly. Finally, you may need to migrate data from the old system to the new system. By following these steps and optimizing your Hop pipelines, you can ensure that your data processing workflows are efficient and effective in the new Apache Hop environment.

Some potential next steps after completing the migration from Kettle/PDI to Apache Hop:

1. Train and onboard users: Train and onboard users on the new Apache Hop system. This may involve providing training materials and conducting training sessions to ensure that users are comfortable with the new system.

2. Update documentation: Update any relevant documentation, including user manuals, technical documentation, and other materials, to reflect the new Apache Hop system.

3. Monitor and optimize performance: Monitor the performance of the new system and optimize it as needed. This may involve adjusting pipeline configurations, optimizing database settings, or fine-tuning other system components.

4. Plan for future upgrades: Plan for future upgrades and enhancements to the Apache Hop system, including new features and capabilities that may be added over time.

5. Share feedback with the Hop community: Share your feedback and experiences with the Apache Hop community to help improve the system and its features for other users.