[ Talend Best practices ] In the training, we learned to build simple integration processes, master and standalone processes, we learned about context variables and learned how to use objects in the repository. It’s time to learn about the best practices for building data flows in Talend Data Integration, which will facilitate the work of you and others when analyzing already prepared ETL processes.
Table of Contents
- Design efficient data flows: When designing data flows in Talend, it is important to consider the overall performance and scalability of the process. This can be achieved by minimizing the number of data processing steps, optimizing data transformation logic, and using appropriate connectors and components for the data sources and targets.
- Use tMap efficiently: tMap is a powerful component in Talend that allows users to perform various data transformations and manipulations. To maximize its efficiency, it is recommended to use tMap’s built-in functions and expressions, rather than implementing complex logic in separate Java code.
- Use parallel processing: Talend allows users to leverage the power of multi-core processors and distributed computing to speed up data processing. By using parallel processing, users can significantly improve the performance of their data integration jobs.
- Use appropriate connectors: Talend provides a wide range of connectors for different data sources and targets. It is important to choose the right connector for the specific data source or target to ensure optimal performance and compatibility.
- Test and optimize: It is important to test and optimize data integration jobs in Talend to ensure that they are functioning correctly and efficiently. This can be done by using Talend’s built-in debugging and profiling tools, as well as by testing the jobs on sample data sets and fine-tuning their settings and parameters as needed.
In accordance with Talend best practices, all data flows within the process should be built from left to right and from top to bottom.
Name your components
The component with the name tDBInput_1 is really fine, but for a beginner developer who built his first process quickly and still has no idea where he could change the name. To make the process more readable and easier to analyze for others, name the components in a way that describes task, but also is short.
Use context variables
If your process depends on external data, always store it as context variables. Keep file paths or names as variables instead of leaving them hard-coded. Remember to make your processes flexible and easy to modify in the case of changes.
Order in the repository
If you use Talend DI only for your own use, you probably won’t feel a big mess in the repository. However, imagine what it would look like if you worked on several projects, and all processes would be thrown under the Job Designs tab – finding a specific process certainly would not be the fastest and easiest tasks. So remember to put processes in dedicated project folders. In addition, you can ensure greater order by giving processes the appropriate names, e.g. 100 – processes loading the stage data layer, 200 – CDC, 600 – test processes, and master processes as 900.
Don’t forget about the documentation
The last good practice I would like to share with you is documentation. Remember to make a brief annotation after creating the data flow process using the tNote component with basic information, e.g. the author or date the process was created. You can also add information about individual components through the record in the tab Component -> Documentation.
When creating each job, only its name is required – but remember that providing the purpose of its creation and description will facilitate its later analysis (Talend Best practices).
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