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Log into your Lokalise account and navigate to the project you wish to export. Go to the "Download" section, where you can export your translations. Select JSON as the file format and configure any additional settings like platform, placeholder formats, or filters. Then, click "Build" to generate the downloadable files.
Once the export process is complete, a download link will be provided. Click on this link to download the JSON file to your local machine. Ensure the file has downloaded correctly and is not corrupted.
Decide on a directory structure for your local files. Create a dedicated folder where you will store the JSON file. This step is crucial for keeping your project organized and ensuring that you can easily reference the translations in your application.
Use a text editor or a code editor like VSCode or Sublime Text to open the JSON file. Review the contents to ensure that the translations and keys have been exported as expected. This is important to verify that no data loss occurred during the export process.
Depending on your application�s requirements, you may need to modify the JSON structure or format. For example, you might need to change key names, adjust nesting levels, or add additional metadata. Make any necessary changes and save the file.
Use a JSON validator tool to check the file for syntax errors. This step is essential to ensure the JSON format is correct and will not cause runtime errors in your application. Correct any errors that the validator identifies.
Finally, integrate the JSON file into your application. This involves referencing the file in your codebase and loading the translations into your application as needed. Ensure that your application correctly parses the JSON and displays the translations as intended.
By following these steps, you can successfully transfer data from Lokalise to a local JSON file manually, maintaining control over the data handling process without relying on third-party tools.
FAQs
What is ETL?
ETL, an acronym for Extract, Transform, Load, is a vital data integration process. It involves extracting data from diverse sources, transforming it into a usable format, and loading it into a database, data warehouse or data lake. This process enables meaningful data analysis, enhancing business intelligence.
Using Lokalise, you can manage your localizations in an easy, affordable, and modern way. It is a cloud-based system that allows you to manage localizations and translations efficiently. Especially when utilizing the continuous localization capabilities, it makes your website, app, game, or any other project global, vibrant, and engaging. The tool localise belongs to the Translation Service category. You need a platform that brings together all stakeholders and processes to make localization successful.
Lokalise's API provides access to a wide range of data related to localization and translation management. The following are the categories of data that can be accessed through Lokalise's API:
1. Projects: Information related to the projects created in Lokalise, including project ID, name, description, and project settings.
2. Keys: Data related to the keys used in the localization process, including key ID, name, description, and translation status.
3. Translations: Information related to the translations of the keys, including translation ID, language, and translation text.
4. Teams: Data related to the teams working on the localization projects, including team ID, name, and team members.
5. Files: Information related to the files used in the localization process, including file ID, name, and file format.
6. Comments: Data related to the comments made on the keys and translations, including comment ID, author, and comment text.
7. Tags: Information related to the tags used to categorize the keys and translations, including tag ID, name, and tag color.
Overall, Lokalise's API provides comprehensive access to the data required for efficient localization and translation management.
What is ELT?
ELT, standing for Extract, Load, Transform, is a modern take on the traditional ETL data integration process. In ELT, data is first extracted from various sources, loaded directly into a data warehouse, and then transformed. This approach enhances data processing speed, analytical flexibility and autonomy.
Difference between ETL and ELT?
ETL and ELT are critical data integration strategies with key differences. ETL (Extract, Transform, Load) transforms data before loading, ideal for structured data. In contrast, ELT (Extract, Load, Transform) loads data before transformation, perfect for processing large, diverse data sets in modern data warehouses. ELT is becoming the new standard as it offers a lot more flexibility and autonomy to data analysts.
What should you do next?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey: