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Begin by exporting your data from Lokalise. Log into your Lokalise account, navigate to the project you want to export, and choose the format for export, such as JSON, CSV, or any other format supported by Lokalise. Ensure you include all necessary fields and translations in the export.
Once you have the exported file, review the data to ensure its completeness and correctness. If necessary, clean and transform the data to match the structure required by Typesense. This might involve reformatting dates, handling special characters, or converting data types.
If you haven't already, set up a Typesense server. You can do this by downloading the latest version of Typesense from its official website and following the installation guides for your specific operating system. Ensure that the server is running and accessible.
Define the schema for your data in Typesense. The schema should include fields that match the data structure from Lokalise, specifying field names, types, and any search-specific configuration like faceting or filtering options. Use the Typesense API to create this schema.
Create a script to automate the data import process. Use a programming language like Python, which has libraries for handling both JSON and HTTP requests. The script should read the exported data file, format it according to the Typesense schema, and send it to the Typesense server using its API.
Run your data migration script to import the data into Typesense. Ensure the script handles errors gracefully and can resume from where it left off in case of interruptions. Verify that all data entries are correctly indexed in Typesense by querying the data post-import.
After import, check that all data is correctly imported and that search functionality works as expected. Conduct sample searches and verify that results are accurate and complete. You may need to adjust your schema or re-import data if issues are found.
By following these steps, you can successfully transfer data from Lokalise to Typesense without relying on third-party connectors or integrations.
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:
