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Begin by logging into your Lokalise account. Navigate to the project from which you want to export data. Use Lokalise’s export feature to download your data in a compatible format, such as JSON, CSV, or another format that meets your needs. Ensure that you structure your export to include only the necessary data fields required for your project in Convex.
Once you have your exported file, open it to review the content. Ensure that the data is correctly formatted and does not contain any errors. Clean up any unnecessary data fields that Convex will not use. This might involve using a text editor or spreadsheet software to adjust the file format to make it easier for import into Convex.
Before importing the data into Convex, map the data fields from the Lokalise export to the corresponding fields in Convex. Create a mapping document to identify how each field in your Lokalise data corresponds to fields in Convex. This step is crucial for ensuring that your data is correctly aligned during the import process.
Log into your Convex account and navigate to the database section where you intend to import the data. Ensure that you have the necessary permissions to add or modify data within the Convex database. Familiarize yourself with the database schema to ensure compatibility with the data from Lokalise.
Depending on the requirements of the Convex database, you may need to transform the data from the Lokalise export. This can involve converting data types, adjusting field names to match the Convex schema, or restructuring the data to fit the format that Convex expects. Use a programming language or script that you’re comfortable with to automate this transformation process if needed.
With the data prepared, use Convex’s import feature to upload your transformed data file. Follow the instructions provided by Convex to ensure the data is imported correctly. Pay attention to any error messages or warnings during the import process, and be prepared to make adjustments if the data does not import as expected.
After importing the data, perform a thorough check to ensure that all data has been transferred correctly. Compare key data points between the Lokalise export and the Convex database to verify accuracy. Look for any discrepancies or missing data, and make necessary corrections. It’s also a good practice to test the functionality of the imported data within Convex to ensure everything operates as expected.
By following these steps, you can manually move data from Lokalise to Convex 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:
