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Begin by exporting your data from Lokalise. Log into your Lokalise account and navigate to the project you wish to export. Use the export feature to download your data in a suitable format such as CSV or JSON, which is commonly supported for this purpose. Make sure to choose the export options that best fit the structure you need for your MSSQL database.
Once you have exported the data, inspect the file(s) to ensure that all necessary information is present and correctly formatted. You may need to clean the data or adjust the format to ensure compatibility with MSSQL. For example, if exporting to CSV, ensure that delimiters are consistent and that there are no unexpected characters or empty fields that could cause issues during import.
Before importing, ensure that your MSSQL database is ready to receive the data. Create a new database if necessary, and set up the appropriate tables with the correct schema to match the data structure of your Lokalise export. Define columns, data types, and any necessary constraints or indices. This step ensures that your data will fit perfectly into the database.
If you haven't already, install SQL Server Management Studio (SSMS) on your machine. This tool will facilitate the import process by providing a user-friendly interface to manage your MSSQL databases. Download SSMS from the Microsoft website and follow the installation instructions.
Open SSMS and connect to your MSSQL server. Right-click on your target database and select "Tasks" > "Import Data" to open the SQL Server Import and Export Wizard. Choose your Lokalise export file as the data source"�this could be a flat file source if using a CSV format. Configure any necessary settings, such as delimiters and text qualifiers, to ensure that the import process reads your data correctly.
In the Import Wizard, carefully map the columns from your Lokalise export file to the corresponding columns in your MSSQL target table. This step is crucial to ensure that data lands in the correct fields without errors. Double-check data types and ensure that any necessary transformations are handled during this process.
Once mapping is complete, execute the import process. Monitor the progress for any errors or issues, and once completed, verify the integrity of the data. Run queries in SSMS to check that all records have been imported correctly and that there are no discrepancies. Ensure that the data in MSSQL aligns with your expectations and the original data from Lokalise.
By following these steps, you can successfully transfer data from Lokalise to an MSSQL destination manually, ensuring complete control over the data migration 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:
