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Begin by logging into your Lokalise account. Navigate to the project dashboard and select the project you wish to export data from. Use the export feature to download your data in a supported format such as CSV, JSON, or XLSX. Ensure you choose a format that is easiest for you to manipulate and compatible with your data processing tools.
Once you have your exported file, open it in a data manipulation tool like Excel or a text editor. Review the structure of the data to understand its format and contents. Identify any fields or data points that need modification or renaming to align with the schema requirements of Weaviate. This step is crucial for a smooth transformation process.
Weaviate uses JSON-LD as its data format. Use a scripting language like Python to write a script that converts your Lokalise data into JSON-LD. This involves mapping Lokalise fields to the appropriate JSON-LD structure and adding any necessary context. Ensure that your JSON-LD includes all required attributes for your Weaviate schema, such as class names and properties.
Before importing data into Weaviate, you need to set up a schema that defines the classes and properties your data will map to. Log into your Weaviate instance and access the schema management interface. Define classes and properties according to your data structure, ensuring alignment with the JSON-LD you prepared. This step ensures Weaviate can correctly interpret and store your data.
With your JSON-LD data and Weaviate schema prepared, you can now load the data into Weaviate. Use the Weaviate REST API to send POST requests that upload your JSON-LD data. Ensure you handle authentication correctly and verify that your requests are formatted according to Weaviate’s API documentation. Check for successful responses to confirm data import.
After importing your data, it's crucial to verify that it was correctly integrated into Weaviate. Use the Weaviate console or API to query your data classes and properties. Check for consistency and accuracy against your original Lokalise data. This step helps ensure that no data was lost or misrepresented during the transfer.
Establish a routine for maintaining and updating your data in Weaviate. This could involve setting up scripts to periodically extract and transform new Lokalise data, followed by importing it into Weaviate. Regular updates help keep your Weaviate database current and reliable, especially if your Lokalise data is frequently updated.
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:
