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First, log in to your Lokalise account and navigate to the project you want to export. Use the export feature within Lokalise to download your project data in a suitable format, such as JSON or CSV. Ensure you select the appropriate options to include all necessary data fields.
Once you have the data exported from Lokalise, review the format and structure. Ensure that you understand the data fields and how they correspond to the structure needed in Elasticsearch. This step is critical for ensuring a smooth transformation and import process.
Write a script or use a programming language like Python to transform your Lokalise data into a JSON format compatible with Elasticsearch. This involves restructuring the data fields and adding any necessary metadata required by Elasticsearch, such as document IDs or index types.
If you haven't already, set up an Elasticsearch cluster where the data will be stored. This can be done by installing Elasticsearch on a server or using a cloud-based service. Ensure that your cluster is accessible and that you have appropriate permissions to create indices and insert data.
Before importing data, create an index in Elasticsearch that will store your Lokalise data. Use the Elasticsearch API or Kibana to define the index's mappings, ensuring the fields match the structure of your transformed data. This step ensures that your data is categorized and searchable upon import.
Develop a script to automate the import of your transformed Lokalise data into Elasticsearch. Use a language like Python with libraries such as `elasticsearch-py` to interact with the Elasticsearch API. The script should read the transformed data and perform bulk insert operations into the designated index.
After importing the data, verify its integrity by querying Elasticsearch. Ensure that all records have been correctly imported and that the data is accessible and accurately searchable. Use tools like Kibana or Elasticsearch queries to check for any discrepancies or errors that may have occurred during the process.
By following these steps, you can successfully move data from Lokalise to Elasticsearch without using third-party connectors or integrations, ensuring that your data is correctly structured and accessible in your Elasticsearch environment.
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:
