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Begin by exporting the data you need from Lokalise. Log into your Lokalise account, navigate to the project containing the data you want to export, and use the export feature. Choose the appropriate file format (e.g., JSON or CSV) that suits your needs and download the file to your local machine.
If you haven't already, set up a Google Cloud Platform account. Navigate to the Google Cloud Console, create a new project, and enable billing. Make sure you have sufficient permissions to create and manage Pub/Sub resources.
Within the Google Cloud Console, go to the Pub/Sub section and create a new topic. This topic will serve as the destination for your data. Name the topic appropriately to reflect the content or purpose of the data you are transferring from Lokalise.
Install the Google Cloud SDK on your local machine. This SDK will allow you to interact with your Google Cloud resources from the command line. Follow the official installation guide for your operating system (Windows, macOS, or Linux) and ensure that you authenticate your account using `gcloud auth login`.
Convert or format the exported Lokalise data into a format suitable for Pub/Sub. If your data is in JSON, ensure it is properly structured. You may need to write a simple script (in Python, Node.js, etc.) to read the data file and prepare each piece of data as a message for Pub/Sub.
Use the Google Cloud SDK or client libraries to publish the prepared messages to your Pub/Sub topic. In your script, utilize the Pub/Sub client library appropriate for your programming language to connect to the topic and publish each piece of data as a message. Ensure that each message is successfully published by handling any exceptions or errors that may occur during the process.
After publishing, verify that the data has been successfully transferred to Google Pub/Sub. You can do this by subscribing to the topic using a Pub/Sub subscription and checking if the messages are received correctly. Alternatively, use the Google Cloud Console to inspect the messages in the topic to ensure that the data from Lokalise has been accurately moved.
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:
