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Begin by obtaining API access from Lokalise. Log in to your Lokalise account and navigate to the API tokens section. Generate a new API token, ensuring it has the necessary permissions to access the data you want to move. Note the token for use in subsequent steps.
Plan the logic to extract data from Lokalise. Identify the specific data you need, such as translations or project details. Use Lokalise API documentation to understand the endpoints available and how to structure your API requests to retrieve the necessary data.
Write a script in a programming language of your choice (e.g., Python, Java) to connect to Lokalise using the API token. Use HTTP requests to fetch the data. Parse the JSON response to extract the relevant information. This script should be able to run independently and be scheduled if regular updates are needed.
Data extracted from Lokalise may need transformation to fit Kafka's message format. Define the structure of your Kafka messages, typically in JSON or Avro format. Modify your extraction script to transform the Lokalise data into this format, ensuring that the keys and values meet the schema you plan to use in Kafka.
Install and configure Apache Kafka on your local machine or server. Ensure Kafka is running properly by starting the Kafka server and ensuring the required topics are created. Use the Kafka command-line tools to create topics that will receive data from Lokalise.
Develop a Kafka producer script using a Kafka client library compatible with your chosen programming language. This script should read the transformed data and send it to the appropriate Kafka topic. Handle errors and retries to ensure data is sent successfully.
Conduct thorough testing to ensure data is correctly extracted from Lokalise, transformed, and sent to Kafka. Use sample data to verify each step of the process. Once testing is successful, deploy the solution. Set up a cron job or similar scheduling tool to automate the process if ongoing data transfer is required. Monitor the solution for any issues and adjust as needed.
By following these steps, you can efficiently move data from Lokalise to Kafka without relying on third-party connectors.
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:
