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Begin by exporting your data from Lokalise. Log into your Lokalise account, navigate to the project that contains the data you wish to export, and select the export feature. Choose a format that is suitable for manual data handling, such as CSV or JSON, ensuring that it includes all necessary fields for your analysis.
Once you have your data exported from Lokalise, review it to ensure that it is complete and formatted correctly. Clean up any inconsistencies or errors in the data. If necessary, use a text editor or spreadsheet software to make adjustments. Make sure the data structure aligns with what you plan to import into BigQuery.
Access Google Cloud Platform (GCP) and set up a new project if you haven't already. Ensure that BigQuery API is enabled for your project. You can do this by navigating to the API library in the GCP console and searching for BigQuery API, then enabling it.
In the BigQuery console, create a new dataset where your Lokalise data will be stored. Click on the "Create Dataset" button, and fill in the required information such as Dataset ID, Data Location, and any other settings that fit your data storage needs.
Depending on the format you exported from Lokalise, you might need to further transform the data into a format that BigQuery accepts. For CSV files, ensure that they comply with BigQuery’s requirements on column names and data types. For JSON files, ensure that the JSON structure is flat or properly nested as needed.
Before importing the data into BigQuery, upload the prepared file(s) to Google Cloud Storage (GCS). Navigate to the GCS console, create a new bucket if needed, and upload your files. Make sure that you set the appropriate permissions so that BigQuery can access the files.
Finally, import your data from GCS into BigQuery. In the BigQuery console, use the "Create Table" function and choose "Google Cloud Storage" as the source. Select your dataset and specify the source file within GCS. Configure the schema if necessary, and then execute the data load operation. Monitor the process for any errors or issues, and verify the data once the import is complete.
By following these steps, you can systematically move your data from Lokalise to BigQuery without relying on third-party connectors or integrations.
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?
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