How to load data from Lokalise to BigQuery

Learn how to use Airbyte to synchronize your Lokalise data into BigQuery within minutes.

Try Airbyte Cloud Manual method (the hard way)

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Bespoke pipelines are:

Inconsistent and inaccurate data
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Furthermore, you will need to build and maintain Y x Z pipelines with Y sources and Z destinations to cover all your needs.

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Start syncing with Airbyte in 3 easy steps within 10 minutes

Set up a Lokalise connector in Airbyte

Connect to or one of 400+ pre-built or 10,000+ custom connectors through simple account authentication.

Set up BigQuery for your extracted Lokalise data

Select where you want to import data from your source to. You can also choose other cloud data warehouses, databases, data lakes, vector databases, or any other supported Airbyte destinations.

Configure the Lokalise to BigQuery in Airbyte

This includes selecting the data you want to extract - streams and columns -, the sync frequency, where in the destination you want that data to be loaded.

Try Airbyte Cloud Manual method (the hard way)

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Check out our interactive demo and our how-to videos to learn how you can sync data from any source to any destination.

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Try Airbyte Cloud Manual method (the hard way)

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How to Sync to Manually

Step 1: Export Data from Lokalise

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.

Step 2: Prepare Exported Data

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.

Step 3: Set Up Google Cloud Platform

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.

Step 4: Create a BigQuery Dataset

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.

Step 5: Format Lokalise Data for BigQuery

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.

Step 6: Upload Data to Google Cloud Storage

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.

Step 7: Load Data into BigQuery

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.