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Begin by exporting the desired data from Lokalise. Navigate to the project from which you want to extract data, and use Lokalise's export feature to download the data as a CSV or JSON file. Ensure that you select the necessary keys, languages, and formats during the export process to align with your data requirements.
Set up your local environment to handle the data transfer process. Verify that Python or a similar scripting language is installed, as it will be used to process and load data. Ensure you have access to the exported file from Lokalise and adequate permissions to execute scripts and access Snowflake.
Install the SnowSQL command-line interface (CLI), which is necessary to interact with Snowflake. Follow the official Snowflake documentation to download and configure SnowSQL on your local machine. Use your Snowflake account credentials to set up the initial configuration.
Use a script to transform the Lokalise data into a format compatible with Snowflake's table structure. This may involve cleaning the data, modifying data types, or restructuring JSON data into tabular form. Python's Pandas library is particularly useful for such transformations.
Log into your Snowflake account using SnowSQL and create a table to store the Lokalise data. Use the `CREATE TABLE` statement to define the structure of the table, ensuring that it matches the schema of your transformed data. Specify the correct data types and constraints for each column.
Use the `PUT` command in SnowSQL to upload the transformed data file to a Snowflake stage, which is a temporary storage location. This step involves transferring the file from your local machine to the Snowflake environment, where it can be accessed for loading into your table.
Execute the `COPY INTO` command to load the data from the Snowflake stage into your newly created table. This command will import the data, respecting the table schema and any transformations applied during the data preparation step. Verify the results by querying the table to ensure data accuracy and completeness.
By following these steps, you can efficiently transfer data from Lokalise to the Snowflake Data Cloud manually, 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?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:
