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Begin by logging into your Lokalise account. Navigate to the project from which you want to export data. Use the export feature in Lokalise to download the data in a structured format such as JSON, CSV, or XML. Ensure that the data is saved to a secure location on your local machine or a server you control.
Set up your environment to use the AWS Command Line Interface (CLI). Install the AWS CLI on your machine if it's not already installed. Configure the AWS CLI with your AWS credentials by running `aws configure` and providing your AWS Access Key, Secret Access Key, region, and default output format when prompted.
Log into the AWS Management Console and navigate to Amazon S3. Create a new S3 bucket where you will temporarily store the exported Lokalise data. Choose a unique name for the bucket and configure permissions as necessary, ensuring the bucket is private to prevent unauthorized access.
Use the AWS CLI to upload your exported Lokalise data files to the S3 bucket. Use the command `aws s3 cp /path/to/local/file s3://your-bucket-name/` to upload each file. Double-check the files have been uploaded correctly by listing the contents of the S3 bucket using `aws s3 ls s3://your-bucket-name/`.
In the AWS Management Console, navigate to AWS Glue. Create a new Glue Data Catalog to define your dataset structure. Set up a new database and table in AWS Glue that matches the structure of your exported Lokalise data. This step ensures that the data is properly organized and queryable once loaded into the data lake.
From AWS Glue, create a new ETL (Extract, Transform, Load) job to load data from the S3 bucket into your data lake. Define the source as the S3 location and the target as your data lake storage. Configure any necessary transformations to ensure data consistency and compatibility. Run the ETL job to move the data into the data lake.
After the ETL job completes, verify the data has been correctly loaded into your AWS data lake. You can do this by querying the data lake using Amazon Athena. Ensure the data format is consistent and correct by running simple queries to check for expected data patterns and values. Adjust any configurations as necessary based on your findings.
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:
