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Begin by exporting the data from Lokalise in a suitable format such as CSV or JSON. Log in to your Lokalise account, navigate to the project that contains the data you want to export, and use the export feature to download the files to your local system. Ensure that you choose a format that is compatible with AWS Glue.
Log into your AWS account and navigate to the S3 service. Create a new bucket where you will store the Lokalise data files. Choose a unique name for the bucket and configure the necessary permissions to ensure only authorized users have access.
Upload the exported Lokalise files to the newly created S3 bucket. You can do this using the AWS Management Console by navigating to your bucket and using the "Upload" feature, or by using the AWS CLI with the `aws s3 cp` command. Ensure that the files are placed in the correct directory structure if you plan to use specific S3 prefixes.
Set up an AWS Glue Crawler to catalog the data stored in your S3 bucket. Go to the AWS Glue console and create a new crawler. Define the data source as your S3 bucket and specify the IAM role with the necessary permissions. Configure the crawler to run on demand or on a schedule as needed.
Execute the AWS Glue Crawler to automatically discover the schema and create a table in the AWS Glue Data Catalog. This step is crucial for AWS Glue to understand the structure of your data and make it available for ETL operations.
In the AWS Glue console, create a new ETL job. Define the source as the table generated by the crawler and specify the target destination (another S3 bucket or database). Configure the job script to transform the data if needed, using either the Glue provided code editor or by uploading a Python or Scala script.
Execute the AWS Glue ETL job to process the data. Monitor the job's progress through the AWS Glue console to ensure that it completes successfully. Check the output location to verify that the data has been transformed and stored as expected.
Following these steps will allow you to move data from Lokalise to AWS S3 and use AWS Glue for processing 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:
