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Begin by exporting the data you need from Lokalise. Log in to your Lokalise account and navigate to the project from which you want to extract data. Use the export feature to download the data in a compatible format, such as CSV or JSON, which can later be processed and transformed for Apache Iceberg. Ensure you have all necessary permissions to access and export the data.
Set up a local environment where you can transform the exported data into a format suitable for Apache Iceberg. Install necessary tools such as Python, Java, or any other programming language that you are comfortable with for processing data. Additionally, ensure that you have installed any required libraries or frameworks that can assist in data manipulation, such as Pandas for Python.
Load the exported Lokalise data into your local environment. Use a script to parse and transform the data into a columnar format such as Parquet or Avro, which are compatible with Apache Iceberg. This may involve converting data types, handling any necessary data cleaning, and restructuring the data to fit the schema you intend to use in Apache Iceberg.
Install and configure Apache Iceberg on your local machine or server. This involves setting up a compatible data processing engine such as Apache Spark or Flink that supports Iceberg. Ensure that your environment is properly configured to use Iceberg libraries and that you have created a database or table schema in Iceberg that matches the structure of your transformed data.
Use your data processing engine to load the transformed data files into the Apache Iceberg table. For example, if you are using Spark, you can write a Spark job that reads the Parquet or Avro files you created and writes them to the Iceberg table. Ensure that the data is correctly partitioned and written according to your Iceberg table schema.
Once the data is loaded, verify its integrity within the Apache Iceberg table. Run queries against the table to ensure that all data has been correctly transferred and transformed. Check for any discrepancies or errors in data types, missing fields, or unexpected values. This step is crucial to ensure that your data is accurate and useful.
After verifying the data, optimize the Iceberg table for performance. This may include actions like compacting small files, optimizing partitioning, and ensuring that metadata is up to date. Utilize available Iceberg utilities to improve query performance and storage efficiency. Regular maintenance and optimization will ensure that your Iceberg data lake performs well as it scales.
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:
