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Begin by exporting your data from Lokalise. Navigate to the project you wish to export, and use the export feature to download the data in a CSV or JSON format. Make sure to select the appropriate language and file format that suits your needs.
Ensure that your local environment is set up to handle the files you exported from Lokalise. This means having a text editor or a spreadsheet application to view and organize your data if needed. Verify that you have Python installed, as it will be used to manipulate and insert data into DuckDB.
Install DuckDB on your system if it's not already installed. You can do this via pip by running the command `pip install duckdb`. DuckDB is a lightweight database management system that can run SQL queries on your local machine efficiently.
Open a terminal or command prompt and start a DuckDB session by typing `duckdb`. Create a new database file by using the command `CREATE DATABASE 'your_database.duckdb';`. This will initialize a new DuckDB database where you will import your Lokalise data.
Use SQL commands to define the table structure in DuckDB that matches the data format you exported from Lokalise. For example:
```sql
CREATE TABLE lokalise_data (
key VARCHAR,
translation VARCHAR,
context VARCHAR,
timestamp TIMESTAMP
);
```
Adjust the column names and types according to the data fields present in your Lokalise export.
Use Python to load the data from your exported file into DuckDB. You can do this by writing a script that reads the CSV or JSON file and inserts it into the DuckDB table. Here is a basic example using a CSV file:
```python
import duckdb
import pandas as pd
# Load CSV into a DataFrame
df = pd.read_csv('lokalise_export.csv')
# Connect to DuckDB and insert data
con = duckdb.connect('your_database.duckdb')
con.execute("INSERT INTO lokalise_data SELECT * FROM df")
con.close()
```
Make sure the column names and data types in the DataFrame match those in the DuckDB table.
After loading the data, it's important to verify that it has been correctly imported into DuckDB. Run a few SQL queries within DuckDB to check the contents of the table. For example:
```sql
SELECT * FROM lokalise_data LIMIT 10;
```
This query will display the first ten rows of your table, allowing you to confirm that the data has been successfully imported and correctly structured.
By following these steps, you can efficiently move your data from Lokalise to DuckDB 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: