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Begin by exporting the data from Lokalise. Log into your Lokalise account and navigate to the project you want to export. Use the export feature to download the data in a suitable format, such as JSON or CSV, which can be easily handled and imported into MongoDB. Save the exported file to your local machine.
Ensure you have MongoDB installed on your system. Download and install the MongoDB Community Server from the official MongoDB website. Also, install MongoDB client tools like `mongo` shell or `MongoDB Compass` to interact with your databases. These tools will help you import the data into MongoDB.
With the exported file in JSON or CSV format, review the data structure to ensure compatibility with MongoDB. If necessary, clean or transform the data to match your MongoDB schema requirements. For JSON files, make sure each document is properly structured; for CSV files, prepare for conversion to a JSON-compatible format.
Open your MongoDB client and create a new database and collection where you will import the Lokalise data. You can do this using the `mongo` shell or MongoDB Compass by executing commands like:
```bash
use yourDatabaseName
db.createCollection('yourCollectionName')
```
If your exported data is in CSV format, convert it to JSON. This can be done using a script in Python or Node.js. For example, in Python, use the `pandas` library:
```python
import pandas as pd
data = pd.read_csv('yourfile.csv')
data.to_json('yourfile.json', orient='records')
```
Use the `mongoimport` tool to import the JSON file into MongoDB. Execute a command in your terminal like:
```bash
mongoimport --db yourDatabaseName --collection yourCollectionName --file yourfile.json --jsonArray
```
This command imports the JSON data into the specified MongoDB database and collection.
After importing the data, verify that the data has been correctly imported into MongoDB. Use the `mongo` shell or MongoDB Compass to query the collection and check the records. For example, in the `mongo` shell, execute:
```bash
use yourDatabaseName
db.yourCollectionName.find().pretty()
```
This will display the imported documents, allowing you to confirm the successful data transfer from Lokalise to MongoDB.
By following these steps, you can manually move data from Lokalise to a MongoDB destination 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: