CSV File
- Files
Upload your CSV files to Yurbi to convert the data into database tables, allowing for easy reporting and dashboard creation that can be refreshed with manual uploads.
About
CSV (Comma-Separated Values) files are a simple and widely used format for storing tabular data in plain text. When integrated with Yurbi, users can upload CSV files to create database tables that facilitate efficient reporting and dashboard creation. It is essential that the CSV files use commas as delimiters to ensure proper parsing of the data. This straightforward format allows for easy data manipulation and is compatible with many applications, making it an excellent choice for data transfer.
Supported Versions
- Comma-delimited values only
Integration Capabilities
- Upload CSV files containing raw data
- Create database tables from uploaded CSV data
Requirements
- CSV file format with comma delimiters
- Properly structured data without blank rows or columns
- Network access to the Yurbi server for uploading files
Support
Integration FAQs
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How do I get started with this integration?
If you have admin privileges, navigate to Settings > Integration and select Create a New App for the data source you want to use.
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What types of data can I access through this integration?
Yurbi generates SQL queries dynamically using the official database driver, ODBC driver, or API endpoint. Through this integration, you can access a variety of structured data from your data source, such as tables, views, and results from stored procedures in MSSQL and MySQL.
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How do I refresh the data in Yurbi after making changes in my database?
Yurbi performs live queries against your data source, ensuring that every time a report or dashboard is executed, it retrieves the most up-to-date data available. This means you always have access to the latest information without needing to manually refresh anything.
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Can Yurbi cause any damage or harm to my data source?
Yurbi performs read-only queries against the connected data source, meaning it cannot write or modify any data. However, if queries are designed to pull excessive amounts of data, they can consume significant resources, potentially leading to timeouts or increased load that may affect overall performance.