You can upload local CSV files and Excel files (.xls and .xlsx files) to the explore space as a data source.

The explore space is a data source type that is used only in the personal workspace. Each user has 1 GB storage space.

In addition, you can import data sources from the Data IDE.

CSV file

CSV files in UTF-8 format are decoded without errors. CSV files in GBK or GB2312 format are automatically decoded, but the decoding may fail in some special cases.

If your CSV files cannot be decoded correctly, use text editors such as Notepad to convert the encoding of the files to UTF-8. Otherwise, the content of the uploaded files is shown as gibberish.

  1. Use Notepad to open a CSV file.
  2. Choose File > Save As.
  3. Click the drop-down arrow of Encoding.
  4. Select UTF-8.

After you have changed the encoding, upload the CSV file to the explore space.

  1. Log on to the Quick BI console.
  2. Click Data Source to enter the Data Sources page.
  3. ClickCreate Data Sources > Local Upload > CSV file.
  4. Enter a display name for the file.
  5. Click Select File to select a file to upload, as shown in the following figure.


  6. Click OK to upload the file.

Excel file

When you upload Excel files, you need to select which sheet in each excel file to upload. To make the editing and maintenance more flexible, you can only select one sheet in one Excel file at a time.

  1. ClickCreate Data Sources > Local Upload > EXCEL file.
  2. Enter a display name for the file.
  3. Click Select a file to select the file to upload, as shown in the following figure.


  4. Click OK to complete uploading the file.

Data IDE

Note Only supports importing data sources from the China (Shanghai) region, and you must add your account to the project.
  1. ClickCreate Data Sources > Local Upload > Data IDE.
  2. Select the data source from the list.
  3. Click Import to complete the importing of the data source.

Update table data according to the local data source

The local data source feature of Quick BI is designed to meet the analysis requirements for your changing and growing business.

After you have uploaded a file, new files are generated as the business grows. You can append the new files to the table corresponding to the previously uploaded file to analyze business data consistently over a long period of time.

The new file can be in a format different from the previously uploaded file. For example, if you have uploaded a CSV file, you can import data from a sheet in an Excel file. Make sure that the field names and the field types in the files to be uploaded are the same as those in the previously uploaded file.

  1. Click Data Source to enter the Data Sources page.
  2. Click Explore Space to enter the Explore Space page.
  3. Select a file, and then click Update.
  4. Click Append to upload the file that needs to be appended.
  5. Click OK to append the data.

Delete the data of the table corresponding to a local data source

If a file that you have appended contains dirty data, which decreases the accuracy of the data, you can delete the file with dirty data that is corresponding to the table in the uploaded file list. The corresponding dashboard displays corrected data without any manual changes. Therefore, the analysis results of data are accurate at all times.

  1. Click Data Source to enter the Data Sources page.
  2. Click Explore Space to enter the Explore Space page.
  3. Select a file, and then click Update.
  4. Locate the file that needs to be deleted, and then click the Delete icon, as shown in the following figure.


Example of local files

To help you learn to use local data source files, we provide a sample CSV file here: Sales data examples.

The structure of the sales data is shown in the following table.

Field Field type Description
order_id varchar Order ID
report_date datetime Order date
customer_name varchar Customer name
order_level varchar Order grade
order_number double Order quantity
order_amt double Order amount
back_point double Discount
shipping_type varchar Shipping type
profit_amt double Profit amount
price double Unit price
shipping_cost double Shipping cost
area varchar Region
province varchar Province
city varchar City
product_type varchar Product type
product_sub_type varchar Product subtype
product_name varchar Product name
product_box varchar Product packing box
shipping_date datetime Shipping date