Local Files

Overview

Feature Description

Importing data from files is a service provided in Guandata's data processing center that supports importing data from files such as Excel and CSV for further processing.

Use Cases

1. Rapid Business Growth Makes Excel Hard to Scale

   As an enterprise expands, adds departments, and grows rapidly, large volumes of data accumulate across sales, supply chain, finance, and other functions. When data volumes become too large, Excel can no longer support business analysis effectively, for example when a wide base table exceeds 100 million rows and cannot be loaded into Excel. Importing Excel and similar file data into the Guandata all-in-one analytics platform helps solve this problem by integrating and cleaning scattered departmental data into a unified enterprise data asset pool.

2. Data Is Siloed Across Departments, Making Correlation Analysis Difficult

   To gain a complete view of business performance, management often needs to combine and correlate statistics from different departments to identify growth opportunities and problem areas, then adjust business strategy accordingly. Importing file-based data from each department into a unified analytics platform helps leaders consolidate data, run pivot-style analysis, perform multidimensional exploration, and use visual dashboards for dimensional grouping, aggregation, slicing, and drill-down analysis.

User Guide

Steps

1. Go to the Data Preparation page and click the Datasets section in the left navigation.
2. Click Add Dataset, then select File > Local File.
3. Choose the corresponding file type: Excel or CSV.
4. After the upload succeeds, continue with file data management.

Select the Connector

Go to the Data Preparation page, click Datasets in the left navigation, and choose Add Dataset > File > Local File. The currently supported file types are Excel and CSV. CSV files can also be uploaded as compressed .zip packages and parsed automatically. If a .txt file follows CSV formatting rules, you can upload it by selecting CSV.

Select the Data Table

Local files support uploads in both Excel and CSV formats.

Note

1. The system enforces file size limits during upload.

   • CSV files, including zipped CSV files, cannot exceed 500 MB.
   • Excel files cannot exceed 500 MB, while older Excel formats such as .xls cannot exceed 5 MB.

2. The system automatically creates datasets at the worksheet level.

Confirm Data Table Information

Excel Files

1. After uploading the selected file, enter the dataset name, storage path, and description, then confirm. The default storage path is the system root directory.

2. You can mark the current dataset as a Sensitive Dataset. After this option is enabled, the dataset cannot be used directly to create cards. You must go to the dataset details page under Data Security to configure sensitive fields. For details, see Data Masking.

   If you are unsure which fields are sensitive, click Smart Detection for automatic scanning. The system identifies sensitive fields based on built-in and user-defined detection templates. For details, see Data Masking.

   

3. You can configure the upload format to import data from specified rows and columns. Preconfiguring the data range makes the upload more precise.

   • Set Header Row: You can define the header row position. By default, the system imports data from the header row through the last non-empty row, with row 1 used as the default header.
   • Select Columns: By default, the system imports columns from column A through the last non-empty column in the header. You can also define a custom column range.
   Note

   When you upload multiple worksheets with the same header row and column range, you can use Sync Configuration to apply the current settings to all remaining datasets.

4. Confirm whether you need to modify the current dataset's field names and field types.

   • Field Name: Click a field name to rename it.
   • Field Type: Click the down arrow next to the field type to adjust it.
   Note

   1. Up to 30 rows of data can be previewed in the data preview area.

   2. When you switch field types, the converted data is displayed immediately. If the value does not match the selected type, it appears as Null.

5. Configure a deduplication primary key as needed. During dataset initialization, the system deduplicates records based on the configured key.

CSV Files

CSV files follow the same upload workflow as Excel files. The only difference is the Confirm Parsing Result step.

Confirm Parsing Result: The system automatically parses the uploaded CSV file and displays the result. You can adjust the following parsing parameters:

• File Encoding: Supports multiple character encodings for parsing, such as UTF-8, UTF-16, and GB18030.
• Delimiter: Provides five common delimiter options and also supports custom delimiters.
• Enclosure Character: The enclosure character marks the beginning and end of text or data. In programming and data formats, enclosure characters help parsers identify structure and boundaries. Common examples include quotation marks, parentheses, and braces.
• Escape Character: The escape character is used to insert special characters into a string when they cannot be included directly. It typically starts with a backslash (`\`) followed by one or more characters representing a specific character or operation.

Create the Dataset

After completing the steps above, click Confirm Creation to create the Excel dataset. You can then find the dataset in the corresponding folder.

Append or Replace Data

Click a dataset in the folder directory to enter its overview page, then click Append Data or Replace Data in the upper-right corner and follow the on-screen instructions.

When appending or replacing data, if the uploaded dataset fields differ from the original dataset, you can map the fields manually before uploading. If multiple uploaded datasets share the same header structure, you can append or replace the same dataset with multiple files at once.

What Is the Difference Between Appending Data to a Manual Table and Replacing Data?

When appending data, field-level deduplication is supported. Click Enable Primary Key Deduplication, then select the fields to deduplicate. If duplicate values are found, the old data is replaced with the new data.

A primary key is one or more fields in a table whose values uniquely identify a record, such as a member ID, product ID, or sales order number.

Why Does Appending an Identical Manual Dataset Still Increase the Data Volume Even When a Deduplication Primary Key Is Selected?