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Connecting to Data · Legacy self-service product

Upload a File or Spreadsheet

The fastest way to start analyzing your data in Scoop

You're viewing documentation for the legacy Scoop self-service product. For the current product, see What Is Scoop.

Uploading files into Scoop Analytics is the fastest way to get started exploring your data. Whether your information is stored in CSV, Excel, or a similar spreadsheet format, Scoop makes it easy to preview, clean, and ingest data right from your browser—no technical skills required.

Supported File Formats

Format Extension Notes
CSV .csv Comma-separated values (most common)
Excel .xlsx Modern Excel format (recommended)
Excel Legacy .xls Older Excel format (supported)
Tab-Separated .tsv Tab-delimited files
Google Sheets URL Paste shareable link

File Size Limits

Upload Method Maximum Size Maximum Rows
Browser Upload 50 MB ~200,000 rows
Large File Upload Contact support Millions of rows

Step-by-Step Upload Process

Step 1: Start Upload

Open the Datasets page and click New Dataset. Select Upload File as the source:

Two ways to upload:

  1. Drag and drop your file into the upload area
  2. Click Browse to select from your computer

Step 2: Preview and Validate

Scoop automatically scans your file and shows a preview:

Detection What Scoop Does
Headers Identifies column names from first row(s)
Data Types Recognizes numbers, dates, text, currencies
Formatting Handles various regional formats
Issues Highlights potential problems

Adjusting Detection

If Scoop's automatic detection needs adjustment:

Issue Solution
Wrong header row Click header row selector, choose correct row
Incorrect data type Click column type dropdown, select correct type
Date format ambiguous Select specific date format (MDY, DMY, etc.)
Exclude columns Uncheck columns you don't need

Tip: If Scoop highlights a date column in orange, it detected ambiguous dates. Click the column type dropdown and pick the correct format.

Step 3: Name and Confirm

  1. Give your dataset a clear, descriptive name you'll recognize later
  2. Review the preview one more time
  3. Click Confirm Upload

Scoop will:

  • Process all rows
  • Scan for data quality issues
  • Load valid data into your workspace
  • Take you to the data explorer

Step 4: Start Analyzing

Once uploaded, you can immediately:

  • Ask questions in natural language
  • Create visualizations
  • Add calculated columns
  • Set up snapshotting (for recurring uploads)

What Happens After Upload

Stage What Occurs
Processing File parsed, data types assigned
Validation Quality checks, issue detection
Ingestion Data loaded into Scoop
Indexing AI learns about your data
Ready Full analysis capabilities available

AI Understanding

After upload, Scoop's AI:

  • Analyzes column relationships
  • Identifies potential metrics and dimensions
  • Suggests relevant visualizations
  • Prepares for natural language queries

Advanced Options

Handling Multiple Sheets

For Excel workbooks with multiple tabs:

Approach When to Use
Select specific sheet Only need one tab's data
Upload multiple times Need each tab as separate dataset
Combine first Pre-merge sheets before upload

Tip: Summary sheets, pivot tables, and chart sheets often cause issues. Focus on raw data tabs only.

Date Parsing

Scoop handles various date formats:

Format Example Scoop Detection
ISO 2024-01-15 Automatic
US 01/15/2024 Automatic
European 15/01/2024 May need selection
Mixed Various Requires attention

Best Practice: Use consistent date formats throughout your file.

Character Encoding

Encoding Support Notes
UTF-8 ✅ Recommended Best compatibility
UTF-16 ✅ Supported Windows exports
ASCII ✅ Supported Basic text
Latin-1 ✅ Supported European characters

If you see garbled characters, re-save your CSV as UTF-8.

Special Characters

Scoop supports non-ASCII characters including:

  • International characters (é, ñ, ü, etc.)
  • Currency symbols (€, £, ¥)
  • Emojis (in data values)

File Preparation Best Practices

Before Uploading

Practice Why
Single header row Ensures correct column detection
Remove merged cells Prevents parsing issues
Delete summary rows Avoids double-counting
Clean trailing spaces Improves matching accuracy
Use consistent formats Enables proper type detection
✅ Good Structure:
Row 1: Column Headers
Row 2+: Data rows only

❌ Avoid:
- Multiple header rows
- Merged cells
- Embedded charts
- Footer/total rows
- Blank rows in middle

Optimizing Large Files

For files approaching size limits:

Optimization How
Remove unused columns Keep only relevant data
Filter rows Limit to needed date range
Split by category Create separate files
Compress formulas Replace with values

Common Troubleshooting

File Won't Upload

Symptom Solution
Upload fails immediately Check file size (max 50 MB)
Format not recognized Ensure extension is .csv, .xls, or .xlsx
Stuck processing Remove password protection or encryption
Browser timeout Try smaller file or different browser

Wrong Header Row Detected

  • Open preview options
  • Select the correct row as header
  • Re-confirm column names

Garbled Characters

  1. Open file in Excel or text editor
  2. Save As → Choose CSV UTF-8
  3. Re-upload the new file

Date Columns Not Detected

Issue Fix
Mixed formats Standardize all dates to one format
Extra spaces Remove leading/trailing spaces
Text dates Convert to actual date values
Regional ambiguity Explicitly select format in preview

Blank Rows/Columns Imported

  • In preview, uncheck empty columns
  • Scroll to bottom to verify no footer rows
  • Check for hidden rows in source file

Data Types Incorrect

Problem Solution
Numbers as text Check for currency symbols, spaces
Dates as text Verify date format consistency
Text truncated Check for line breaks in cells

After Upload: Next Steps

Immediate Actions

Action Purpose
Review data Verify all rows imported correctly
Check types Confirm data types are appropriate
Add descriptions Document column meanings
Start asking questions Test with natural language queries

For Recurring Data

If you'll upload updated versions of this file:

Approach Use Case
Replace dataset Overwrite with new data
Append data Add new rows to existing
Data Email Automate with email ingestion

See Data Email Ingestion to automate recurring file uploads.

If you have data in multiple files:

Scenario Solution
Same structure, different periods Append or create time series
Different structures, related data Create blended dataset
Lookup tables Upload separately, use in blends

See Blending Datasets to combine files.

Related Topics

Updated October 19, 2026