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Data Organization

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Use Meaningful Names

Give your projects and datasets clear, descriptive names that your team will understand. Good examples:
  • “Q1 2024 Marketing Leads”
  • “Customer Churn Analysis”
  • “Product Inventory - US Warehouse”
Avoid:
  • “Data1”
  • “Test”
  • “Final_v2_FINAL”

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Structure Your Projects

Group related datasets into projects by:
  • Team or department - Marketing, Sales, Product
  • Initiative - Product launch, Annual review
  • Data source - CRM exports, Survey responses
Create a naming convention and document it. Consistency makes it easier for teammates to find what they need.

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Data Quality

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Clean Data on Import

Address data quality issues early:
  1. Remove duplicates - Deduplicate immediately after import using primary keys
  2. Standardize formats - Use operations to normalize dates, phone numbers, and addresses
  3. Handle nulls - Decide how to treat missing values before analysis

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Set Primary Keys

Always designate primary keys for datasets that will be updated:
  • Enables reliable deduplication
  • Supports upsert operations
  • Maintains record identity across updates
Without a primary key, appending data may create duplicates.

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Validate Your Data

Before sharing or exporting:
  1. Check row counts match expectations
  2. Verify column types are correct
  3. Review a sample of transformed data
  4. Test filters return expected results

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Operations Pipeline

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Order Matters

Apply operations in a logical sequence: 
1. Filter (remove irrelevant rows first)
2. Deduplicate (on filtered data)
3. Transform (clean and standardize)
4. Sort (organize for output)
Filtering before deduplication ensures you keep the right records when duplicates exist.

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Keep Pipelines Simple

  • Each operation should do one thing well
  • Avoid overly complex filter conditions
  • Break large transformations into steps
  • Name operations descriptively

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Document Your Work

Add comments explaining:
  • Why an operation was applied
  • Business logic behind filters
  • Data source and freshness
  • Known limitations or caveats

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Collaboration

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Use Appropriate Access Levels

When to useAccess level
Working draftKeep private
Team reviewShare with editors
Stakeholder reviewShare as view-only
External sharingUse share links carefully

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Communicate Changes

When modifying shared datasets:
  • Notify teammates before major changes
  • Document what changed and why
  • Use comments for context

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Review Before Sharing Externally

Before creating public links:
  • Verify no sensitive data is exposed
  • Check that filters are applied correctly
  • Confirm the view shows what you intend

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Performance

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Optimize Large Datasets

For datasets with 100K+ rows:
  • Filter early - Reduce row count before other operations
  • Limit columns - Remove unnecessary columns
  • Use pagination - Don’t load everything at once
  • Export in batches - For very large exports

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Import Efficiently

  • Use CSV for large files (faster than Excel)
  • Split very large files into chunks
  • Remove unnecessary columns before import

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Version Control

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Leverage Version History

Rowbase automatically versions your data:
  • Before major changes - Note the current version
  • After mistakes - Rollback to a previous version
  • For audits - Export data from specific points in time

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Create Checkpoints

Before significant transformations:
  1. Export a backup
  2. Note the version number
  3. Document what you’re about to change
Version history is your safety net. Don’t be afraid to experiment knowing you can always go back.

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Security

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Protect Sensitive Data

  • Never include passwords or API keys in datasets
  • Be cautious with PII (names, emails, addresses)
  • Use view-only sharing for sensitive reports
  • Audit who has access to sensitive projects

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Manage Access Regularly

  • Remove access when team members leave
  • Review sharing settings quarterly
  • Use project-level permissions over dataset-level when possible