Sample Files

Sample files are built-in datasets you can load without uploading your own data. Use them to evaluate Mammoth, train team members, or build demos.

Why use sample files?

• No data preparation: Start exploring right away.
• Business scenarios: Each dataset represents a common business problem.
• No sensitive data: Test features without using company data.
• Shared training data: Teams can practice on identical datasets.
• Demo preparation: Build presentations with familiar data patterns.

Available sample datasets

Each sample is a CSV file of 25 to 63 rows, so it loads quickly and is easy to inspect. Samples are grouped into four categories.

Business

Dataset	Description
Multi-Store Retail Sales	5 clothing stores format standardization
E-commerce Multi-Channel	Shopify, Amazon, eBay product matching
Global Franchise Performance	Multi-country restaurant data with currency
Hotel Occupancy Analytics	Multi-platform booking data consolidation
Social Media Campaign Performance	Facebook, Instagram, LinkedIn consolidation

Technology

Dataset	Description
SaaS Business Metrics	Customer acquisition, churn, expansion analysis
Competitive Intelligence	Competitor pricing and feature analysis
Manufacturing Quality Control	Production line and IoT sensor metrics
Supply Chain Optimization	Global vendor performance and delivery
Media & Entertainment ROI	Cross-platform content performance

Finance & Real Estate

Dataset	Description
Financial P&L Consolidation	Multi-subsidiary revenue with currency conversion
Credit Risk Assessment	Multi-bureau loan data risk scoring
Real Estate Portfolio Analytics	Multi-market property performance and occupancy
Construction Project Tracking	Multi-site timelines, costs, resources

Healthcare & Entertainment

Dataset	Description
Patient Outcome Tracking	Clinical data ID standardization and metrics
Educational Institution Analytics	Student performance, enrollment, resource data
Non-Profit Fundraising Analytics	Multi-platform donor and campaign data
Sports Team Performance	Player stats, financials, fan engagement
Event Management Analytics	Multi-venue ticket sales and attendance

How to access sample files

Create a dataset from sample data

1. Start Dataset Creation

   • Click + New Dataset in your Project
   • Select Sample Files from the data source options

2. Browse by Category

   • Choose the All samples tab, or one of the Business, Technology, Finance & Real Estate, or Healthcare & Entertainment tabs
   • Review dataset descriptions
   • Consider your learning objectives or demo requirements

3. Select

   • Click the card of your chosen dataset. The card is highlighted; there is no data preview
   • Confirm the description matches your intended use case

4. Create Dataset

   • Click Create new dataset to load the sample data
   • Your new dataset appears in the Project with sample data ready for exploration

Tip: Choose sample data that matches your team's domain, for example a manufacturing quality dataset for an operations team.

Choosing the right sample dataset

For first-time exploration

Recommended: Multi-Store Retail Sales

• Familiar business concepts (sales, products, regions)
• Suits several transformation types
• Has patterns for data quality assessment
• Fits aggregation and filtering scenarios

For advanced feature testing

Recommended: E-commerce Multi-Channel or Supply Chain Optimization

• Complex multi-source scenarios
• Data standardization challenges
• Advanced calculation opportunities

For team training

Recommended: Match your industry (Manufacturing → Quality Control, SaaS → Business Metrics)

• Domain-relevant scenarios keep exercises practical
• Team members recognize the patterns and problems

For client demonstrations

Recommended: Industry-specific datasets

• Stakeholders recognize familiar data
• Use cases map to real business problems

Get the most from sample data

Exploration best practices

Start with a data quality assessment

1. Use Explore Cards to understand data distributions
2. Check for missing values, outliers, inconsistencies
3. Identify transformation opportunities before building pipelines

Try several transformation types

1. Cleaning: Standardize company names, fix formatting issues
2. Filtering: Remove test data, focus on specific regions or time periods
3. Aggregating: Calculate totals, averages, growth rates
4. Joining: Combine multiple datasets to create richer analysis

Build example pipelines

• Create monthly sales reports (Multi-Store Retail)
• Calculate customer lifetime value (SaaS Business Metrics)
• Analyze campaign ROI (Social Media Performance)
• Track quality improvements (Manufacturing Quality Control)

Move to your own data

When you are ready for real data:

1. Document Your Learning: Note which transformations worked well with sample data
2. Identify Patterns: Which data quality issues did you encounter and solve?
3. Plan Your Implementation: How will your real data differ from the samples?
4. Start Small: Begin with a subset of your production data

Example sample file use cases

New user onboarding

Timeline: about 15 minutes

Path:

1. Select Multi-Store Retail Sales sample (2 minutes)
2. Explore data using Explore Cards (5 minutes)
3. Build simple filter pipeline (5 minutes)
4. Generate first dashboard showing sales by region (3 minutes)

Outcome: The user has hands-on experience with the platform.

Consultant client demo

Timeline: 30 minutes preparation, 15 minutes presentation

Path:

1. Choose industry-relevant sample dataset
2. Build demo pipeline solving common client problem
3. Create polished dashboard for presentation
4. Use familiar data patterns to explain capabilities

Outcome: A demo that uses relevant, familiar data.

Team training standardization

Timeline: 1 hour training session

Path:

1. All team members use identical sample dataset
2. Follow structured exercises with predictable outcomes
3. Compare results and discuss best practices
4. Progress to individual exploration with other samples

Outcome: The team shares a common baseline.

Tip: Build your demo pipeline with sample data before the client meeting. You can then repeat the same transformations with their actual data.

Sample file limitations

What sample files are not:

• Your actual business data (they're learning tools)
• Production-ready datasets (they're designed for education)
• Comprehensive industry solutions (they're starting points)

What to expect:

• Datasets are smaller than production data (optimized for learning speed)
• Some edge cases may not be represented
• Data is clean enough to focus on learning Mammoth, not data cleaning

When to move beyond samples:

• You're comfortable with basic transformations
• You need to solve specific business problems
• You're ready to integrate with your actual systems
• You want to build production dashboards

Next steps

After exploring sample files:

1. Try File Upload: Import your own CSV or Excel files
2. Build your first real pipeline: Apply learned techniques to your business data
3. Explore connectors: Connect to your databases and business systems
4. Create dashboards: Visualize insights from your transformed data
5. Set up Automation: Schedule regular data processing workflows