🚀 Semantic Data Science Toolkit

Stop breaking pipelines when schemas change. The Semantic Data Science Toolkit introduces Stable Column Anchors (SCAs) that survive renames, reordering, and schema evolution.

🎯 What You're Looking At

This is our v1 foundation that demonstrates core innovations while being transparent about current vs. future capabilities. We've built something genuinely impressive but want your honest feedback on what works, what's missing, and where to focus next.

⚡ Quick Start (5 minutes)

# Install and try the core demo
npm install -g @semantic-ds/toolkit

# See the main innovation in action
semantic-ds quickstart --demo

# Test basic file analysis
semantic-ds infer examples/customers.csv

# Try the interactive project setup
semantic-ds init --interactive

🏆 What Actually Works (Please Test These)

1. Stable Column Anchors (SCA) - Our Core Innovation

Status: ✅ PRODUCTION READY

This is our main technical breakthrough - column fingerprinting that survives schema changes:

# The anchor system has 64 passing tests and real functionality
npm test test/anchors.test.ts
npm test test/shadow-semantics.test.ts

What to test:

• Load the same CSV with renamed columns - anchors should match
• Try different file formats (CSV, JSON) - universal adaptation works
• Check ./semantics/anchors/ directory for real YAML persistence

Why this matters: This solves the #1 cause of pipeline breaks in data engineering.

2. Professional CLI Experience

Status: ✅ FULLY FUNCTIONAL

We've built enterprise-grade developer experience:

# Real tab completion (try this)
semantic-ds completion install bash

# Professional help system
semantic-ds --help
semantic-ds infer --help

# Interactive wizards work
semantic-ds init --interactive

What to test:

• Tab completion for commands and options
• Error handling and help messages
• Directory creation and project scaffolding

3. DataFrame Integration

Status: ✅ WORKING

Universal support for different data formats without external dependencies:

# Test with your own CSV files
semantic-ds infer your-data.csv

# JSON support
semantic-ds infer your-data.json

What works: File loading, parsing, basic pattern recognition, anchor creation

⚠️ What's Demo Level (Don't Rely On These Yet)

Semantic Inference Results

The CLI shows beautiful progress bars and realistic-looking results like:

Semantic mappings found: 18
Average confidence: 0.87
Estimated time saved: 4.2 hours

Reality: File loading and basic pattern matching work, but the semantic mapping results are largely templated. We have the infrastructure but not the deep inference engine yet.

Health Monitoring & Validation

semantic-ds health    # Pretty dashboard, simulated metrics
semantic-ds validate  # Framework exists, limited real validation

Reality: Professional UX with realistic-looking metrics, but the actual health analysis is mostly simulated.

Performance Claims

We show impressive performance metrics in the CLI output.

Reality: The underlying anchor system is fast (sub-second for typical datasets), but the performance numbers shown are targets, not current measurements.

❌ What's Not Implemented Yet (Clear Roadmap Items)

1. SQL Generation - Referenced but not built
2. dbt/Snowflake Integration - Planned for next version
3. GitHub Bot - Directory structure exists, implementation doesn't
4. Advanced Drift Detection - Basic framework only
5. Federated CID Registry - Local YAML files only currently
6. Real-time Monitoring - Command exists but placeholder

🧪 Specific Testing Scenarios

Scenario 1: Schema Evolution (Core Strength)

# 1. Create a CSV with customer data
echo "customer_id,email,amount\n1,[email protected],100\n2,[email protected],200" > test1.csv

# 2. Analyze it
semantic-ds infer test1.csv

# 3. Rename columns and analyze again  
echo "cust_pk,mail,price\n1,[email protected],100\n2,[email protected],200" > test2.csv
semantic-ds infer test2.csv

# 4. Check ./semantics/anchors/ - should show matching anchors

Expected: Anchor system should recognize the renamed columns as the same concepts.

Scenario 2: Real File Analysis

# Try with your own data files
semantic-ds infer path/to/your/data.csv --verbose

# Check what anchors were created
ls -la ./semantics/anchors/
cat ./semantics/anchors/*.yml

Expected: Real file loading, basic column analysis, YAML anchor creation.

Scenario 3: Developer Experience

# Test the full DX workflow
semantic-ds init my-project --interactive
cd my-project
semantic-ds health
semantic-ds infer data/*.csv

Expected: Professional project scaffolding and workflow.

💭 Questions for Your Feedback

Technical Questions:

1. SCA System: Does the column fingerprinting approach make sense? Any edge cases we're missing?

2. API Design: Is the CLI interface intuitive? Would you want a Python/JavaScript API first?

3. Performance: The demos show sub-second performance. What scale should we target for v2?

Product Questions:

1. Problem-Solution Fit: Does this actually solve a pain you've experienced?

2. Adoption Path: If this were production-ready, how would you roll it out in your organization?

3. Integration Priority: Which integrations matter most? (dbt, Snowflake, Airflow, etc.)

Market Questions:

1. Competitive Position: How does this compare to tools you currently use?

2. Pricing Sensitivity: Open source core + premium features - does that model work?

3. Enterprise Readiness: What's missing for enterprise adoption?

🚀 Why We're Excited (Despite Current Limitations)

Technical Innovation

The Stable Column Anchor approach is a new approach we think is an improvement to some of the existing schema evolution solutions elsewhere.

Foundation Quality

While the features are limited, what we've built has:

• Zero external dependencies for core functionality
• Professional developer experience
• Strong test coverage for implemented features
• Clean, extensible architecture

Clear Path Forward

We have some ideas on what to build next from early users and research:

• Real semantic inference engine
• SQL generation for major warehouses
• Performance optimization with SIMD/vectorization
• Enterprise governance features

⏰ Timeline Expectations

Based on current velocity:

• Next 4 weeks: Real inference engine, basic SQL generation
• Next 8 weeks: dbt integration, performance optimization
• Next 12 weeks: Enterprise features, GitHub bot

🤝 How to Give Feedback

What's Most Helpful:

1. Try the working features and tell us about edge cases
2. Share your specific use cases - do our examples match reality?
3. Test at your data scale - does it break at 10K rows? 100K rows?
4. Integration priorities - what would make this immediately useful for you?

What We Don't Need Yet:

• Bug reports on features marked as "demo level"
• Performance feedback on simulated metrics

License

Apache License 2.0. See LICENSE for details.