sagescan-data 1.0.6

Production-grade, CLI-first data quality validation for modern data pipelines

SageScan

Production-grade, CLI-first data quality validation for modern data pipelines.

SageScan combines a Go CLI for fast, scriptable orchestration with a Python engine for rich statistical validation — connected via a clean JSON bridge over stdin/stdout.

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Table of Contents

1. Why SageScan?
2. Architecture
3. Installation
4. Quick Start
5. CLI Reference
6. Configuration Reference
7. Validator Types
8. Example Dataset & Output
9. Big-Data Readiness
10. AI Layer (Optional)
11. Testing
12. Roadmap
13. Contributing

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Why SageScan?

Data pipelines fail silently more often than they break loudly.
SageScan helps you catch those failures before they reach production.

Feature	SageScan
CLI-first design	✅ — pipe-friendly, CI-ready
Declarative rules	✅ — YAML-based, version-controlled
Statistical checks	✅ — z-score, mean, std, PSI, KS
Drift detection	✅ — KS test + Population Stability Index
AI rule generation	✅ — optional, GPT-4 compatible
Big-data support	✅ — CSV chunked reads, Parquet
Zero external DB	✅ — runs on local files

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Architecture

┌─────────────────────────────────────────────────────┐
│                    Go CLI (Cobra)                    │
│   validate │ profile │ report │ generate-rules │ init│
└─────────────────────┬───────────────────────────────┘
                      │  JSON via stdin/stdout
┌─────────────────────▼───────────────────────────────┐
│              Python Engine (main.py)                 │
│   Command router → runner → pipeline → validators   │
└──────────────────────────────────────────────────────┘
         │                              │
   Pydantic v2                   Validator Registry
   (strict schema)          (not_null │ regex │ z_score
                             min_length │ allowed_values …)

Key Design Decisions:

Layer	Technology	Rationale
CLI	Go + Cobra + Viper	Fast startup, single binary, no runtime needed
Validation Engine	Python + pandas	Rich ecosystem for data science
Schema Validation	Pydantic v2	Strict, fast, Pythonic
Communication	JSON stdin/stdout	Simple, debuggable, language-agnostic
Config	YAML	Human-readable, version-control friendly

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Installation

Install SageScan effortlessly via PyPI. This will automatically bundle the compiled Go CLI alongside the Python statistical validations!

pip install sagescan-data[all]

(You must have a valid Python 3.9+ runtime to execute the pipeline).

Verify your installation globally:

sagescan --version

Advanced Setup (From Source)

For contributors or teams who want to build the Go binary and harness the raw codebase directly instead of using pip:

1. Clone the repo

   git clone https://github.com/sagescan/sagescan.git
   cd sagescan
   

2. Setup the local Python Engine

   make setup-python
   

3. Compile the Go CLI natively

   make build
   

4. Use your local binary instead of the global pip command:

   ./sagescan --help
   

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Quick Start

# 1. Create a sample data file
cat > data/sample.csv << 'EOF'
user_id,age,email,status
1,25,[e-mail],active
2,17,[e-mail],inactive
3,30,[e-mail],active
EOF

# 2. Initialise a config
sagescan init --output rules.yaml

# 3. Edit rules.yaml to match your columns, then run:
sagescan validate rules.yaml

# 4. Get JSON output (great for CI / downstream processing)
sagescan validate rules.yaml --output json

# 5. Fail CI pipeline on any violation
sagescan validate rules.yaml --fail-fast

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CLI Reference

Usage:
  sagescan [command]

Available Commands:
  validate        Run data quality validation on a config file
  profile         Generate a statistical profile of a dataset
  report          Generate a formatted validation report
  generate-rules  Auto-generate validation rules using AI (requires API key)
  init            Create a new SageScan config file with defaults

Flags:
  --timeout duration   Max wait time for engine (default: 5m)
  --context string     Validation context tag (e.g. production, staging)
  --baseline string    Path to baseline config for comparison
  -h, --help           Help for any command
  -v, --version        Show version

validate

sagescan validate <config.yaml> [flags]

Flags:
  -o, --output string    Output format: text (default) or json
      --fail-fast        Exit 1 on any validation failure (for CI)
      --timeout 10m      Override engine timeout
      --context string   Tag the run (e.g. production)

profile

sagescan profile <config.yaml> [flags]

Profiles the dataset: row count, column types, null %, min/max/mean/std for numeric columns.

report

sagescan report <config.yaml> [flags]

Runs validation and outputs a rich formatted report.

generate-rules

sagescan generate-rules -i data.csv -o rules.yaml [flags]

Flags:
  -i, --input string             Input CSV file (required)
  -o, --output string            Output YAML rules file (required)
      --llm-model string         LLM model (default: gpt-4o)
      --llm-api-key string       OpenAI API key (or set OPENAI_API_KEY env var)
      --llm-base-url string      Custom API base URL for self-hosted or local LLMs
      --llm-temperature float32  Temperature setting for LLM generation (default: 0.3)

init

sagescan init --output my_rules.yaml

Generates a ready-to-use config file with sensible defaults.

---

Configuration Reference

version: "1.0"

source:
  type: csv              # csv | parquet
  path: data/users.csv   # relative to the config file, or absolute

rules:
  - column: user_id
    checks:
      - type: not_null
      - type: unique

  - column: age
    checks:
      - type: range
        min: 18
        max: 120
      - type: z_score
        value: 3.0         # flag |z| > 3 as outlier

  - column: email
    checks:
      - type: not_null
      - type: regex
        value: "^[^@]+@[^@]+\\.[^@]+$"
      - type: max_length
        value: 254

  - column: status
    checks:
      - type: allowed_values
        values: [active, inactive, pending, banned]

# Optional — needed only for generate-rules and explain features
# llm_api_key: sk-...   (prefer OPENAI_API_KEY env var)
# llm_model: gpt-4o
# llm_max_tokens: 2000
# llm_base_url: http://localhost:11434/v1
# llm_temperature: 0.3

---

Validator Types

Check type	Config keys	Description
not_null	—	No null values allowed
unique	—	All values must be distinct
min_value	value	All values ≥ value
max_value	value	All values ≤ value
range	min, max	All values in [min, max]
regex	value	Values match regex pattern
pattern	value	Alias for regex
null_percentage	value	% of nulls ≤ value (0–100)
min_length	value	String length ≥ value
max_length	value	String length ≤ value
allowed_values	values: [...]	Value must be in the list
mean_check	min, max	Column mean in [min, max]
std_check	min, max	Column std in [min, max]
z_score	value	Flag rows where |z| > value
z_score_outlier	threshold, upper_threshold, lower_threshold	Separate upper/lower bounds
ks_test	reference_path, alpha	Kolmogorov–Smirnov distribution test
psi	reference_path, threshold, bins	Population Stability Index

---

Example Dataset & Output

examples/sample_data.csv

user_id,age,email,status
1,25,[e-mail],active
2,17,[e-mail],inactive
3,30,[e-mail],active
3,40,[e-mail],pending
5,,[e-mail],active

Run:

sagescan validate examples/basic_rules.yaml

Output:

📊 Validating data quality rules from: examples/basic_rules.yaml
────────────────────────────────────────────────────────────

Status: FAIL
Pass Rate: 60.0%

Checks: 5 total | 3 passed | 2 failed

  ✅ user_id              not_null             All values are not null
  ❌ user_id              unique               Found 1 duplicate values
  ❌ age                  not_null              Null values found in 1 rows
  ✅ age                  range                All values are within range
  ✅ email                regex                All values match the specified pattern

JSON output mode:

sagescan validate examples/basic_rules.yaml --output json

{
  "status": "FAIL",
  "summary": {
    "total": 5,
    "passed": 3,
    "failed": 2,
    "pass_rate": 60.0
  },
  "results": [
    {
      "column": "user_id",
      "check_type": "not_null",
      "passed": true,
      "message": "All values are not null"
    },
    ...
  ]
}

---

Environment Variables

Variable	Default	Description
SAGESCAN_ENGINE_PATH	<binary_dir>/engine/main.py	Override Python engine path
SAGESCAN_VERBOSE	false	Set true for debug timing info
SAGESCAN_LOG_LEVEL	WARNING	Python log level (DEBUG/INFO/WARNING/ERROR)
OPENAI_API_KEY	—	API key for AI features

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Big-Data Readiness

Scenario	Current Behaviour
CSV < 50 MB	Full in-memory load
CSV 50 MB – 2 GB	Chunked pd.read_csv(chunksize=100_000)
CSV > 2 GB	Rejected with clear error message
Parquet	pd.read_parquet via pyarrow (up to 4 GB)
Failed rows	Capped at 1,000 per check to prevent OOM

Planned improvements:

• Polars backend (10–50× faster than pandas for large files)
• Streaming validation (per-chunk without materialising full DataFrame)
• DuckDB connector for in-process SQL-based validation
• S3 / GCS URI support for cloud-native use

---

AI Layer (Optional)

Generate rules from a dataset

export OPENAI_API_KEY=sk-...
sagescan generate-rules -i data/users.csv -o rules/users.yaml --context "user registration data"

To use a custom local model like Ollama instead of OpenAI, provide your custom base URL and temperature:

sagescan generate-rules -i data/users.csv -o rules/users.yaml --llm-model="llama3" --llm-base-url="http://localhost:11434/v1" --llm-temperature=0.8 --llm-api-key="dummy"

SageScan sends column statistics (not raw data) to the LLM to generate rules — no PII leaves your machine.

Explain validation failures

Add llm_api_key to your config. On the next validate run, failed checks automatically get plain-English explanations appended to the JSON output.

AI is always optional. Core validation is fully deterministic and works without any API key.

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Testing

# Run all tests
make test

# Go tests only
make test-go

# Python tests only
make test-python

# With coverage
cd engine && python -m pytest --cov=sagescan_engine --cov-report=term-missing

Test coverage:

• engine/sagescan_engine/validators/test_implementations.py — all 17 validator types
• engine/sagescan_engine/rules/test_models.py — Pydantic schema validation
• internal/cli/base_test.go — CLI config loading
• internal/python/engine_test.go — subprocess communication

---

Project Structure

sagescan/
├── cmd/sagescan/
│   └── main.go                  # CLI entrypoint
├── internal/
│   ├── cli/                     # Cobra command definitions
│   │   ├── cli.go               # Root command + init
│   │   ├── validate.go          # sagescan validate
│   │   ├── profile.go           # sagescan profile
│   │   ├── report.go            # sagescan report
│   │   ├── generate_rules.go    # sagescan generate-rules
│   │   ├── init.go              # sagescan init
│   │   ├── base.go              # Shared flags/helpers
│   │   └── logger.go            # Structured logging setup
│   ├── python/
│   │   └── engine.go            # Go ↔ Python subprocess bridge
│   └── config/
│       └── config.go            # Config loading utilities
├── engine/
│   ├── main.py                  # Python engine entrypoint
│   ├── requirements.txt
│   ├── pyproject.toml
│   └── sagescan_engine/
│       ├── core/
│       │   ├── pipeline.py      # Data loading + validator orchestration
│       │   ├── runner.py        # Command dispatch (validate/profile/…)
│       │   └── report.py        # Report formatting (CLI / JSON / HTML)
│       ├── rules/
│       │   ├── models.py        # Pydantic config models
│       │   └── schema.py        # JSON schema
│       ├── validators/
│       │   ├── base.py          # BaseValidator + ValidationResult
│       │   ├── implementations.py  # All 17 validator types
│       │   ├── distribution.py  # KS test, PSI, z-score outlier
│       │   └── registry.py      # Validator factory
│       └── llm/
│           ├── rule_generator.py     # AI rule generation
│           └── explanation_generator.py  # AI failure explanation
├── examples/
│   ├── sample_data.csv
│   ├── basic_rules.yaml
│   └── comprehensive_rules.yaml
├── Makefile
├── go.mod
└── README.md

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Roadmap

v1.1 (Next)

• Polars backend (opt-in via --engine polars)
• DuckDB connector for SQL-based validation
• HTML report generation
• --watch mode for streaming pipelines
• GitHub Actions example workflow

v1.2

• PostgreSQL connector
• Snowflake connector
• S3/GCS URI support
• Webhook notifications (Slack, PagerDuty)
• Rule inheritance / rule templates

v2.0

• Web UI dashboard
• Drift alerting + historical trending
• dbt integration
• Great Expectations compatibility layer

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Contributing

Contributions are welcome!

# Fork the repo, then:
git checkout -b feat/my-improvement

# Make your changes, then test:
make test

# Open a pull request

Guidelines:

• All new validators must have tests in test_implementations.py
• Go changes must pass go vet ./...
• Python changes must pass python -m py_compile
• Keep engine/main.py stdout clean — only JSON, nothing else
• Document new check types in this README