etlplus 0.7.0

A Swiss Army knife for simple ETL operations

ETLPlus

ETLPlus is a veritable Swiss Army knife for enabling simple ETL operations, offering both a Python package and command-line interface for data extraction, validation, transformation, and loading.

• ETLPlus
  • Features
  • Installation
  • Quickstart
  • Usage
    • Command Line Interface
      • Inspect Pipelines
      • Render SQL DDL
      • Extract Data
      • Validate Data
      • Transform Data
      • Load Data
    • Python API
    • Complete ETL Pipeline Example
    • Format Overrides
  • Transformation Operations
    • Filter Operations
    • Aggregation Functions
  • Validation Rules
  • Development
    • API Client Docs
    • Runner Internals and Connectors
    • Running Tests
      • Test Layers
    • Code Coverage
    • Linting
    • Updating Demo Snippets
    • Releasing to PyPI
  • Links
  • License
  • Contributing
  • Acknowledgments

Features

• Check data pipeline definitions before running them:

  • Summarize jobs, sources, targets, and transforms
  • Confirm configuration changes by printing focused sections on demand

• Render SQL DDL from shared table specs:

  • Generate CREATE TABLE or view statements
  • Swap templates or direct output to files for database migrations

• Extract data from multiple sources:

  • Files (CSV, JSON, XML, YAML)
  • Databases (connection string support)
  • REST APIs (GET)

• Validate data with flexible rules:

  • Type checking
  • Required fields
  • Value ranges (min/max)
  • String length constraints
  • Pattern matching
  • Enum validation

• Transform data with powerful operations:

  • Filter records
  • Map/rename fields
  • Select specific fields
  • Sort data
  • Aggregate functions (avg, count, max, min, sum)

• Load data to multiple targets:

  • Files (CSV, JSON, XML, YAML)
  • Databases (connection string support)
  • REST APIs (PATCH, POST, PUT)

Installation

pip install etlplus

For development:

pip install -e ".[dev]"

Quickstart

Get up and running in under a minute.

Command line interface:

# Inspect help and version
etlplus --help
etlplus --version

# One-liner: extract CSV, filter, select, and write JSON
etlplus extract file examples/data/sample.csv \
  | etlplus transform - --operations '{"filter": {"field": "age", "op": "gt", "value": 25}, "select": ["name", "email"]}' \
  -o temp/sample_output.json

Python API:

from etlplus import extract, transform, validate, load

data = extract("file", "input.csv")
ops = {"filter": {"field": "age", "op": "gt", "value": 25}, "select": ["name", "email"]}
filtered = transform(data, ops)
rules = {"name": {"type": "string", "required": True}, "email": {"type": "string", "required": True}}
assert validate(filtered, rules)["valid"]
load(filtered, "file", "temp/sample_output.json", file_format="json")

Usage

Command Line Interface

ETLPlus provides a powerful CLI for ETL operations:

# Show help
etlplus --help

# Show version
etlplus --version

Check Pipelines

Use etlplus check to explore pipeline YAML definitions without running them. The command can print job names, summarize configured sources and targets, or drill into specific sections.

List jobs and show a pipeline summary:

etlplus check --config examples/configs/pipeline.yml --jobs
etlplus check --config examples/configs/pipeline.yml --summary

Show sources or transforms for troubleshooting:

etlplus check --config examples/configs/pipeline.yml --sources
etlplus check --config examples/configs/pipeline.yml --transforms

Render SQL DDL

Use etlplus render to turn table schema specs into ready-to-run SQL. Render from a pipeline config or from a standalone schema file, and choose the built-in ddl or view templates (or provide your own).

Render all tables defined in a pipeline:

etlplus render --config examples/configs/pipeline.yml --template ddl

Render a single table in that pipeline:

etlplus render --config examples/configs/pipeline.yml --table customers --template view

Render from a standalone table spec to a file:

etlplus render --spec schemas/customer.yml --template view -o temp/customer_view.sql

Extract Data

Note: For file sources, the format is normally inferred from the filename extension. Use --source-format to override inference when a file lacks an extension or when you want to force a specific parser.

Extract from JSON file:

etlplus extract file examples/data/sample.json

Extract from CSV file:

etlplus extract file examples/data/sample.csv

Extract from XML file:

etlplus extract file examples/data/sample.xml

Extract from REST API:

etlplus extract api https://api.example.com/data

Save extracted data to file:

etlplus extract file examples/data/sample.csv -o temp/sample_output.json

Validate Data

Validate data from file or JSON string:

etlplus validate '{"name": "John", "age": 30}' --rules '{"name": {"type": "string", "required": true}, "age": {"type": "number", "min": 0, "max": 150}}'

Validate from file:

etlplus validate examples/data/sample.json --rules '{"email": {"type": "string", "pattern": "^[\\w.-]+@[\\w.-]+\\.\\w+$"}}'

Transform Data

When piping data through etlplus transform, use --source-format whenever the SOURCE argument is - or a literal payload, mirroring the etlplus extract semantics. Use --target-format to control the emitted format for stdout or other non-file outputs, just like etlplus load. File paths continue to infer formats from their extensions. Use --from to override the inferred source connector type and --to to override the inferred target connector type, matching the etlplus extract/etlplus load behavior.

Transform file inputs while overriding connector types:

etlplus transform --from file examples/data/sample.json \
  --operations '{"select": ["name", "email"]}' \
  --to file -o temp/selected_output.json

Filter and select fields:

etlplus transform '[{"name": "John", "age": 30}, {"name": "Jane", "age": 25}]' \
  --operations '{"filter": {"field": "age", "op": "gt", "value": 26}, "select": ["name"]}'

Sort data:

etlplus transform examples/data/sample.json --operations '{"sort": {"field": "age", "reverse": true}}'

Aggregate data:

etlplus transform examples/data/sample.json --operations '{"aggregate": {"field": "age", "func": "sum"}}'

Map/rename fields:

etlplus transform examples/data/sample.json --operations '{"map": {"name": "new_name"}}'

Load Data

etlplus load consumes JSON from stdin; provide only the target argument plus optional flags.

Load to JSON file:

etlplus extract file examples/data/sample.json \
  | etlplus load --to file temp/sample_output.json

Load to CSV file:

etlplus extract file examples/data/sample.csv \
  | etlplus load --to file temp/sample_output.csv

Load to REST API:

cat examples/data/sample.json \
  | etlplus load --to api https://api.example.com/endpoint

Python API

Use ETLPlus as a Python library:

from etlplus import extract, validate, transform, load

# Extract data
data = extract("file", "data.json")

# Validate data
validation_rules = {
    "name": {"type": "string", "required": True},
    "age": {"type": "number", "min": 0, "max": 150}
}
result = validate(data, validation_rules)
if result["valid"]:
    print("Data is valid!")

# Transform data
operations = {
    "filter": {"field": "age", "op": "gt", "value": 18},
    "select": ["name", "email"]
}
transformed = transform(data, operations)

# Load data
load(transformed, "file", "temp/sample_output.json", format="json")

For YAML-driven pipelines executed end-to-end (extract → validate → transform → load), see:

• Authoring: docs/pipeline-guide.md
• Runner API and internals: docs/run-module.md

CLI quick reference for pipelines:

# List jobs or show a pipeline summary
etlplus check --config examples/configs/pipeline.yml --jobs
etlplus check --config examples/configs/pipeline.yml --summary

# Run a job
etlplus run --config examples/configs/pipeline.yml --job file_to_file_customers

# Deprecated shim (will be removed): etlplus pipeline

Complete ETL Pipeline Example

# 1. Extract from CSV
etlplus extract file examples/data/sample.csv -o temp/sample_extracted.json

# 2. Transform (filter and select fields)
etlplus transform temp/sample_extracted.json \
  --operations '{"filter": {"field": "age", "op": "gt", "value": 25}, "select": ["name", "email"]}' \
  -o temp/sample_transformed.json

# 3. Validate transformed data
etlplus validate temp/sample_transformed.json \
  --rules '{"name": {"type": "string", "required": true}, "email": {"type": "string", "required": true}}'

# 4. Load to CSV
cat temp/sample_transformed.json \
  | etlplus load --to temp/sample_output.csv

Format Overrides

--source-format and --target-format override whichever format would normally be inferred from a file extension. This is useful when an input lacks an extension (for example, records.txt that actually contains CSV) or when you intentionally want to treat a file as another format.

Examples (zsh):

# Force CSV parsing for an extension-less file
etlplus extract --from file data.txt --source-format csv

# Write CSV to a file without the .csv suffix
etlplus load --to file output.bin --target-format csv < data.json

# Leave the flags off when extensions already match the desired format
etlplus extract --from file data.csv
etlplus load --to file data.json < data.json

Transformation Operations

Filter Operations

Supported operators:

• eq: Equal
• ne: Not equal
• gt: Greater than
• gte: Greater than or equal
• lt: Less than
• lte: Less than or equal
• in: Value in list
• contains: List/string contains value

Example:

{
  "filter": {
    "field": "status",
    "op": "in",
    "value": ["active", "pending"]
  }
}

Aggregation Functions

Supported functions:

• sum: Sum of values
• avg: Average of values
• min: Minimum value
• max: Maximum value
• count: Count of values

Example:

{
  "aggregate": {
    "field": "revenue",
    "func": "sum"
  }
}

Validation Rules

Supported validation rules:

• type: Data type (string, number, integer, boolean, array, object)
• required: Field is required (true/false)
• min: Minimum value for numbers
• max: Maximum value for numbers
• minLength: Minimum length for strings
• maxLength: Maximum length for strings
• pattern: Regex pattern for strings
• enum: List of allowed values

Example:

{
  "email": {
    "type": "string",
    "required": true,
    "pattern": "^[\\w.-]+@[\\w.-]+\\.\\w+$"
  },
  "age": {
    "type": "number",
    "min": 0,
    "max": 150
  },
  "status": {
    "type": "string",
    "enum": ["active", "inactive", "pending"]
  }
}

Development

API Client Docs

Looking for the HTTP client and pagination helpers? See the dedicated docs in etlplus/api/README.md for:

• Quickstart with EndpointClient
• Authentication via EndpointCredentialsBearer
• Pagination with PaginationConfig (page and cursor styles)
• Tips on records_path and cursor_path

Runner Internals and Connectors

Curious how the pipeline runner composes API requests, pagination, and load calls?

• Runner overview and helpers: docs/run-module.md
• Unified "connector" vocabulary (API/File/DB): etlplus/config/connector.py
  • API/file targets reuse the same shapes as sources; API targets typically set a method.

Running Tests

pytest tests/ -v

Test Layers

We split tests into two layers:

• Unit (tests/unit/): single function or class, no real I/O, fast, uses stubs/monkeypatch (e.g. etlplus.cli.create_parser, transform + validate helpers).
• Integration (tests/integration/): end-to-end flows (CLI main(), pipeline run(), pagination + rate limit defaults, file/API connector interactions) may touch temp files and use fake clients.

If a test calls etlplus.cli.main() or etlplus.run.run() it’s integration by default. Full criteria: CONTRIBUTING.md#testing.

Code Coverage

pytest tests/ --cov=etlplus --cov-report=html

Linting

flake8 etlplus/
black etlplus/

Updating Demo Snippets

DEMO.md shows the real output of etlplus --version captured from a freshly built wheel. Regenerate the snippet (and the companion file docs/snippets/installation_version.md) after changing anything that affects the version string:

make demo-snippets

The helper script in tools/update_demo_snippets.py builds the wheel, installs it into a throwaway virtual environment, runs etlplus --version, and rewrites the snippet between the markers in DEMO.md.

Releasing to PyPI

setuptools-scm derives the package version from Git tags, so publishing is now entirely tag driven—no hand-editing pyproject.toml, setup.py, or etlplus/__version__.py.

1. Ensure main is green and the changelog/docs are up to date.
2. Create and push a SemVer tag matching the v*.*.* pattern:

git tag -a v1.4.0 -m "Release v1.4.0"
git push origin v1.4.0

3. GitHub Actions fetches tags, builds the sdist/wheel, and publishes to PyPI via the publish job in .github/workflows/ci.yml.

If you want an extra smoke-test before tagging, run make dist && pip install dist/*.whl locally; this exercises the same build path the workflow uses.

Links

• API client docs: etlplus/api/README.md
• Examples: examples/README.md
• Pipeline authoring guide: docs/pipeline-guide.md
• Runner internals: docs/run-module.md
• Design notes (Mapping inputs, dict outputs): docs/pipeline-guide.md#design-notes-mapping-inputs-dict-outputs
• Typing philosophy: CONTRIBUTING.md#typing-philosophy
• Demo and walkthrough: DEMO.md
• Additional references: REFERENCES.md

License

This project is licensed under the MIT License.

Contributing

Code and codeless contributions are welcome! If you’d like to add a new feature, fix a bug, or improve the documentation, please feel free to submit a pull request as follows:

1. Fork this repository.
2. Create a new feature branch for your changes (git checkout -b feature/feature-name).
3. Commit your changes (git commit -m "Add feature").
4. Push to your branch (git push origin feature-name).
5. Submit a pull request with a detailed description.

If you choose to be a code contributor, please first refer these documents:

• Pipeline authoring guide: docs/pipeline-guide.md
• Design notes (Mapping inputs, dict outputs): docs/pipeline-guide.md#design-notes-mapping-inputs-dict-outputs
• Typing philosophy (TypedDicts as editor hints, permissive runtime): CONTRIBUTING.md#typing-philosophy

Acknowledgments

ETLPlus is inspired by common work patterns in data engineering and software engineering patterns in Python development, aiming to increase productivity and reduce boilerplate code. Feedback and contributions are always appreciated!