etlplus 0.21.3

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
  • Getting Started
  • Features
  • Installation
  • Quickstart
  • Data Connectors
    • REST APIs (api)
    • Databases (database)
    • Files (file)
      • Handler Matrix Guardrail
      • Stubbed / Placeholder
      • Tabular & Delimited Text
      • Semi-Structured Text
      • Columnar / Analytics-Friendly
      • Binary Serialization and Interchange
      • Databases and Embedded Storage
      • Spreadsheets
      • Statistical / Scientific / Numeric Computing
      • Logs and Event Streams
      • Data Archives
      • Templates
  • Usage
    • Command Line Interface
      • Argument Order and Required Options
      • Check 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
  • License
  • Contributing
  • Documentation
    • Python Packages/Subpackage
    • Community Health
    • Other
  • Acknowledgments

Getting Started

ETLPlus helps you extract, validate, transform, and load data from files, databases, and APIs, either as a Python library or from the command line.

To get started:

• See Installation for setup instructions.
• Try the Quickstart for a minimal working example (CLI and Python).
• Explore Usage for more detailed options and workflows.

ETLPlus supports Python 3.13 and above.

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; extract is a placeholder today)
  • 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; load is a placeholder today)
  • REST APIs (PATCH, POST, PUT)

Installation

pip install etlplus

For development:

pip install -e ".[dev]"

For full file-format support (optional extras):

pip install -e ".[file]"

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 examples/data/sample.csv \
  | etlplus transform --operations '{"filter": {"field": "age", "op": "gt", "value": 25}, "select": ["name", "email"]}' \
  - temp/sample_output.json

Python API:

from etlplus.ops 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")

Data Connectors

Data connectors abstract sources from which to extract data and targets to which to load data. They are differentiated by their types, each of which is represented in the subsections below.

REST APIs (api)

ETLPlus can extract from REST APIs and load results via common HTTP methods. Supported operations include GET for extract and PATCH/POST/PUT for load.

Databases (database)

Database connectors use connection strings for extraction and loading, and DDL can be rendered from table specs for migrations or schema checks. Database extract/load operations are currently placeholders; plan to integrate a database client in your runner.

Files (file)

Recognized file formats are listed in the tables below. Support for reading to or writing from a recognized file format is marked as:

• Y: implemented (may require optional dependencies)
• N: stubbed or not yet implemented

Handler Architecture

• File IO is moving to class-based handlers rooted at etlplus/file/base.py (FileHandlerABC, category ABCs, and ReadOnlyFileHandlerABC).
• etlplus/file/registry.py resolves handlers using an explicit FileFormat -> handler class map.
• Dispatch is explicit-only: unmapped formats raise Unsupported format.
• Placeholder handlers are split into:
  • etlplus/file/stub.py for generic stub behavior
  • etlplus/file/_stub_categories.py for category-aware internal stub ABCs
• Scientific/statistical handlers dta, nc, rda, rds, sav, and xpt now implement ScientificDatasetFileHandlerABC dataset hooks.

Current Migration Coverage (Class-Based + Explicit Registry Mapping)

• Delimited/text: csv, dat, fwf, psv, tab, tsv, txt
• Semi-structured/config: ini, json, ndjson, properties, toml, xml, yaml
• Columnar: arrow, feather, orc, parquet
• Binary/interchange: avro, bson, cbor, msgpack, pb, proto
• Embedded DB: duckdb, sqlite
• Spreadsheets: ods, xls, xlsm, xlsx
• Scientific/statistical: dta, nc, rda, rds, sav, xpt, sas7bdat (read-only), plus single-dataset scientific stubs mat, sylk, zsav
• Archive wrappers: gz, zip
• Log/event streams: log
• Templates: hbs, jinja2, mustache, vm
• Explicit module-owned stub handlers (via stub.py + _stub_categories.py): stub, accdb, cfg, conf, ion, mdb, numbers, pbf, wks

Handler Matrix Guardrail

The concise matrix below is the migration guardrail for class-based handler coverage. For batch-by-batch maintenance notes and the same matrix in docs, see docs/file-handler-matrix.md.

Format	Handler Class	Base ABC	Read/Write Support	Status
accdb	AccdbFile	StubEmbeddedDatabaseFileHandlerABC	read/write	stub
arrow	ArrowFile	ColumnarFileHandlerABC	read/write	implemented
avro	AvroFile	BinarySerializationFileHandlerABC	read/write	implemented
bson	BsonFile	BinarySerializationFileHandlerABC	read/write	implemented
cbor	CborFile	BinarySerializationFileHandlerABC	read/write	implemented
cfg	CfgFile	StubSemiStructuredTextFileHandlerABC	read/write	stub
conf	ConfFile	StubSemiStructuredTextFileHandlerABC	read/write	stub
csv	CsvFile	DelimitedTextFileHandlerABC	read/write	implemented
dat	DatFile	DelimitedTextFileHandlerABC	read/write	implemented
dta	DtaFile	SingleDatasetScientificFileHandlerABC	read/write	implemented
duckdb	DuckdbFile	EmbeddedDatabaseFileHandlerABC	read/write	implemented
feather	FeatherFile	ColumnarFileHandlerABC	read/write	implemented
fwf	FwfFile	TextFixedWidthFileHandlerABC	read/write	implemented
gz	GzFile	ArchiveWrapperFileHandlerABC	read/write	implemented
hbs	HbsFile	TemplateFileHandlerABC	read/write	implemented
hdf5	Hdf5File	ScientificDatasetFileHandlerABC	read-only	implemented
ini	IniFile	SemiStructuredTextFileHandlerABC	read/write	implemented
ion	IonFile	StubSemiStructuredTextFileHandlerABC	read/write	stub
jinja2	Jinja2File	TemplateFileHandlerABC	read/write	implemented
json	JsonFile	SemiStructuredTextFileHandlerABC	read/write	implemented
log	LogFile	LogEventFileHandlerABC	read/write	implemented
mat	MatFile	StubSingleDatasetScientificFileHandlerABC	read/write	stub
mdb	MdbFile	StubEmbeddedDatabaseFileHandlerABC	read/write	stub
msgpack	MsgpackFile	BinarySerializationFileHandlerABC	read/write	implemented
mustache	MustacheFile	TemplateFileHandlerABC	read/write	implemented
nc	NcFile	SingleDatasetScientificFileHandlerABC	read/write	implemented
ndjson	NdjsonFile	SemiStructuredTextFileHandlerABC	read/write	implemented
numbers	NumbersFile	StubSpreadsheetFileHandlerABC	read/write	stub
ods	OdsFile	SpreadsheetFileHandlerABC	read/write	implemented
orc	OrcFile	ColumnarFileHandlerABC	read/write	implemented
parquet	ParquetFile	ColumnarFileHandlerABC	read/write	implemented
pb	PbFile	BinarySerializationFileHandlerABC	read/write	implemented
pbf	PbfFile	StubBinarySerializationFileHandlerABC	read/write	stub
properties	PropertiesFile	SemiStructuredTextFileHandlerABC	read/write	implemented
proto	ProtoFile	BinarySerializationFileHandlerABC	read/write	implemented
psv	PsvFile	DelimitedTextFileHandlerABC	read/write	implemented
rda	RdaFile	ScientificDatasetFileHandlerABC	read/write	implemented
rds	RdsFile	SingleDatasetScientificFileHandlerABC	read/write	implemented
sas7bdat	Sas7bdatFile	SingleDatasetScientificFileHandlerABC	read-only	implemented
sav	SavFile	SingleDatasetScientificFileHandlerABC	read/write	implemented
sqlite	SqliteFile	EmbeddedDatabaseFileHandlerABC	read/write	implemented
stub	StubFile	StubFileHandlerABC	read/write	stub
sylk	SylkFile	StubSingleDatasetScientificFileHandlerABC	read/write	stub
tab	TabFile	DelimitedTextFileHandlerABC	read/write	implemented
toml	TomlFile	SemiStructuredTextFileHandlerABC	read/write	implemented
tsv	TsvFile	DelimitedTextFileHandlerABC	read/write	implemented
txt	TxtFile	TextFixedWidthFileHandlerABC	read/write	implemented
vm	VmFile	TemplateFileHandlerABC	read/write	implemented
wks	WksFile	StubSpreadsheetFileHandlerABC	read/write	stub
xls	XlsFile	ReadOnlySpreadsheetFileHandlerABC	read-only	implemented
xlsm	XlsmFile	SpreadsheetFileHandlerABC	read/write	implemented
xlsx	XlsxFile	SpreadsheetFileHandlerABC	read/write	implemented
xml	XmlFile	SemiStructuredTextFileHandlerABC	read/write	implemented
xpt	XptFile	SingleDatasetScientificFileHandlerABC	read/write	implemented
yaml	YamlFile	SemiStructuredTextFileHandlerABC	read/write	implemented
zip	ZipFile	ArchiveWrapperFileHandlerABC	read/write	implemented
zsav	ZsavFile	StubSingleDatasetScientificFileHandlerABC	read/write	stub

Stubbed / Placeholder

Format	Read	Write	Description
stub	N	Placeholder format for tests and future connectors.

Tabular & Delimited Text

Format	Read	Write	Description
csv	Y	Y	Comma-Separated Values
dat	Y	Y	Generic data file, often delimited or fixed-width
fwf	Y	Y	Fixed-Width Fields
psv	Y	Y	Pipe-Separated Values
tab	Y	Y	Often synonymous with TSV
tsv	Y	Y	Tab-Separated Values
txt	Y	Y	Plain text, often delimited or fixed-width

Semi-Structured Text

Format	Read	Write	Description
cfg	N	N	Config-style key-value pairs
conf	N	N	Config-style key-value pairs
ini	Y	Y	Config-style key-value pairs
json	Y	Y	JavaScript Object Notation
ndjson	Y	Y	Newline-Delimited JSON
properties	Y	Y	Java-style key-value pairs
toml	Y	Y	Tom's Obvious Minimal Language
xml	Y	Y	Extensible Markup Language
yaml	Y	Y	YAML Ain't Markup Language

Columnar / Analytics-Friendly

Format	Read	Write	Description
arrow	Y	Y	Apache Arrow IPC
feather	Y	Y	Apache Arrow Feather
orc	Y	Y	Optimized Row Columnar; common in Hadoop
parquet	Y	Y	Apache Parquet; common in Big Data

Binary Serialization and Interchange

Format	Read	Write	Description
avro	Y	Y	Apache Avro
bson	Y	Y	Binary JSON; common with MongoDB exports/dumps
cbor	Y	Y	Concise Binary Object Representation
ion	N	N	Amazon Ion
msgpack	Y	Y	MessagePack
pb	Y	Y	Protocol Buffers (Google Protobuf)
pbf	N	N	Protocolbuffer Binary Format; often for GIS data
proto	Y	Y	Protocol Buffers schema; often in .pb / .bin

Databases and Embedded Storage

Format	Read	Write	Description
accdb	N	N	Microsoft Access (newer format)
duckdb	Y	Y	DuckDB
mdb	N	N	Microsoft Access (older format)
sqlite	Y	Y	SQLite

Spreadsheets

Format	Read	Write	Description
numbers	N	N	Apple Numbers
ods	Y	Y	OpenDocument
wks	N	N	Lotus 1-2-3
xls	Y	N	Microsoft Excel (BIFF; read-only)
xlsm	Y	Y	Microsoft Excel Macro-Enabled (Open XML)
xlsx	Y	Y	Microsoft Excel (Open XML)

Statistical / Scientific / Numeric Computing

Format	Read	Write	Description
dta	Y	Y	Stata
hdf5	Y	N	Hierarchical Data Format
mat	N	N	MATLAB
nc	Y	Y	NetCDF
rda	Y	Y	RData workspace/object
rds	Y	Y	R data
sas7bdat	Y	N	SAS data
sav	Y	Y	SPSS data
sylk	N	N	Symbolic Link
xpt	Y	Y	SAS Transport
zsav	N	N	Compressed SPSS data

Logs and Event Streams

Format	Read	Write	Description
log	Y	Y	Generic log file

Data Archives

Format	Read	Write	Description
gz	Y	Y	Gzip-compressed file
zip	Y	Y	ZIP archive

Templates

Format	Read	Write	Description
hbs	Y	Y	Handlebars
jinja2	Y	Y	Jinja2
mustache	Y	Y	Mustache
vm	Y	Y	Apache Velocity

Usage

Command Line Interface

ETLPlus provides a powerful CLI for ETL operations:

# Show help
etlplus --help

# Show version
etlplus --version

The CLI is implemented with Typer (Click-based). The legacy argparse parser has been removed, so rely on the documented commands/flags and run etlplus <command> --help for current options.

Example error messages:

• If you omit a required argument: Error: Missing required argument 'SOURCE'.
• If you place an option before its argument: Error: Option '--source-format' must follow the 'SOURCE' argument.

Argument Order and Required Options

For each command, positional arguments must precede options. Required options must follow their associated argument:

• extract: etlplus extract SOURCE [--source-format ...] [--source-type ...]
  • SOURCE is required. --source-format and --source-type must follow SOURCE.
• transform: etlplus transform [--operations ...] SOURCE [--source-format ...] [--source-type ...] TARGET [--target-format ...] [--target-type ...]
  • SOURCE and TARGET are required. Format/type options must follow their respective argument.
• load: etlplus load TARGET [--target-format ...] [--target-type ...] [--source-format ...]
  • TARGET is required. --target-format and --target-type must follow TARGET.
• validate: etlplus validate SOURCE [--rules ...] [--source-format ...] [--source-type ...]
  • SOURCE is required. --rules and format/type options must follow SOURCE.

If required arguments or options are missing, or if options are placed before their associated argument, the CLI will display a clear error message.

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 examples/data/sample.json

Extract from CSV file:

etlplus extract examples/data/sample.csv

Extract from XML file:

etlplus extract examples/data/sample.xml

Extract from REST API:

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

Save extracted data to file:

etlplus extract examples/data/sample.csv > 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 --source-type to override the inferred source connector type and --target-type to override the inferred target connector type, matching the etlplus extract/etlplus load behavior.

Transform file inputs while overriding connector types:

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

Filter and select fields:

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

Sort data:

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

Aggregate data:

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

Map/rename fields:

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

Load Data

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

Load to JSON file:

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

Load to CSV file:

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

Load to REST API:

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

Python API

Use ETLPlus as a Python library:

from etlplus.ops 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", file_format="json")

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

• Authoring: docs/pipeline-guide.md
• Runner API and internals: see etlplus.ops.run docstrings and docs/pipeline-guide.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

Complete ETL Pipeline Example

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

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

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

# 4. Load to CSV
cat temp/sample_transformed.json \
  | etlplus load 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 data.txt --source-type file --source-format csv

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

# Leave the flags off when extensions already match the desired format
etlplus extract data.csv --source-type file
etlplus load output.json --target-type file < 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: see etlplus.ops.run docstrings and docs/pipeline-guide.md
• Unified "connector" vocabulary (API/File/DB): etlplus/connector
  • 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 three layers:

• Unit (tests/unit/): single function or class, no real I/O, fast, uses stubs/monkeypatch (e.g. small helpers in etlplus.utils, transform + validate helpers).
• Smoke (tests/smoke/): minimal end-to-end checks for core flows; may touch temp files but avoids external network calls.
• 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.ops.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.

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

Documentation

Python Packages/Subpackage

Navigate to detailed documentation for each subpackage:

• etlplus.api: Lightweight HTTP client and paginated REST helpers
• etlplus.file: Unified file format support and helpers
• etlplus.cli: Command-line interface definitions for etlplus
• etlplus.database: Database engine, schema, and ORM helpers
• etlplus.templates: SQL and DDL template helpers
• etlplus.ops: Extract/validate/transform/load primitives
• etlplus.workflow: Helpers for data connectors, pipelines, jobs, and profiles

Community Health

• Contributing Guidelines: How to contribute, report issues, and submit PRs
• Code of Conduct: Community standards and expectations
• Security Policy: Responsible disclosure and vulnerability reporting
• Support: Where to get help

Other

• API client docs: etlplus/api/README.md
• Examples: examples/README.md
• File handler matrix guardrail: docs/file-handler-matrix.md
• Pipeline authoring guide: docs/pipeline-guide.md
• Runner internals: see etlplus.ops.run docstrings and docs/pipeline-guide.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

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!