navas-etl 0.1.2

A flexible ELT pipeline implementation in Python

navas-etl

A flexible ELT (Extract, Load, Transform) pipeline implementation in Python with support for multiple data sources and destinations.

Features

• Extract from multiple sources:

  • REST APIs
  • CSV files
  • Databases (via SQLAlchemy)

• Transform data with:

  • Cleaning operations
  • Aggregations
  • Formatting

• Load to various targets:

  • Databases
  • CSV files
  • JSON files

Installation

pip install navas-etl

Quick Start

from navas-etl import ApiExtractor, Cleaner, DbLoader

# 1. Extract data from API
extractor = ApiExtractor("https://api.example.com")
data = extractor.extract_to_dataframe("users")

# 2. Transform/Clean the data
cleaner = Cleaner(data)
cleaned_data = (cleaner
               .drop_duplicates()
               .fill_missing(strategy='mean')
               .get_data())

# 3. Load to database
loader = DbLoader("postgresql://user:password@localhost:5432/mydb")
loader.load(cleaned_data, "users_table")

Documentation

Extractors

API Extractor

from navas-etl import ApiExtractor

extractor = ApiExtractor(base_url="https://api.example.com", auth=("user", "pass"))
data = extractor.extract("endpoint", params={"param": "value"})

CSV Extractor

from navas-etl import CsvExtractor

extractor = CsvExtractor()
df = extractor.extract("data.csv")

Database Extractor

from navas-etl import DbExtractor

extractor = DbExtractor("postgresql://user:pass@localhost:5432/db")
df = extractor.extract("SELECT * FROM table")

Transformers

See full documentation for more examples.

Contributing

Contributions are welcome! Please open an issue or submit a pull request.

License

This project is licensed under the MIT License - see the LICENSE file for details.