acme_dw 0.0.5

Simple data warehouse using S3

acme-dw

Simple data warehouse using S3

Problem

Some LLM based definitions: A data warehouse is a centralized repository designed for storing, managing, and analyzing structured data from various sources, optimized for query performance and reporting. It typically uses a schema-based approach to organize data in tables and supports complex queries and analytics. In contrast, a data lake is a storage system that holds vast amounts of raw, unstructured, and structured data in its native format until needed. It is designed for scalability and flexibility, allowing for the storage of diverse data types and enabling advanced analytics, machine learning, and big data processing.

We can see how S3 can be easily utilized as a data lake with little extra functionality. However to use it as a data warehouse we need to add some extra functionality that largly depends on the needs of a given domain.

Features

• Provides read/wrie on schema-less pd.DataFrame/pl.DataFrame
• Saves pd.DataFrame/pl.DataFrame using parquet format for fast read performance.
• Standardizes metadata associated with each dataset
• Support for parquet datasets (datasets spread over multiple parquet files).

Dev environment

The project comes with a python development environment. To generate it, after checking out the repo run:

chmod +x create_env.sh

Then to generate the environment (or update it to latest version based on state of uv.lock), run:

./create_env.sh

This will generate a new python virtual env under .venv directory. You can activate it via:

source .venv/bin/activate

If you are using VSCode, set to use this env via Python: Select Interpreter command.

Example usage

from acme_dw import DW, DatasetMetadata

dw = DW()
        
# Write with DatasetMetadata object
metadata = DatasetMetadata(
    source='yahoo_finance',
    name='price_history', 
    version='v1',
    process_id='fetch_yahoo_data',
    partitions=['minute', 'AAPL', '2025'],
    file_name='20250124',
    file_type='parquet'
)
dw.write_df(df, metadata)
df = dw.read_df(metadata)

Project template

This project has been setup with acme-project-create, a python code template library.

Required setup post use

• Enable GitHub Pages to be published via GitHub Actions by going to Settings-->Pages-->Source

• Create release-pypi environment for GitHub Actions to enable uploads of the library to PyPi

• Setup auth to PyPI for the GitHub Action implemented in .github/workflows/release.yml via Trusted Publisher uv publish doc

• Once you create the python environment for the first time add the uv.lock file that will be created in project directory to the source control and update it each time environment is rebuilt

• In order not to replicate documentation in docs/docs/index.md file and README.md in root of the project setup a symlink from README.md file to the index.md file. To do this, from docs/docs dir run:

  ln -sf ../../README.md index.md