Skip to main content

Add your description here

Project description

Project Overview: PyDhara

PyDhara is a Asynchronous data processing framework that enables users to create custom nodes to process data at various levels. These nodes can be connected using the Operator Pattern, a software design pattern that allows for the composition of nodes to form a data processing pipeline.

Key Features:

  • Custom Nodes: Users can define their own nodes to perform specific data processing tasks, such as filtering, transformation, and aggregation.
  • Operator Pattern: PyDhara uses the operator pattern to process data, where nodes are composed together to form a data processing pipeline.
  • Asynchronous Processing: PyDhara is designed to process data asynchronously, allowing for efficient and scalable data processing pipelines.
  • Modular Architecture: PyDhara's design allows for easy composition of nodes to create complex data processing pipelines.
  • Event-Driven Architecture: PyDhara's operator pattern is built on top of an event-driven architecture, where nodes publish events (data) to a channel, and other nodes subscribe to receive those events.

Example Use Case:

Suppose we want to build a data pipeline that:

  1. Reads data from a CSV file
  2. Filters out records with missing values
  3. Transforms the data by converting dates to a standard format
  4. Aggregates the data by group

Using PyDhara, we can define custom nodes for each of these tasks and connect them using the operator pattern. This allows us to create a modular and reusable data processing pipeline that can handle large datasets efficiently.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

pydhara-0.1.0.tar.gz (21.4 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

pydhara-0.1.0-py3-none-any.whl (13.3 kB view details)

Uploaded Python 3

File details

Details for the file pydhara-0.1.0.tar.gz.

File metadata

  • Download URL: pydhara-0.1.0.tar.gz
  • Upload date:
  • Size: 21.4 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: uv/0.6.5

File hashes

Hashes for pydhara-0.1.0.tar.gz
Algorithm Hash digest
SHA256 878bb47fb748fcc9ea86c3882e5321689a23affddff406c5fc58511f2d167f8b
MD5 930af70ab70d35e766c4d5f2ac36de3d
BLAKE2b-256 bb667b51ac3be96ed1e850fd440677b530d5d8c118e5d8c68226acd54f9e530e

See more details on using hashes here.

File details

Details for the file pydhara-0.1.0-py3-none-any.whl.

File metadata

  • Download URL: pydhara-0.1.0-py3-none-any.whl
  • Upload date:
  • Size: 13.3 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: uv/0.6.5

File hashes

Hashes for pydhara-0.1.0-py3-none-any.whl
Algorithm Hash digest
SHA256 c2cd4e5877d383115c1d5e1cac4d3ad8c43da366cf102a92c1366287a95d883a
MD5 3c3097b2a21b6f20e0c2795fdc262f33
BLAKE2b-256 8aebca6358f40ede57ac645dee60d6f2a73d77c800d7ee5233246c25eacc922f

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page