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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:
- Reads data from a CSV file
- Filters out records with missing values
- Transforms the data by converting dates to a standard format
- 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.
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