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About Hamilton

Apache Hamilton (incubating) is a general-purpose framework designed to help users build and manage dataflows using standard Python functions. It automatically constructs a Directed Acyclic Graph (DAG) from these functions, where each function defines a transformation and its parameters indicate dependencies. This allows for the execution, visualization, optimization, and reporting of complex data pipelines. The product is ideal for data scientists, engineers, and developers who need to create robust, reusable, and scalable data processing pipelines. It facilitates collaboration by promoting a flat dataflow structure, making code reviews, debugging, and project hand-offs more efficient. Hamilton also offers a UI for visualizing lineage, cataloging code and artifacts, and monitoring dataflows. It aims to reduce development time through reusability and provides flexibility to integrate with existing frameworks and tools, preventing vendor lock-in. It's battle-tested for intensive enterprise data workloads and supports scaling through remote execution and specialized computation engines. Key benefits include improved code structuring, easy visualization of data lineage, and the ability to model Generative AI/LLM based workflows. It's designed to be a library that runs anywhere Python runs, ensuring code is always unit testable and documentation-friendly.