Projects sharing features with Hamilton

ZenML is an orchestration platform designed for building, deploying, and monitoring reproducible machine learning pipelines and agentic workflows. It provides a unified framework that manages the entire lifecycle of machine learning assets, from data processing and model training to the deployment of persistent inference services. By decoupling pipeline logic from underlying compute and storage, the platform enables teams to transition workflows seamlessly from local development environments to production-grade cloud infrastructure. The platform distinguishes itself through a service-oriented

Rudder Server is a customer data platform and event routing pipeline designed to collect, transform, and route customer event data from various sources to data warehouses and business tools. It functions as a customer identity resolver, linking identifiers from multiple sources to build a unified identity graph and comprehensive behavioral customer profiles. The system differentiates itself through reverse ETL capabilities, which push processed customer segments and audiences from data warehouses back into operational third-party applications. It also provides a containerized data plane for K

Orchest is a data pipeline orchestrator and containerized workflow manager. It provides a platform for designing, scheduling, and executing complex data processing sequences through a combination of a graphical interface and scripting. The platform distinguishes itself by using containers to manage software dependencies, ensuring consistent execution across different environments. It features a polyglot task scheduler capable of triggering jobs written in multiple programming languages and includes a version control system that tracks historical snapshots of project configurations and code.

The fastest ⚡️ way to build data pipelines. Develop iteratively, deploy anywhere. ☁️

Argo Workflows is a container-native workflow engine that functions as a Kubernetes custom resource controller. It orchestrates complex sequences of containerized tasks by executing them as directed acyclic graphs, allowing for dependency management and parallel processing within a cluster. The system extends the native Kubernetes control plane to manage the full lifecycle of automated processes, from initial triggering to final resource cleanup. The platform distinguishes itself through its controller-pattern reconciliation, which continuously monitors workflow states to align them with desi

Kedro is a data science pipeline framework and production toolbox designed to build reproducible, modular workflows using software engineering best practices. It functions as a data engineering orchestrator and catalog manager, bridging the gap between interactive analysis and maintainable production pipelines. The framework distinguishes itself by using a data catalog to decouple data access from processing logic and providing tools to transition analysis from interactive notebooks into structured workflows. It includes a workflow visualization tool that generates visual maps of data pipelin

Prefect is a workflow orchestration platform designed to define, schedule, and monitor complex data pipelines as Python code. It functions as a container-native engine that wraps individual tasks in isolated environments, ensuring consistent dependencies and resource allocation across diverse infrastructure. By utilizing a state-machine-based orchestration model, the system tracks execution progress through discrete transitions and persistent event logs to maintain reliable and observable task processing. The platform distinguishes itself through a decoupled worker-API architecture, which sep

Airflow is a platform for programmatically authoring, scheduling, and monitoring complex data pipelines. It functions as a workflow automation engine that manages the lifecycle of recurring business processes by executing code-defined task dependencies. By representing workflows as directed acyclic graphs, the system ensures that task execution order and data flow are explicitly defined and reliably maintained across distributed computing environments. The platform distinguishes itself through a highly modular, provider-based architecture that decouples core orchestration logic from external

DBND an open source framework for building and tracking data pipelines. DBND is used for processes ranging from data ingestion, preparation, machine learning model training and production.

Cocoindex is an incremental data processing engine that builds and maintains live indexes for AI agents, with a core focus on codebase indexing and knowledge graph extraction. The engine uses a function-graph execution model where user-defined Python functions are composed into a directed acyclic graph, and it processes data incrementally so only changed source records or code paths are re-computed, avoiding full recomputation at any scale. It supports automatic schema inference from transformation pipeline type annotations and provides full data lineage tracing, tagging every output record wi

Build data pipelines with SQL and Python, ingest data from different sources, add quality checks, and build end-to-end flows.

Metaflow is a Python machine learning framework and MLOps workflow orchestrator designed to manage the lifecycle of data pipelines from local prototyping to production. It serves as a distributed compute manager and an experiment tracking system, enabling the creation of reproducible pipelines that transition between development and high-availability production environments. The framework distinguishes itself through an integrated checkpointing system that automatically persists intermediate data artifacts to remote storage, allowing failed runs to be resumed from the last successful step. It

Elementary OSS: dbt-native data observability

Flyte is a Kubernetes-based machine learning orchestrator and containerized pipeline manager designed for coordinating AI workflows and data pipelines. It functions as an engine for defining and executing resilient pipelines, utilizing a data lineage tracker to maintain immutable execution states and ensure reproducible outputs. The platform distinguishes itself by packaging individual tasks into separate containers to ensure dependency isolation and environment consistency. It provides specialized capabilities for machine learning, including the transformation of trained models into scalable

Kestra is a declarative workflow orchestrator designed to manage complex task dependencies and automated processes through versioned configuration files. It functions as a distributed platform that decouples task scheduling from execution by offloading computational workloads to a fleet of worker nodes. The system uses a reactive, event-driven engine to initiate workflows automatically in response to external signals, webhooks, schedules, or file system changes. The platform distinguishes itself through a modular plugin architecture that allows for the integration of custom tasks and external

Pinball is a scalable workflow manager

This project is an educational resource and a collection of instructional materials for performing data manipulation and statistical analysis using Python. It provides a comprehensive set of guides and code examples for using the Pandas, NumPy, and Matplotlib libraries to analyze structured data. The resource includes a dedicated guide for reshaping, cleaning, and aggregating tabular data and time series via Pandas, alongside a reference for high-performance vectorized operations and linear algebra using NumPy. It also features tutorials for creating publication-quality charts, distribution p

Teable is a self-hosted relational data management tool and no-code PostgreSQL database. It provides a spreadsheet-like interface for managing and querying structured data, allowing users to interact with a professional database backend without writing manual SQL for every operation. The platform is an extensible low-code system that allows for the integration of custom plugins and extensions through a dedicated application bridge and marketplace. It enables the creation of tailored internal tools by adding new features or modifying behavior via these external extensions. The system covers a

DataFrame is a C++ tabular data library and manipulation engine designed for managing heterogeneous data in contiguous memory. It functions as a statistical analysis framework and time series analysis toolkit, providing the means to store, index, and transform multidimensional datasets. The project distinguishes itself through a high-performance execution model that utilizes column-major storage, SIMD-aligned memory allocation, and a thread-pool for parallel computations. It employs a visitor-based algorithm dispatch system and policy-driven transformations to decouple data processing logic f

This repository is a comprehensive collection of instructional guides and practical examples for Python development, focusing on machine learning, data science, and web scraping. It provides implementations for neural networks, reinforcement learning algorithms, and deep learning architectures using PyTorch, alongside detailed manuals for scientific computing and data visualization. The project distinguishes itself by offering specialized tutorials on concurrent programming to optimize CPU performance and guides for setting up Linux development environments. It covers the implementation of ad

This project is a comprehensive pandas data analysis tutorial and instructional guide designed for learning data manipulation and analysis. It serves as a tabular data processing guide and a manual for time series analysis, providing a structured approach to cleaning, merging, and transforming datasets. The repository functions as a data feature engineering course, providing tutorials on constructing and selecting dataset features to improve machine learning model performance. It also includes a vectorized data operations guide for performing element-wise mathematical computations and matrix

Scriban is a text templating library and .NET scripting engine used for dynamic text generation. It functions as a template processor and a safe scripting sandbox, providing a secure execution environment that restricts object exposure to prevent unauthorized code execution. The project also includes an abstract syntax tree template parser that allows for programmatic template analysis and modification. The engine features a dedicated Liquid template engine and compatibility mode, allowing it to parse, execute, and convert templates written in Liquid syntax. It distinguishes itself through a

OpenAddresses is an open-source geospatial data aggregator and directory that collects public domain and open-license address, parcel, and building datasets from governments and organizations worldwide. It functions as a global index and data warehouse for locating and distributing free geospatial records. The project operates a normalization pipeline that cleans and standardizes diverse source formats into a consistent global coordinate and attribute schema. This process includes a crowdsourced curation pipeline and programmatic quality validation to verify the spatial accuracy and formattin