Awesome GitHub RepositoriesAutomated Data Validation

SurrealDB is a multi-model database engine designed to store and query document, graph, relational, and vector data within a single ACID-compliant platform. It functions as an AI-native data store, integrating vector search, graph traversal, and machine learning model execution directly into its query layer. By providing a unified declarative query language, the platform eliminates the need for external middleware to synchronize data across different storage models. The platform distinguishes itself through its ability to manage agent memory and complex workflows natively. It allows developer

Automa is a browser-based automation platform that enables users to build, schedule, and execute repetitive web tasks through a visual, no-code interface. By operating as a browser extension, it provides a canvas-based environment where users construct workflows by connecting functional blocks to interact with web elements, manage browser state, and process data. The platform distinguishes itself through its deep integration with the browser environment, allowing for complex orchestration such as event-driven triggers, cross-origin request handling, and the ability to package workflows as sta

Sequel is a relational database toolkit for Ruby that provides object-relational mapping, a fluent SQL query builder, and schema migration capabilities. It maps database tables to Ruby classes with support for associations, validations, lifecycle hooks, and eager loading, offering a comprehensive ORM layer for building data-centric applications. Sequel distinguishes itself through a plugin-based extension architecture that allows composable customization of models, databases, and datasets without relying on deep inheritance hierarchies. It includes a thread-safe connection pool with support f

docetl is an AI-powered document ETL tool and map-reduce orchestrator designed to transform large collections of unstructured documents into structured, queryable tables using language models. It provides a declarative pipeline framework for extracting, cleaning, and transforming data from sources such as PDFs and text files into predefined schemas. The project distinguishes itself through a semantic data integration suite that enables joining datasets and resolving duplicate entities based on embedding-based similarity. It includes an interactive prompt playground for developing and optimizi

Data engineering practice repository providing tutorials, distributed processing engines, and Python data pipeline automation scripts. The system encompasses automated data validation, distributed compute aggregation, embedded columnar querying, lazy evaluation planning, partitioned storage export, and cloud storage retrieval. The capability surface covers cloud integration and storage, data engineering and pipelines, data processing and analytics, data quality and testing, database and storage, file management, and monitoring and observability.