Awesome GitHub RepositoriesData Access Patterns

This project is a collection of reference implementations demonstrating recommended patterns for organizing code and managing data flow in Android applications. It provides structural examples of layered architecture, separating code into presentation, domain, and data layers to decouple business logic from data sources. The repository includes specific samples for implementing declarative user interfaces that automatically update their visual state based on underlying data changes. It further demonstrates how to manage object lifetimes and component dependencies to reduce boilerplate and sim

TypeORM is an object-relational mapper for TypeScript and JavaScript that bridges the gap between object-oriented application code and relational database tables. It provides a comprehensive data persistence layer that allows developers to define database entities using class decorators or configuration objects, enabling seamless interaction with data through object-oriented patterns. The project distinguishes itself through a flexible architecture that supports both the data mapper and repository patterns, alongside a fluent query builder that translates high-level method calls into platform

Hazelcast is a distributed data platform that combines an in-memory data grid with a stream processing engine to support real-time analytics and event-driven applications. It functions as a partitioned, distributed key-value store that replicates data across cluster nodes to provide low-latency access and high availability. The platform also serves as a distributed SQL query engine, allowing users to execute standard SQL statements against both in-memory datasets and external data sources. What distinguishes Hazelcast is its use of a distributed consensus subsystem to maintain strongly consis

This project is a reference implementation of Domain-Driven Design, Clean Architecture, and Command Query Responsibility Segregation (CQRS) patterns using the Go programming language. It serves as a sample application to demonstrate how to decouple core domain rules from infrastructure and delivery mechanisms. The system is built as a gRPC microservices architecture, utilizing type-safe communication and service contracts. It implements an event-driven architecture to manage eventual consistency and asynchronous processing, specifically employing the Outbox pattern to ensure reliable messagin

SynapseML ist eine Apache Spark Machine-Learning-Bibliothek, die für den Aufbau und die Skalierung von Machine-Learning-Workflows und Datenpipelines über verteilte Cluster hinweg entwickelt wurde. Sie dient als Framework für verteilte Machine-Learning-Pipelines und als verteilte Inferenz-Engine zur Ausführung hardwarebeschleunigter Vorhersagen und Deep-Learning-Aufgaben auf großskaligen Datensätzen. Das Projekt fungiert als Cloud-KI-Integrationsschicht, die es Benutzern ermöglicht, vortrainierte KI-Dienste für Text, Bild und Sprache innerhalb verteilter Pipelines anzuwenden. Es enthält zudem eine dedizierte Suite von Tools für verteilte Anomalieerkennung, um multivariate und Zeitreihen-Ausreißer in hochdimensionalen Daten zu identifizieren. Die Bibliothek deckt ein breites Spektrum an Funktionen ab, einschließlich verteilter Computer Vision für Gesichts- und Bildanalyse, skalierbarem Natural Language Processing für Textanalysen und Übersetzungen sowie das Training von Gradient Boosted Decision Trees. Sie bietet Tools für Ähnlichkeitssuche mittels k-Nearest-Neighbor-Modellierung, Modellerklärbarkeit durch Feature-Attribution und die Orchestrierung von Reinforcement-Learning-Workflows. Das System nutzt eine zusammensetzbare Pipeline-Architektur und unterstützt ONNX-basierte Modellinferenz für plattformübergreifende Kompatibilität.

Asterinas is a memory-safe operating system kernel designed to prevent data races and memory corruption. It functions as a Linux-ABI compatible kernel, enabling the execution of existing Linux binaries and container workloads while providing a declarative operating system distribution model. The project distinguishes itself by acting as a virtual machine container host and a confidential computing guest OS, allowing it to run within hardware-isolated Trusted Execution Environments such as Intel TDX. It implements a minimal trusted computing base by isolating unsafe low-level operations and se