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 is an Apache Spark machine learning library designed for building and scaling machine learning workflows and data pipelines across distributed clusters. It serves as a distributed machine learning pipeline framework and a distributed inference engine for executing hardware-accelerated predictions and deep learning tasks on large-scale datasets. The project functions as a cloud AI integration layer, allowing users to apply pretrained artificial intelligence services for text, vision, and speech within distributed pipelines. It also includes a dedicated suite of tools for distributed

Asterinas 是一个内存安全的操作系统内核，旨在防止数据竞争和内存损坏。它作为一个兼容 Linux-ABI 的内核，能够运行现有的 Linux 二进制文件和容器工作负载，同时提供声明式的操作系统分发模型。 该项目的特色在于充当虚拟机容器宿主机和机密计算客户机操作系统，使其能够在 Intel TDX 等硬件隔离的可信执行环境（TEE）中运行。它通过隔离不安全底层操作实现了最小化的可信计算基（TCB），并将核心内核机制与特定策略实现分离开来。 该系统涵盖了广泛的能力，包括物理和虚拟内存管理、对称多处理（SMP），以及针对各种 CPU 架构的硬件抽象。它还包括对安全容器运行时的支持、一套全面的网络和 Socket 原语，以及用于内核编译和仿真的专用工具链。 该项目支持在 x86-64、RISC-V 64 和 LoongArch 64 平台上进行多架构部署。