Awesome GitHub RepositoriesStreaming Source and Sink Integration

This project is a collection of educational resources and reference implementations for the Apache Flink stream processing framework. It provides a learning resource focused on mastering distributed stream processing through implementation guides, performance tuning tutorials, and practical examples. The repository features detailed walkthroughs for building real-time data pipelines using the DataStream and Table APIs. It includes specific integration examples for connecting Apache Flink with Kafka brokers and Elasticsearch indices, as well as reference implementations for real-time deduplica

Nebula is a distributed graph database designed for storing and querying massive volumes of interconnected vertices and edges across a horizontally scalable cluster. It functions as a Kubernetes-native database and a distributed graph analytics engine, utilizing a Raft-based distributed store to ensure strong consistency and high availability. The system features an OpenCypher query engine for performing complex graph traversals and pattern matching. It distinguishes itself with a decoupled compute-storage architecture and a shared-nothing distributed design, allowing query processing and dat

This project is a streaming data integration framework that captures real-time database changes and synchronizes them with downstream systems. It operates as a distributed streaming ETL and database synchronizer, reading database logs and snapshots to propagate row-level modifications to target sinks. The system supports declarative data integration, allowing users to define source-to-sink data flows using SQL or YAML configurations. It distinguishes itself by automating schema evolution to maintain synchronization when source structures change and ensuring exactly-once delivery and processin

lakeFS 是一个数据湖版本控制系统，为存储在对象存储中的大型数据集提供类似 Git 的分支和提交功能。它作为一个版本控制层，支持创建不可变快照、原子提交和零拷贝分支，从而在不复制物理文件的情况下为数据实验创建隔离环境。 该系统充当 S3 兼容的存储网关和 Iceberg REST 目录，允许标准云存储协议和兼容客户端管理版本化表。它通过使用事件驱动的钩子系统在更改合并到生产环境之前根据治理策略验证数据集，从而充当数据质量守门人。 该平台涵盖了广泛的数据治理功能，包括 Pull Request 协作、基于角色的访问控制和数据血缘追踪。它为工作流编排、机器学习管线和各种大数据计算引擎提供了集成，支持多云存储连接以及通过 SSO 和 SCIM 进行身份同步。 该软件可以使用二进制文件、容器或 Helm Chart 安装，以便在 Kubernetes 上部署。

oboe.js 是一个流式 JSON 解析器和 HTTP 流客户端，专为增量数据处理而设计。它允许应用程序在网络请求或可读流到达时，在整个下载完成之前对单个 JSON 对象进行处理。 该库作为 JSON 流转换器，支持在传输过程中对节点进行修改、过滤和映射。它具有基于路径的节点选择功能，可在匹配特定模式时触发回调，并能在找到所需数据后提前终止网络连接以节省带宽。 为了处理超出系统可用内存的数据集，该库利用基于流的内存管理来丢弃已处理的节点。它还支持通过长连接将历史记录与实时更新合并，并提供在连接中断时恢复部分数据的能力。

Kotlinx-io is a multiplatform library designed for input and output operations, providing a unified interface for streaming data, managing byte buffers, and interacting with local filesystems. It serves as a cross-platform abstraction layer that standardizes how applications handle data movement across different operating systems and hardware architectures. The library distinguishes itself by providing high-performance tools for both mutable and immutable byte sequences. It utilizes segmented memory pools and direct memory access to minimize allocation overhead and prevent unnecessary data co

Data warehouse learning is a reference implementation of a real-time stream processing system and open-source data lakehouse architecture. It combines stream processing engines, open lakehouse formats, and analytical data warehouses into a complete e-commerce data warehouse system built for both offline and real-time analytics pipelines. The project implements hybrid data warehouse architectures utilizing multi-layer storage models and stream-batch processing pipelines. It features change data capture pipelines that stream database transaction logs into messaging systems, progressive data tra