Awesome GitHub RepositoriesUnified Batch and Stream Processing Engines

Pathway is a high-performance data processing framework designed for building unified batch and streaming pipelines. It functions as an orchestrator for complex data transformations, utilizing a differential dataflow engine to process updates incrementally. By treating static datasets and continuous event streams with identical logic, the platform ensures exactly-once processing semantics and consistent results across diverse data sources. The framework distinguishes itself through its specialized support for real-time artificial intelligence and retrieval-augmented generation. It features in

This project is a data processing engine and AI application platform designed for building production-grade machine learning workflows. It provides a unified programming model that handles both historical batch data and live stream ingestion, enabling the development of real-time ETL pipelines and scalable data transformation workflows. The framework distinguishes itself through differential dataflow execution, which propagates only changes through a pipeline rather than recomputing entire datasets. It supports distributed state management across worker nodes and utilizes incremental stream p

Apache Flink is a distributed processing engine designed for both high-throughput, low-latency data streams and finite batch workloads. It functions as a stateful stream processor and a SQL stream processing engine, providing a unified runtime to execute relational queries and event-based transformations. The system is distinguished by its ability to manage persistent operator state to ensure exactly-once processing guarantees and consistency during failures. It features specialized capabilities for complex event processing to detect temporal patterns and handles out-of-order events using eve

This project serves as a comprehensive technical reference for the architecture and design of data-intensive applications. It provides a structured analysis of the fundamental principles required to build reliable, scalable, and maintainable software systems, covering the core trade-offs inherent in modern data infrastructure. The repository explores the mechanics of distributed data management, including strategies for replication, partitioning, and achieving consensus across multiple nodes. It details the design of storage engines, indexing techniques, and transaction management models, whi

Cube is a semantic data layer that provides a unified framework for defining business metrics, dimensions, and relationships across diverse data sources. By acting as a headless business intelligence engine, it transforms raw data into a governed model that can be queried via SQL, REST, and GraphQL interfaces. This architecture ensures consistent data definitions and logic across all downstream analytical applications and reporting tools. The platform distinguishes itself through its integrated conversational AI capabilities, which allow users to explore data using natural language. It orches

BigData-Notes is a big data learning resource and data engineering knowledge base. It provides a collection of guides, technical references, and documentation focused on the installation and configuration of distributed data processing technologies. The project covers a learning path for distributed systems, including the setup of large-scale data storage and computing clusters. It specifically addresses both batch and stream processing workflows and the implementation of data APIs for interacting with distributed messaging and storage systems. The materials are organized using markdown-base

Apache Beam is a distributed data pipeline framework and unified data processing model designed to handle both bounded batch data and unbounded real-time streams. It provides a system for building scalable, data-parallel workflows that operate across compute clusters using a single programming model. The framework utilizes a cross-runner pipeline abstraction that decouples the data processing logic from the underlying execution backend, allowing the same pipeline to run on different distributed compute engines. It supports multi-language pipeline development by translating high-level code fro

Delta is a lakehouse table format that brings ACID transactions and data warehouse consistency to large scale data lakes on cloud object storage. It serves as an ACID transaction manager, coordinating atomic commits and serializable isolation for concurrent reads and writes across distributed compute engines. The project provides a multi-engine interoperability layer that uses format translation to allow diverse SQL engines and processing frameworks to read and write the same tables. It functions as a data versioning system, utilizing a transaction log to enable time travel, historical snapsh

Ibis is a portable Python dataframe library and multi-backend query engine that provides a unified interface for executing data transformations across diverse compute engines. It functions as a Python SQL expression compiler and dialect transpiler, allowing users to define data logic once and execute it across cloud warehouses, embedded databases, and distributed clusters without rewriting code. The project distinguishes itself through a database backend abstraction that decouples transformation logic from the underlying execution engine. It enables polyglot data workflows by mixing raw SQL s

Pinot is a distributed, columnar analytical database designed for high-concurrency, low-latency query processing. It functions as a real-time OLAP datastore, enabling interactive, user-facing analytics by ingesting and querying massive datasets from both streaming and batch sources. The system architecture relies on a centralized controller for cluster coordination and a distributed segment-based storage model to ensure horizontal scalability. The platform distinguishes itself through a hybrid ingestion pipeline that unifies real-time event streams and historical batch data into a single quer

StreamPark ist eine zentralisierte Managementplattform, die darauf ausgelegt ist, das Deployment, Monitoring und den operativen Lebenszyklus verteilter Stream-Processing- und Batch-Anwendungen zu koordinieren. Sie fungiert als Control-Plane und Orchestrator für Datenpipelines und bietet spezifisch Managementfunktionen für Apache Flink- und Hadoop YARN-Umgebungen. Die Plattform zeichnet sich durch einen Low-Code-Ansatz für das Task-Deployment und einen Multi-Engine-Execution-Adapter aus, der diverse Verarbeitungs-Runtimes unterstützt. Sie erleichtert das Echtzeit-Datenpipeline-Management durch die Kombination von Streaming-SQL-Analytics mit einer ressourcenbasierten Deployment-Pipeline, die Versionierung, Binär-Uploads und Savepoint-basierte Zustands-Wiederherstellung handhabt. Das System deckt ein breites Spektrum an Funktionen ab, einschließlich verteilter Job-Orchestrierung, Echtzeit-Datenintegration über vorgefertigte Connectors und Identitätsintegration via LDAP oder SSO. Es bietet zudem Observability-Tools für sekundengenaue Anwendungsüberwachung und automatisierte operative Fehlerbenachrichtigungen.