Awesome GitHub RepositoriesData Partitioning

Dieses Projekt ist eine umfassende Bildungsressource und ein Studienleitfaden, der sich auf die Architektur verteilter Systeme und das Design von Backend-Infrastrukturen konzentriert. Es bietet einen strukturierten Lehrplan zur Beherrschung der Prinzipien von Skalierbarkeit, Zuverlässigkeit und Leistung, die für den Entwurf komplexer Softwaresysteme erforderlich sind. Das Repository zeichnet sich durch einen methodischen Ansatz zur Vorbereitung auf technische Vorstellungsgespräche aus, der Entwurfsmuster, architektonische Kompromisse und Tools für räumliche Wiederholungen integriert, um Nutzern das Behalten komplexer Konzepte zu erleichtern. Es betont die einschränkungsgesteuerte Analyse und lehrt Nutzer, wie sie konkurrierende Anforderungen wie Latenz, Konsistenz und Verfügbarkeit beim Entwurf von Architekturen bewerten können. Der Inhalt deckt ein breites Spektrum an Systemdesign-Fähigkeiten ab, einschließlich Strategien für die Datenbankskalierung, Verkehrsmanagement und Infrastrukturoptimierung. Es werden Techniken für horizontale Skalierung, mehrschichtiges Caching, asynchrone Kommunikation und Service-Discovery detailliert beschrieben, während gleichzeitig Frameworks für die Durchführung von Ressourcenschätzungen und Kapazitätsplanungen bereitgestellt werden. Die Dokumentation ist als Studienleitfaden organisiert und bietet einen systematischen Pfad durch die Grundlagen des Backend-Engineerings und des großskaligen Systemdesigns.

Milvus is a specialized vector database engine designed for the indexing, management, and high-speed similarity retrieval of high-dimensional vector embeddings. It functions as a similarity search engine capable of identifying nearest neighbors within large-scale vector spaces, supporting the storage and retrieval of billions of data points while maintaining consistent performance. The system utilizes a distributed architecture that decouples storage, query, and coordination into independent services, allowing for horizontal scaling across clusters. It employs a global indexing mechanism that

This project is a comprehensive educational resource focused on the principles, patterns, and trade-offs required to design scalable, reliable, and high-performance distributed systems. It provides a structured curriculum that covers the fundamental architectural strategies necessary for building modern software infrastructure, ranging from high-level system decomposition to low-level networking and data management. The repository distinguishes itself by offering deep dives into complex architectural patterns, such as microservices-based decomposition, event-driven communication, and command-

Qdrant is a high-performance vector similarity database designed to store, index, and search high-dimensional vectors alongside structured metadata. It functions as a distributed search engine that manages large-scale data clusters, providing low-latency retrieval and complex filtering capabilities. The system is built to serve as a specialized middleware layer, connecting machine learning pipelines and AI agents to persistent storage for intelligent information retrieval and recommendation tasks. The platform distinguishes itself through advanced retrieval techniques, including support for h

RocksDB is a high-performance, embeddable persistent key-value library and storage engine based on Log-Structured Merge-trees. It is designed to provide durable storage for large-scale datasets, integrating directly into applications to manage data on flash and RAM-based hardware. The engine is distinguished by its focus on minimizing read and write amplification through multi-threaded compaction and custom memory allocators. It features specialized optimizations for flash storage, including support for zoned block devices, and provides the ability to extend store behavior via external plugin

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

This project is a comprehensive educational resource and technical documentation suite for learning and developing deep learning models. It serves as an open-source textbook, implementation manual, and framework tutorial designed to guide users through the mathematical foundations and practical application of neural networks. The resource provides detailed instructional content on building various model architectures, including convolutional and recurrent neural networks. It includes a dedicated distributed training guide and a learning path that covers the fundamentals of tensors, automatic

Presto is a distributed SQL query engine designed for high-performance analytical processing across heterogeneous data sources. It functions as a data federation platform and massively parallel processing engine, allowing users to execute interactive queries against diverse storage systems without requiring data migration. By mapping remote metadata and structures to a unified relational namespace, it enables seamless cross-platform analysis through a standard SQL interface. The engine distinguishes itself through a pluggable connector architecture and a shared-nothing distributed processing

Dagster is a data orchestration platform designed to manage the entire lifecycle of data assets through declarative modeling and version-controlled code. It functions as a workflow engine that treats data assets as first-class primitives, allowing teams to define, schedule, and monitor complex pipelines while maintaining clear visibility into lineage, dependencies, and data quality. The platform distinguishes itself by using a code-as-configuration framework that enables standard software engineering practices, such as unit testing and local mocking, to be applied directly to data workflows.

Druid is a distributed columnar store and online analytical processing database designed for real-time analytics. It functions as a SQL analytics platform and a streaming data ingestion engine, allowing for the analysis of large datasets with low latency to support interactive dashboards and high-concurrency operational workloads. The system integrates a streaming data ingestion engine that loads information via batch or streaming processes to enable immediate analysis of arriving data. It provides high-performance analytical processing to execute slice-and-dice queries on massive data volume

Dask ist ein Framework für paralleles Rechnen und ein verteilter Task-Scheduler, der darauf ausgelegt ist, Python-Data-Science-Workflows von einzelnen Maschinen auf große Cluster zu skalieren. Es fungiert als Cluster-Ressourcenmanager, der die Berechnungslogik orchestriert, indem Aufgaben und deren Abhängigkeiten als gerichtete azyklische Graphen dargestellt werden. Diese Architektur ermöglicht es dem System, die Verteilung von Workloads auf verfügbare Hardware zu automatisieren und gleichzeitig komplexe Ausführungsanforderungen zu verwalten. Das Projekt zeichnet sich durch eine Lazy-Evaluation-Engine aus, die Datenoperationen verzögert, bis sie explizit angefordert werden, was eine globale Graphoptimierung und effiziente Ressourcenzuweisung ermöglicht. Es integriert speicherbewusstes Data-Spilling, um Systemabstürze bei der Verarbeitung von Datensätzen zu verhindern, die den verfügbaren Speicher überschreiten, und nutzt Task-Graph-Fusion, um Sequenzen von Operationen in einzelne Ausführungsschritte zu kombinieren, wodurch Scheduling-Overhead und Inter-Node-Kommunikation minimiert werden. Die Plattform bietet eine umfassende Oberfläche für die Datenanalyse im großen Maßstab, einschließlich Unterstützung für verteiltes maschinelles Lernen, Integration in das Hochleistungsrechnen und parallele Datenverarbeitung. Sie bietet umfangreiche Werkzeuge für das Cluster-Lebenszyklusmanagement, Performance-Profiling und die Echtzeitüberwachung der Aufgabenausführung. Benutzer können diese Umgebungen über verschiedene Infrastrukturen hinweg bereitstellen, einschließlich lokaler Hardware, Cloud-Anbietern, containerisierten Systemen und Hochleistungsrechner-Clustern.

Electric is a Postgres data synchronization engine and replication proxy designed to enable local-first software. It replicates data from Postgres databases to client-side stores in real time using logical replication, allowing applications to maintain a local embedded database for offline access and low-latency updates. The system distinguishes itself by using shapes to filter and authorize specific subsets of database rows and columns before streaming them to clients or edge workers. It further supports multi-user collaboration by integrating a conflict-free replicated data type framework t

Cassandra is a distributed NoSQL database and wide-column store designed for high availability and linear scalability. It functions as a fault-tolerant distributed system that utilizes an LSM-tree storage engine to optimize write throughput and manage massive datasets. The system is a CQL-compliant database, using a structured query language to manage and retrieve tabular data stored across multiple nodes. It organizes information into rows and columns based on a flexible schema and primary keys. The project provides capabilities for horizontal database scaling, distributed data partitioning

pprof is a tool for visualizing and analyzing performance profiling data. It converts sampled call stacks into a directed graph rendered as an SVG, enabling visual identification of execution hotspots. The tool also parses Linux perf.data files, converting them into an internal profile representation for further analysis. Beyond visualization, pprof provides a command-line REPL for interactive exploration of profiling data, allowing users to filter, refine, and query performance information on the fly. It generates sorted text reports that highlight the most resource-intensive call stacks, an

This project is an open-source software development kit and framework for implementing the Matter smart home standard. It provides a universal IPv6-based application layer and a cluster-based data model to ensure interoperability between diverse smart home devices and controllers. The system is distinguished by its multi-transport network abstraction, which maps Bluetooth LE, Thread, and Wi-Fi implementations to a common layer. It includes specialized tooling for secure device commissioning via QR codes and NFC, as well as a comprehensive over-the-air firmware update system for distributing s

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

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

Apache Hive is a SQL-on-Hadoop data warehouse that enables querying and managing petabytes of data stored in distributed storage such as HDFS and cloud storage services. It provides a familiar SQL interface for batch analytics and reporting, supported by a core set of components including the HiveServer2 Thrift service for remote query execution, the Hive Metastore Service for central metadata management, the Hive ACID Transaction Engine for concurrent read-write operations, and the Hive LLAP Interactive Engine for low-latency analytical processing. The WebHCat REST API offers an HTTP interfac

GreptimeDB is a distributed, open-source time-series database built for unified observability. It stores and queries metrics, logs, and traces together in a single columnar engine, supporting both SQL and PromQL for analysis. The database is designed as a Kubernetes-native operator with a decoupled compute and storage architecture, enabling horizontal scaling and multi-region deployment. What distinguishes GreptimeDB is its role as a multi-protocol ingestion gateway, accepting data through OpenTelemetry, Prometheus Remote Write, InfluxDB, Loki, Elasticsearch, Kafka, and MQTT protocols without

Algodeck is an open-source collection of flash cards designed for reviewing algorithms, data structures, and system design concepts, specifically curated for technical interview preparation. The project organizes knowledge into atomic question-and-answer pairs and incorporates spaced repetition scheduling to optimize long-term memory retention. The flash card catalog covers a broad range of computer science topics, including classic sorting algorithms like quicksort and mergesort, data structure operations for arrays, trees, heaps, tries, and graphs, as well as bit manipulation techniques for