7 Repos
Performs data transformations and calculations directly within the database engine.
Distinct from Server-Side Data Management: Distinct from Server-Side Data Management: focuses on the execution of complex calculations on stored data rather than storage scoping.
Explore 7 awesome GitHub repositories matching data & databases · Server-Side Aggregations. Refine with filters or upvote what's useful.
This project is a feature-rich Go client library designed for interacting with Redis. It serves as a comprehensive interface for managing remote data stores, enabling developers to execute standard database commands, handle complex data structures, and perform asynchronous operations within Go applications. The library distinguishes itself through its support for advanced Redis capabilities, including connection pooling, pipelining, and transactional integrity. It provides specialized primitives for managing distributed clusters, including automated topology updates and request routing to sha
Groups and transforms stored data on the database server to perform complex calculations without transferring large datasets to the application.
Matomo is a self-hosted web analytics platform designed to track user behavior and website performance while maintaining full data ownership. It functions as a comprehensive analytics suite that captures visitor interactions and processes raw tracking logs into structured metrics, providing organizations with a centralized system for monitoring traffic patterns and engagement. The platform distinguishes itself through a strong emphasis on privacy and modularity. It includes built-in tools to anonymize visitor information and manage user consent, ensuring compliance with global data protection
Processes raw tracking logs into structured metrics using server-side background tasks.
Pygwalker is a library that transforms tabular data into interactive, drag-and-drop interfaces for exploratory analysis and visualization. It functions as a grammar-based framework that translates user interactions into declarative chart definitions, allowing for the creation of dynamic data exploration environments directly within notebooks or embedded web applications. The system distinguishes itself by offloading heavy analytical computations to backend kernels, which maintains responsiveness when visualizing large datasets. It supports the serialization of visual states into portable conf
Offloads heavy analytical computations and aggregations to backend kernels to ensure responsive data exploration.
Midday is an open-source, self-hosted financial dashboard designed for business expense management and automated bookkeeping. It functions as a centralized platform that aggregates transaction history and account balances from multiple external banking providers, allowing users to maintain full control over their sensitive financial data on private infrastructure. The platform distinguishes itself through its automated reconciliation workflows, which categorize business expenditures and generate structured financial reports to reduce manual data entry. By integrating with financial data aggre
Consolidates multi-source financial data into a unified internal database for consistent reporting and analysis.
Redis is a high-performance in-memory key-value store that functions as a distributed cache, message broker, and NoSQL database. It provides sub-millisecond read and write access to data stored in RAM and can operate as a vector database for indexing high-dimensional embeddings. The system supports a wide range of data storage and synchronization primitives, including the management of strings, hashes, lists, sets, and JSON documents. It enables real-time data operations through atomic transactions, hybrid persistence using snapshots and append-only logs, and high-availability configurations
Groups documents and executes computations like sums and reductions directly on the server.
HBase ist ein verteilter, Wide-Column-NoSQL-Speicher und Big-Data-Speicher-Engine, die für spärliche Datensätze konzipiert ist. Sie fungiert als skalierbare spaltenorientierte Datenbank, die auf dem Hadoop Distributed File System aufbaut, um Echtzeit-Lese- und Schreibzugriffe auf massive Mengen strukturierter und unstrukturierter Daten zu ermöglichen. Das System agiert als sprachübergreifendes Datenbank-Gateway und bietet Konnektivität über native Remote Procedure Calls (RPC), REST- und Thrift-Schnittstellen. Es zeichnet sich durch ein Master-Worker-Koordinationsmodell aus, das horizontale Skalierung und Fehlertoleranz über einen Cluster hinweg ermöglicht. Das Projekt deckt ein breites Spektrum an Funktionen ab, einschließlich fein abgestimmter Zugriffskontrolle über Visibility-Labels auf Zellebene, plugbarer Datenkompression und serverseitiger Datenaggregation. Es unterstützt zudem Big-Data-Analytics-Workflows durch Map-Reduce-Integration und erlaubt die Ausführung benutzerdefinierter serverseitiger Logik. Die betriebliche Überwachung wird durch System-Metrik-Tracking und Plugin-basierten Metrik-Export bereitgestellt.
Calculates summaries and statistics directly on the server to minimize data transfer to the client.
This project is a self-hosted web application designed to serve as a centralized platform for managing personal fitness data. It functions as a comprehensive activity logger and fitness data aggregator, allowing users to consolidate workout histories, health metrics, and training logs into a single private dashboard. The application distinguishes itself by automating the synchronization of workout records from third-party fitness services through secure authorization flows. It supports the ingestion of standardized fitness files, enabling the extraction of performance metrics and the visualiz
Processes raw activity logs into statistical summaries and performance trends for progress visualization.