4 Repos
Processes that filter and combine raw data responses into refined domain-specific models for UI consumption.
Distinct from Combined Filtering and Transformation: Different from stream filters or raw retrieval as it focuses on domain model mapping for the UI layer.
Explore 4 awesome GitHub repositories matching data & databases · Domain Model Transformations. Refine with filters or upvote what's useful.
This project is a collection of official plugin packages and a native integration library designed to provide a consistent interface for accessing hardware and software functionality across different mobile and desktop platforms. It serves as a native platform bridge, enabling cross-platform applications to invoke native code and manage operating system dependencies. The project utilizes a federated plugin architecture, splitting plugins into common interfaces and separate platform implementations to allow for independent development and extension. It further supports native integration throu
Filters and combines API responses into refined domain models required by the user interface.
Orange3 is a visual data mining platform that provides an interactive canvas for building data analysis workflows without writing code. At its core, it offers a widget-based visual programming environment where users connect configurable components to perform data preprocessing, machine learning model training, statistical evaluation, and interactive visualization. The platform is built on NumPy-backed data tables with domain descriptors that define variable names, types, and roles, and includes a lazy SQL query proxy for working with database tables without loading all data into memory. The
Transforms data between different domain descriptors by remapping variables and converting values.
dlt ist ein Python-Tool zur Datenaufnahme und ein ETL-Pipeline-Framework, das darauf ausgelegt ist, Daten aus verschiedenen Quellen abzurufen und in strukturierten Zielen zu speichern. Es fungiert als Schema-Inferenz-Engine, die automatisch Datentypen erkennt und verschachtelte JSON-Strukturen in relationale Tabellen flacht, wobei Daten von Quellen in Lakehouses, Warehouses oder Vektordatenbanken verschoben werden. Das Projekt zeichnet sich durch KI-gestützte Pipeline-Generierung aus, die Large Language Models nutzt, um Extraktionscode und Konnektoren für REST-APIs zu erstellen. Es unterstützt zudem multimodale Vektorspeicherung und die spezialisierte Befüllung von Vektordatenbanken zur Unterstützung von KI- und Machine-Learning-Anwendungen. Das Framework deckt ein breites Spektrum an Funktionen ab, einschließlich automatisierter Schema-Evolution, inkrementellem Datenladen mittels Statusverfolgung und Datenqualitätsvalidierung durch die Durchsetzung von Datenverträgen. Es bietet Tools für relationale Datennormalisierung, Pre- und Post-Load-Transformationen sowie eine Vielzahl von Ziel-Adaptern für SQL-Datenbanken und Cloud-Objektspeicher. Die Observability wird durch Pipeline-Ausführungs-Dashboards, Spalten-Lineage-Tracking und Schema-Versionsverifizierung mittels inhaltsbasierter Hashes gehandhabt.
Converts raw data into a structured ontology using dimensional modeling and transformation functions.
fast-json-stringify is a high-performance JSON serialization library that uses JSON Schema to compile optimized functions for converting JavaScript objects into strings. It serves as a schema-based JSON serializer that generates specialized serialization logic to outperform standard stringification methods. The project provides capabilities to export standalone serialization code to files for direct execution and supports schema reference resolution to eliminate logic duplication. It allows for the deactivation of character escaping for trusted data to reduce overhead and includes specific op
Ensures outgoing data adheres to specific types and requirements during conversion to strings.