awesome-repositories.com
Blog
awesome-repositories.com

Descubre los mejores repositorios open-source con nuestra búsqueda potenciada por IA.

ExplorarBúsquedas curadasAlternativas open-sourceSoftware autohospedableBlogMapa del sitio
ProyectoAcerca deCómo clasificamosPrensaServidor MCP
Aviso legalPrivacidadTérminos
© 2026 Bringes Technology SRL·VAT RO45896025·hello@awesome-repositories.com
·

24 repositorios

Awesome GitHub RepositoriesTable Creation

Populates new tables from query results with configurable storage properties.

Distinct from Virtual Table Querying: Distinct from Virtual Table Querying: focuses on persistent table creation rather than virtual view aggregation.

Explore 24 awesome GitHub repositories matching data & databases · Table Creation. Refine with filters or upvote what's useful.

Awesome Table Creation GitHub Repositories

Encuentra los mejores repositorios con IA.Buscaremos los repositorios que mejor coincidan usando IA.
  • alibaba/dataxAvatar de alibaba

    alibaba/DataX

    17,241Ver en GitHub↗

    DataX is a distributed data integration framework and plugin-based ETL tool designed for synchronizing large datasets between heterogeneous sources and destinations. It functions as a JDBC data migration engine and offline synchronization tool, enabling the movement of data between relational databases, NoSQL stores, and object storage. The system utilizes a plugin-based connector architecture that decouples reader and writer logic, allowing it to map and transform data types across different storage engines using a standardized internal representation. This design supports heterogeneous data

    Directs data ingestion into specific leaf-level partitions within a partitioned table structure.

    Java
    Ver en GitHub↗17,241
  • prestodb/prestoAvatar de prestodb

    prestodb/presto

    16,711Ver en GitHub↗

    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

    Enables creating and populating new tables directly from query results with custom storage settings.

    Javabig-datadatahadoop
    Ver en GitHub↗16,711
  • perspective-dev/perspectiveAvatar de perspective-dev

    perspective-dev/perspective

    10,981Ver en GitHub↗

    Perspective is a columnar data analytics engine and high-performance visualization component powered by WebAssembly. It provides a system for analyzing and visualizing large or streaming datasets through interactive data grids and charts, utilizing a compiled binary to achieve near-native performance within the browser. The project distinguishes itself through a WebSocket-based data streaming interface and deep Apache Arrow integration, which minimize memory overhead when synchronizing tables between servers and clients. It acts as a remote query proxy capable of translating visualization con

    Constructs transient in-memory tables from input data to serve as the foundation for real-time analysis.

    C++analyticsbidata-visualization
    Ver en GitHub↗10,981
  • hazelcast/hazelcastAvatar de hazelcast

    hazelcast/hazelcast

    6,570Ver en GitHub↗

    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

    Maintains an up-to-date, queryable view of external database tables by applying incoming change records to distributed maps.

    Javabig-datacachingdata-in-motion
    Ver en GitHub↗6,570
  • ibis-project/ibisAvatar de ibis-project

    ibis-project/ibis

    6,574Ver en GitHub↗

    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

    Enables adding, dropping, and altering table partitions to optimize data organization and query performance.

    Pythonbigqueryclickhousedatabase
    Ver en GitHub↗6,574
  • apache/flink-cdcAvatar de apache

    apache/flink-cdc

    6,430Ver en GitHub↗

    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

    Defines new primary keys or partition keys for the downstream target table to optimize storage.

    Javabatchcdcchange-data-capture
    Ver en GitHub↗6,430
  • apache/pinotAvatar de apache

    apache/pinot

    6,098Ver en GitHub↗

    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

    Provides a unified query interface that abstracts multiple physical tables, including those distributed across different clusters or storage locations.

    Java
    Ver en GitHub↗6,098
  • apache/hiveAvatar de apache

    apache/hive

    6,012Ver en GitHub↗

    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

    Defines tables whose data is either managed by Hive or stored at a user-specified location.

    Javaapachebig-datadatabase
    Ver en GitHub↗6,012
  • greptimeteam/greptimedbAvatar de GreptimeTeam

    GreptimeTeam/greptimedb

    5,968Ver en GitHub↗

    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

    Ships a DDL syntax for declaring observability concepts and lineage directly in CREATE TABLE statements.

    Rustanalyticscloud-nativedatabase
    Ver en GitHub↗5,968
  • ckeditor/ckeditor4Avatar de ckeditor

    ckeditor/ckeditor4

    5,817Ver en GitHub↗

    CKEditor 4 is a browser-based WYSIWYG rich text editor that enables users to create and format HTML content directly in the browser. It operates on a plugin-based architecture with a configurable toolbar system, DOM-based content editing, and an event-driven lifecycle, all delivered through a CDN-based distribution model. The editor supports skin-based theming and includes a legacy plugin compatibility layer for backward compatibility. The editor distinguishes itself as a cross-platform framework that integrates natively with Angular, React, Vue, Electron, Android, and iOS environments. It of

    Enables creating and nesting tables directly within the editor for structured content.

    Rich Text Formatckeditorckeditor4contenteditable
    Ver en GitHub↗5,817
  • dotnetnext/sqlsugarAvatar de DotNetNext

    DotNetNext/SqlSugar

    5,816Ver en GitHub↗

    SqlSugar is an object-relational mapping library for .NET that translates C# and VB objects into database queries and tables without requiring raw SQL. It is designed as a multi-database ORM supporting SQL Server, MySQL, PostgreSQL, Oracle, MongoDB, ClickHouse, and other databases through a unified API, and it is compatible with .NET AOT compilation for native ahead-of-time deployment. The library distinguishes itself through high-speed bulk data operations that can insert or update millions of records in seconds using batch processing instead of row-by-row handling. It also provides multi-te

    Automatically routes inserts and queries to the correct physical table partition based on date fields.

    C#clickhousemongodbmysql
    Ver en GitHub↗5,816
  • go-pg/pgAvatar de go-pg

    go-pg/pg

    5,785Ver en GitHub↗

    pg is a PostgreSQL object-relational mapper (ORM) for Go that maps Go structs to database tables and provides a fluent query builder for constructing SQL statements programmatically. At its core, it automatically generates CREATE TABLE statements from Go struct definitions using struct tags and naming conventions, and builds queries through method chaining with placeholder-based parameter binding to prevent SQL injection. The library distinguishes itself through relation-aware join generation that automatically constructs JOIN clauses for has-one, has-many, many-to-many, and polymorphic assoc

    Creates parent tables with PARTITION BY clauses using struct tags.

    Go
    Ver en GitHub↗5,785
  • alibaba/alisqlAvatar de alibaba

    alibaba/AliSQL

    5,706Ver en GitHub↗

    AliSQL is a fork of MySQL by Alibaba that extends the relational database management system with enhancements for high performance, scalability, and enterprise-grade availability. It retains the core MySQL identity as a SQL-based database for storing, organizing, and retrieving structured data, while adding optimizations for large-scale transactional and analytical workloads. The project differentiates itself through a set of Alibaba-specific improvements, including a columnar engine for accelerating analytical queries directly on MySQL tables, and a distributed, shared-nothing NDB Cluster en

    Divides tables into smaller physical segments based on a key to improve query performance and manageability.

    C++alisqldatabaseduckdb
    Ver en GitHub↗5,706
  • google/perfettoAvatar de google

    google/perfetto

    5,558Ver en GitHub↗

    Perfetto is a platform for system-level performance tracing and analysis on Linux and Android. It combines a high-throughput trace recorder, a SQL-based query engine, and a browser-based visualizer into a single toolchain. The platform covers CPU scheduling and call-stack profiling, native and Java heap memory allocation tracking, GPU and graphics events, and system-wide counters such as CPU frequency and power consumption. The architecture decouples trace recording from offline analysis, using a compact protobuf format for event encoding and columnar storage for efficient SQL queries. The we

    Creates read-only tables from SQL queries optimized for analytic performance on trace data.

    C++
    Ver en GitHub↗5,558
  • anjoy8/blog.coreAvatar de anjoy8

    anjoy8/Blog.Core

    5,288Ver en GitHub↗

    Blog.Core es un boilerplate de backend listo para producción para construir API empresariales y microservicios utilizando ASP.NET Core. Proporciona una infraestructura fundamental para sistemas distribuidos, incluyendo herramientas para scaffolding de base de datos y la implementación de frameworks de API multi-inquilino (multi-tenant). El proyecto se distingue por la generación automatizada de la capa de datos, que produce modelos de entidad y capas de repositorio directamente a partir de esquemas de base de datos. Implementa un sistema centralizado de gestión de identidad utilizando protocolos estándar de servidor de identidad para manejar la autenticación y autorización a través de múltiples clientes y proyectos. El framework cubre una amplia gama de capacidades empresariales, incluyendo procesamiento de mensajes asíncronos mediante buses de eventos, caché de memoria distribuida y división de tráfico de base de datos de lectura-escritura. Incorpora control de acceso basado en roles con restricciones de datos departamentales y proporciona observabilidad del sistema mediante perfilado de rendimiento de API y auditoría de actividades. El sistema también incluye soporte para comunicación bidireccional servidor-cliente para notificaciones push, integración de búsqueda de texto completo y configuración centralizada de servicios.

    Implements paginated queries and operations across key-based database table partitions to optimize performance.

    C#aopautofacautomapper
    Ver en GitHub↗5,288
  • jeremyevans/sequelAvatar de jeremyevans

    jeremyevans/sequel

    5,076Ver en GitHub↗

    Sequel is a relational database toolkit for Ruby that provides object-relational mapping, a fluent SQL query builder, and schema migration capabilities. It maps database tables to Ruby classes with support for associations, validations, lifecycle hooks, and eager loading, offering a comprehensive ORM layer for building data-centric applications. Sequel distinguishes itself through a plugin-based extension architecture that allows composable customization of models, databases, and datasets without relying on deep inheritance hierarchies. It includes a thread-safe connection pool with support f

    Populates new tables from SELECT query results without explicit column type definitions.

    Ruby
    Ver en GitHub↗5,076
  • corna/me_cleanerAvatar de corna

    corna/me_cleaner

    4,982Ver en GitHub↗

    me_cleaner es un conjunto de herramientas especializadas para analizar descriptores flash, eliminar blobs de firmware y configurar apagados a nivel de hardware para motores de gestión y ejecución. Proporciona utilidades para analizar volcados de memoria BIOS, extraer regiones de firmware específicas y eliminar módulos binarios no esenciales para reducir la superficie de interacción del sistema. El proyecto se dirige específicamente a la limpieza de imágenes de firmware de Intel Management Engine y Trusted Execution Engine. Esto implica eliminar blobs binarios y modificar bits de configuración para forzar a estos subsistemas a apagarse automáticamente después del proceso de inicialización del hardware. El conjunto de herramientas cubre capacidades de modificación de firmware como eliminar bloques comprimidos de particiones de fábrica, eliminar particiones no fundamentales y recalcular tablas de particiones para mantener la integridad de la imagen.

    Updates internal offsets and sizes after removing firmware partitions to maintain image integrity.

    Python
    Ver en GitHub↗4,982
  • arroyosystems/arroyoAvatar de ArroyoSystems

    ArroyoSystems/arroyo

    4,819Ver en GitHub↗

    Arroyo is a high-performance stream processing platform built in Rust. It executes continuous SQL queries on streaming data with event-time semantics, enabling accurate windowed aggregations, joins, and stateful computations on unbounded event streams. The platform uses native Rust execution for high throughput and low latency, with periodic checkpointing for exactly-once fault tolerance and horizontal scaling across distributed workers. The system integrates deeply with Kafka for reading and writing topics with exactly-once delivery and supports change data capture (CDC) from MySQL and Postg

    Defines transient in-memory tables within a pipeline for intermediate state storage.

    Rustdatadata-stream-processingdev-tools
    Ver en GitHub↗4,819
  • supabase/supabase-jsAvatar de supabase

    supabase/supabase-js

    4,483Ver en GitHub↗

    supabase-js is a comprehensive client library designed to integrate frontend applications with a hosted backend-as-a-service. It provides a unified interface for interacting with a PostgreSQL database, identity management systems, cloud object storage, and real-time data synchronization. The library features an isomorphic client design that operates across both browser and server environments. It distinguishes itself through a type-safe approach, utilizing TypeScript to map database schemas directly to client-side definitions, and employs a PostgREST-based API to translate JavaScript calls in

    Creates and manages Iceberg tables to optimize large-scale analytical queries and data processing.

    TypeScriptclient-librarydatabaseisomorphic
    Ver en GitHub↗4,483
  • datlechin/tableproAvatar de datlechin

    datlechin/TablePro

    4,471Ver en GitHub↗

    TablePro is a cross-platform database management client designed for browsing, querying, and administering both SQL and NoSQL databases. It functions as a unified workspace that integrates a code-centric SQL editor with schema visualization tools, allowing developers to manage complex data models and execute queries across diverse database engines. The application distinguishes itself through an agentic AI integration layer that connects language models directly to database tools, enabling automated query generation, optimization, and error fixing with configurable approval gates. It features

    Performs maintenance actions on data partitions, including optimizing tables and dropping or detaching partitions.

    Swift
    Ver en GitHub↗4,471
Ant.12Siguiente
  1. Home
  2. Data & Databases
  3. Virtual Table Querying
  4. Table Creation

Explorar subetiquetas

  • Analytic Table Optimizations1 sub-etiquetaRead-only tables populated from SELECT queries with storage optimizations for analytic performance. **Distinct from Table Creation:** Distinct from Table Creation: focuses on read-only analytic-optimized tables, not general table creation.
  • Managed and External TablesTable definitions where data is either managed by the system or stored at a user-specified location. **Distinct from Table Creation:** Distinct from Table Creation: focuses on the managed vs external ownership model, not the act of populating tables.
  • Observability Metadata AnnotationsAttaches structured metadata to tables at creation time so machine consumers can identify signal type, source, and instrument kind. **Distinct from Table Creation:** Distinct from Table Creation: focuses on annotating tables with observability-specific metadata rather than the general act of creating a table.
  • Physical Table Definitions4 sub-etiquetasCreating physical tables with user-defined column types, indexes, and storage engine settings. **Distinct from Table Creation:** Distinct from general Table Creation: focuses on defining physical table schemas with custom configurations, not populating tables from query results.
  • Rich Text Table EditorsCreating and nesting tables within rich text editors for data organization and layout. **Distinct from Table Creation:** Distinct from database Table Creation: focuses on visual table editing in a WYSIWYG editor, not database schema operations.
  • Table RepartitioningsAdjusts table partition boundaries after creation to relieve hotspots and match current data distribution. **Distinct from Table Creation:** Distinct from Table Creation: modifies existing table partitions rather than creating new tables.
  • Template-BasedGenerating multiple tables from a single super-table definition using tag values. **Distinct from Table Creation:** Distinct from general Table Creation by utilizing a 'super table' template to generate related children tables.
  • Transient In-Memory Tables1 sub-etiquetaDefines temporary tables within a pipeline that can be written once and read multiple times for intermediate state. **Distinct from Table Creation:** Distinct from Table Creation: creates transient in-memory tables for pipeline intermediate state, not persistent database tables.
  • View CreationsOperations for creating virtual tables from query results that present a subset or transformation of base table data. **Distinct from Table Creation:** Distinct from Table Creation: creates virtual views rather than persistent tables.