4 个仓库
Checking all values within a specific column against a set of validation rules to identify errors.
Distinct from Data Validation: Specifically addresses bulk validation across a UI column, distinct from request payload validation in web development.
Explore 4 awesome GitHub repositories matching web development · Column-Level Validation. Refine with filters or upvote what's useful.
Tabulator is an interactive data table library and virtual DOM data grid used to create high-performance tables from JSON or arrays. It functions as a hierarchical data viewer and a spreadsheet interface component, capable of rendering thousands of records efficiently through viewport-based virtualization and progressive loading. The library distinguishes itself by providing a full spreadsheet interface mode with multi-sheet management, cell range selection, and bulk copy-paste capabilities. It supports complex data architectures, including nested data field mapping, expandable tree structure
Tabulator checks all cells within a specific column against defined validators to identify errors.
dlt 是一个 Python 数据摄取工具和 ETL 流水线框架,旨在从不同来源获取数据并将其持久化到结构化目标中。它作为一个模式推断引擎,可自动检测数据类型并将嵌套的 JSON 结构扁平化为关系表,将数据从源端移动到数据湖、数据仓库或向量数据库。 该项目通过 AI 驱动的流水线生成脱颖而出,利用大语言模型为 REST API 构建提取代码和连接器。它还支持多模态向量存储和向量数据库的专门填充,以支持 AI 和机器学习应用。 该框架涵盖了广泛的功能,包括自动化模式演进、通过状态跟踪进行增量数据加载,以及通过强制执行数据契约进行数据质量验证。它提供了用于关系数据规范化、加载前后转换的工具,以及针对 SQL 数据库和云对象存储的多种目标适配器。 可观测性通过流水线执行仪表板、列血缘跟踪以及使用基于内容的哈希进行模式版本验证来处理。
Defines column-level validation rules to inspect schemas and diagnose data integrity failures during execution.
Amundsen is a data catalog and discovery platform that provides a centralized directory for indexing tables and dashboards. It functions as a metadata management system and search engine, allowing users to locate and understand available data assets across diverse distributed sources. The platform includes capabilities for data lineage tracking to map the origin and movement of datasets between systems. It also serves as a data profiling tool, calculating distribution and quality statistics for individual table columns to provide automated insights into the nature of the data. The system man
Generates distribution and quality statistics for individual table columns to understand dataset nature.
Replibyte is a tool that automates the lifecycle of database snapshots for non-production environments, handling the export, anonymization, subsetting, and restoration of data. It is designed to support privacy-compliant development workflows by replacing sensitive production data with synthetic values and extracting consistent subsets of rows while preserving referential integrity. The tool operates through a configurable pipeline defined in a YAML file, orchestrating stages such as dump, anonymize, subset, and restore. Each operation runs as an isolated, ephemeral container job, and snapsho
Replaces sensitive database column values with synthetic data during restore for privacy compliance.