2 个仓库
Awesome GitHub RepositoriesDimensional Data Modeling
Techniques for structuring data into facts and dimensions to optimize for analytical querying and reporting.
Distinct from Dimensional Data Slicing: Closest candidates focus on slicing, indexing, or visualization, whereas this is about the structural design of the data model itself.
Explore 2 awesome GitHub repositories matching data & databases · Dimensional Data Modeling. Refine with filters or upvote what's useful.
Awesome Dimensional Data Modeling GitHub Repositories
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- dlt-hub/dlt
dlt-hub/dlt
5,472在 GitHub 上查看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。
Applies dimensional modeling to transform loaded data into structured formats.
Pythondatadata-engineeringdata-lake - apache/kylin
apache/kylin
3,765在 GitHub 上查看Kylin is a distributed OLAP engine designed for executing fast SQL queries on massive datasets. It utilizes multi-dimensional data cubes to pre-calculate data aggregates, enabling sub-second response times for large-scale analytical queries and big data analytics. The system focuses on large-scale data warehousing and multi-dimensional data modeling. It allows for the organization and querying of vast amounts of structured data to support business intelligence and reporting workflows through distributed SQL querying.
Structures data into facts and dimensions via cubes to optimize for analytical querying and reporting.
Javakylin
