关系架构和数据集入门 gs-schemas

在此页面上:​了解创建关系架构和数据集、链接关系架构和数据集以及为编排的营销活动摄取数据的主要概念和步骤。

本指南将指导您完成以下过程:创建关系架构、为编排的营销活动配置数据集以及摄取数据。

架构 {modal="regular"}

重要概念

在编排的营销活动上下文中,数据集​是用于数据集合的存储和管理结构,通常是包含架构(行)和字段(列)的表。 成功引入Experience Platform的数据将作为数据集存储在数据湖中。

架构​表示并验证数据的结构和格式。 它提供了真实世界对象(如人)的抽象定义,并概述了该对象的每个实例中应包含哪些数据(如名称、生日等)。

数据模型​是标准化数据的概念蓝图

它描述了:

  • 实体(例如,客户、营销策划、区段)
  • 这些实体的属性(例如,客户名称、促销活动开始日期)
  • 实体之间的关系(例如,客户属于区段,营销活动针对区段)

数据模型是逻辑和概念性的,与编排的活动中的物理实施无关

在​ 关系数据模型 ​中,数据被组织到与其他表相关的表中。

  • 每个表都有行(记录)和列(属性)
  • 每个表都有一个用于唯一标识行的主键
  • 表之间的关系使用外键表示

关系架构​是关系数据模型的正式定义。

它指定:

  • 表集
  • 每个表中的列
  • 约束
  • 表之间的关系

在关系数据模型中组织架构或表就是将数据结构化为多个表。 确保每个表存储一种类型的实体/架构

➡️ 在Adobe Experience Platform文档中了解有关架构的更多信息

实施步骤 implementation

要摄取数据并创建关系架构,请执行以下步骤:

  1. 使用DDL文件🔗手动创建关系架构或

    定义数据模型的结构,包括表、属性和关系。 选择在用户界面中手动构建架构,或上传DDL文件以加快设置。

    手动创建架构时,还必须手动创建和启用数据集。 使用DDL文件时,数据集创建和启用是自动执行的。

  2. 链接架构

    在架构之间建立关系以确保数据一致性并启用跨实体查询。 例如,将忠诚度交易关联到收件人,或将奖励关联到品牌。

  3. 创建数据集

    定义架构后,您需要基于该架构创建数据集。 此数据集将用作已摄取数据的存储。

  4. 启用编排的营销活动

    数据集会存储您摄取的数据,必须为编排的营销活动启用该数据集,以确保在Adobe Journey Optimizer中可访问这些数据。

  5. 摄取数据

    将来自受支持源(如SFTP、云存储或数据库)的数据引入Adobe Experience Platform。

AI Knowledge Reference

This section contains structured knowledge intended to support interpretation, retrieval, and question answering related to this topic.

For complete understanding, this information should be combined with the documentation on this page. Neither source is intended to stand alone; the page describes the feature, while this section provides additional context that helps disambiguate terminology, intent, applicability, and constraints.

  • TL;DR: This page outlines the key concepts and implementation steps for creating relational schemas and datasets, linking them, and ingesting data to support Orchestrated campaigns.

Intents:

  • Understand the key concepts of dataset, schema, data model, relational data model, and relational schema in the context of Orchestrated campaigns.
  • Create a relational schema either manually or by uploading a DDL file.
  • Link schemas to establish relationships between entities.
  • Create a dataset based on a schema and enable it for Orchestrated campaigns.
  • Ingest data into Adobe Experience Platform from supported sources.

Glossary:

  • Dataset: a storage and management construct for a collection of data, typically a table, that contains a schema (rows) and fields (columns); data successfully ingested into Experience Platform is stored within the data lake as datasets (product-specific)
  • Schema: represents and validates the structure and format of data, providing an abstract definition of a real-world object and outlining what data should be included in each instance of that object (product-specific)
  • Data model: the conceptual blueprint of normalizing your data, describing entities, their attributes, and the relationships between entities; it is logical and conceptual, not tied to a physical implementation in Orchestrated campaigns (product-specific)
  • Relational data model: a data model in which data is organized into tables relating to other tables, where each table has rows (records) and columns (attributes), every table has a primary key to uniquely identify rows, and relationships between tables are expressed using foreign keys (product-specific)
  • Relational schema: the formal definition of the relational data model, specifying the set of tables, the columns in each table, the constraints, and the relationships across tables (product-specific)

Guardrails:

  • Organizing schemas or tables in a relational data model means structuring data into multiple tables, and each table should store one type of entity/schema.
  • When creating a schema manually, the dataset must also be created and enabled manually.
  • When using a DDL file, dataset creation and enablement are automatic.
  • The dataset must be enabled for Orchestrated campaigns to be accessible in Adobe Journey Optimizer.

Terminology:

  • Do not confuse: “schema” (an abstract definition of a real-world object) ≠ “data model” (the conceptual blueprint describing entities, attributes, and relationships) ≠ “relational schema” (the formal definition of the relational data model, specifying tables, columns, constraints, and relationships)
  • Do not confuse: creating a schema “manually” (dataset must also be created and enabled manually) ≠ creating a schema “using a DDL file” (dataset creation and enablement are automatic)

FAQ:

  • Q: What is a dataset in the context of Orchestrated campaigns? — A storage and management construct for a collection of data, typically a table, that contains a schema (rows) and fields (columns).
  • Q: What are the two ways to create a relational schema? — Create it manually in the user interface, or upload a DDL file for faster setup.
  • Q: Does creating a schema manually also create the dataset automatically? — No; when creating a schema manually, the dataset must also be created and enabled manually. When using a DDL file, dataset creation and enablement are automatic.
  • Q: Why link schemas? — To establish relationships between schemas, ensure data consistency, and enable cross-entity queries, for example linking loyalty transactions to recipients or rewards to brands.
  • Q: What are the implementation steps to ingest data and create a relational schema? — Create the relational schema (manually or using a DDL file), link schemas, create a dataset, enable it for Orchestrated campaigns, and ingest data.
  • Q: Where can data be ingested from? — Supported sources such as SFTP, cloud storage, or databases.

Related pages:

  • To create a relational schema in the user interface: relational schema manually (orchestrated/manual-schema.md)
  • To create a relational schema by uploading a DDL file: using a DDL file (orchestrated/file-upload-schema.md)
  • To establish relationships between schemas: Link schemas (orchestrated/file-upload-schema.md)
  • To create a dataset based on a schema: Create Dataset (orchestrated/manual-schema.md)
  • To enable the dataset for Orchestrated campaigns: Enable Orchestrated campaigns (orchestrated/manual-schema.md)
  • To bring data into Adobe Experience Platform: Ingest Data (orchestrated/ingest-data.md)

Personas:

  • developer: Can create a relational schema manually or by uploading a DDL file, defining tables, attributes, and relationships. Can link schemas to establish relationships between entities, such as linking loyalty transactions to recipients or rewards to brands. Can ingest data into Adobe Experience Platform from sources such as SFTP, cloud storage, or databases. Where: “Implementation steps”.
  • admin: Can create a relational schema manually in the user interface or by uploading a DDL file for faster setup. The page states that creating a schema manually requires manually creating and enabling the dataset, while a DDL file automates dataset creation and enablement. Where: “Guardrails”.

Onboarding:

  • Level: Experienced
  • First minimal step: Create a relational schema manually or by uploading a DDL file.
  • Success check: The dataset is accessible in Adobe Journey Optimizer.
  • Next: Create a relational schema manually: Configuration steps (orchestrated/manual-schema.md); Ingest data into Adobe Experience Platform: Configuration steps (orchestrated/ingest-data.md)
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