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GC Enterprise Architecture/Framework/DataGuide (view source)
Revision as of 15:59, 22 February 2023
, 15:59, 22 February 2023no edit summary
[[File:Generic Data Architecture Model.png|800px|center]]
[[File:Generic Data Architecture Model.png|800px|center]]
Data Product: A service or device that collects, processes, and stores data for a business. Data producers also monitor the data obtains to ensure the quality of the data asset.
Data Source: A data source is made up of fields and groups. In the same way that folders on your hard disk contain and organize your files, fields contain the data that users enter into forms that are based on your form template, and groups contain and organize those fields.
Data Integration: Data integration is the process for combining data from several disparate sources to provide users with a single, unified view.
Integration is the act of bringing together smaller components into a single system so that it's able to function as one. And in an IT context, it's stitching together different data subsystems to build a more extensive, more comprehensive, and more standardized system between multiple teams, helping to build unified insights for all.
Data Lake: A data lake is a centralized repository that ingests and stores large volumes of data in its original form. The data can then be processed and used as a basis for a variety of analytic needs. Due to its open, scalable architecture, a data lake can accommodate all types of data from any source, from structured (database tables, Excel sheets) to semi-structured (XML files, webpages) to unstructured (images, audio files, tweets), all without sacrificing fidelity. The data files are typically stored in staged zones—raw, cleansed, and curated—so that different types of users may use the data in its various forms to meet their needs. Data lakes provide core data consistency across a variety of applications, powering big data analytics, machine learning, predictive analytics, and other forms of intelligent action.
Data Mart: A data warehouse is a system that aggregates data from multiple sources into a single, central, consistent data store to support data mining, artificial intelligence (AI), and machine learning—which, ultimately, can enhance sophisticated analytics and business intelligence. Through this strategic collection process, data warehouse solutions consolidate data from the different sources to make it available in one unified form.
A data mart (as noted above) is a focused version of a data warehouse that contains a smaller subset of data important to and needed by a single team or a select group of users within an organization. A data mart is built from an existing data warehouse (or other data sources) through a complex procedure that involves multiple technologies and tools to design and construct a physical database, populate it with data, and set up intricate access and management protocols.
Data Consumers: Data consumers are services or applications, such as Power BI or Dynamics 365 Customer Insights, that read data in Common Data Model folders in Data Lake Storage Gen2. Other data consumers include Azure data-platform services (such as Azure Machine Learning, Azure Data Factory, and Azure Databricks) and turnkey software as a service (SaaS) applications (such as Dynamics 365 Sales Insights). A data consumer might have access to many Common Data Model folders to read content throughout the data lake. If a data consumer wants to write back data or insights that it has derived from a data producer, the data consumer should follow the pattern described for data producers above and write within its own file system.
Outlined in the points below, are objectives to be fulfilled in order to maintain information architecture standards.
Outlined in the points below, are objectives to be fulfilled in order to maintain information architecture standards.
