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This article mainly explains "how to design the data architecture of the database". Interested friends may wish to take a look. The method introduced in this paper is simple, fast and practical. Let's let the editor take you to learn how to design the data architecture of the database.

1. Conceptual data Architecture Design based on Business process and Operation

In modern IT, business processes are supported and driven by data entities, data flows and business rules applied to data. Therefore, data architects need to have in-depth business knowledge, including expertise in specific industries such as finance, marketing, products, and business processes, such as health, insurance, manufacturers, and retailers.

He is then able to build the correct enterprise data blueprint by designing data entities and taxonomies that represent each business domain, as well as data flows under business processes. In particular, the following aspects need to be considered and planned at this conceptual stage:

Core data entities and data elements, such as data about customers, products, and sales.

Output data required by customers and customers.

The source data to be collected, transformed, or referenced to generate output data.

Ownership of each data entity and how to use and allocate it based on business use cases.

The security policy to apply to each data entity.

Relationships between data entities, such as referential integrity, business rules, execution order.

Standard data classification and classification.

Standards for data quality, operations, and service level agreements (SLA).

The conceptual level of the design consists of the underlying data entities that support each business function. Blueprints are critical to the successful design and implementation of enterprise and system architectures and their future extensions or upgrades.

In many organizations, this conceptual design is often embedded in business analysis driven by a single project, without a guided approach from the perspective of enterprise end-to-end solutions and standards.

two。 Logical level data architecture design

This design is sometimes called data modeling because you want to consider which type of database or data format to use. It links business requirements to basic technology platforms and systems. However, given the role of the data modeler, most organizations design data modeling only in specific databases or systems.

By considering the standards applicable to each database or system and the data flow between these data systems, an integrated approach should be adopted to develop a successful data architecture. In particular, the following five areas require collaborative design:

(1) naming conventions and data integrity

Naming conventions for data entities and elements should be applied consistently to all databases. Similarly, if the same data must reside in multiple databases, the integrity between the data source and its references should be enhanced. Ultimately, these data elements should belong to data entities in the conceptual design of the data architecture, which can then be updated or modified accurately according to business requirements.

(2) data archiving / retention strategy

If data archiving and retention strategies are often considered or established in the final stage of production, it will lead to a waste of resources, inconsistent data states between different databases, and poor performance of data queries and updates. To enhance data integrity, data architects define data archiving and retention policies in a data architecture based on operating standards.

(3) Privacy and security information

Privacy and security have become important considerations in logical database design. Although the conceptual design has defined which data components are sensitive information, the logical design should protect confidential information in databases with restricted access, restricted data replication, specific data types, and secure data flows to protect information.

(4) data replication

Data replication is a key factor to take into account three goals:

1) High availability.

2) avoid the performance of transmitting data over the network.

3) low coupling to minimize downstream effects.

However, too much data replication can lead to confusion, poor data quality and poor performance. Any data replication should be checked by the data architect and certain principles and disciplines should be followed.

(5) data flow and pipelines

At this level, the flow of data between different database systems and applications should be clearly defined. Again, this process is consistent with the process mentioned in the business process and data architect concept level. In addition, the frequency of data intake, data conversion in the pipeline, and data access patterns for output data should be considered in the integrated view of the logical design. For example, if the upstream data source is real-time and the downstream system is mainly used for data access with reindexed aggregate information (for example, it is expensive for frequent updates and inserts), you need to design a data pipeline between the two. to optimize performance.

Continuous governance is the key to the success of data architecture

Because the data schema reflects and supports the business process, the data schema may change when the business process changes. As the underlying database system changes, the data schema needs to be adjusted. Therefore, the data architecture is not static, but needs to be continuously managed, enhanced, and audited. Therefore, data governance should be used to ensure that the enterprise data architecture is properly designed and implemented when starting each new project.

At this point, I believe you have a deeper understanding of "how to design the data architecture of the database". You might as well do it in practice. Here is the website, more related content can enter the relevant channels to inquire, follow us, continue to learn!

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