Data Architecture Explained: Standard, Component, and Architecture Changing
Data Architecture is an IT infrastructure framework for supporting your business Data Strategy. Any data architecture aims to show the company’s infrastructure how data is acquired, transported, stored, queried, and secured.
Data architecture is the foundation of any data strategy. It is the “how” when implementing a data strategy.
In this article, we’ll look at the following:
- Business agility
- Data Architecture
- Architecture components
- Data standards
Data on Business Agility
Agility allows your company to adapt quickly to the business environment and industry. A particular kind of data architecture can enable the skill, so you can meet these business demands.
Data architecture is critical to the success of a business (and why we’ve written extensively on each data component). The world is jumping about this framework. The writings, how-to’s, and best practices are there to share the architecture and help get organizations moving toward it.
Data Architecture
a data architecture describes how data is managed from collection through to distribution, transformation, and consumption.
It sets the blueprint for data and how it flows through the data storage system.
Different data architecture on business agility
Storing a file as a .csv on a local hard drive and reading the file into Tableau on a person’s computer for analysis is a straightforward kind of data architecture.
Streaming data from a set of point-of-sale registers to accounting is another kind of architecture.
The data architecture is 100% responsible for increasing a company’s freedom to move around the world.
If agility is needed to avoid collapse during slow seasons or capitalize on the spontaneous popularity of a new product, the more advanced the data architecture is, the more capable the company is to take action.
Component of Data Architecture
Now let’s discuss components of data architecture like
- Data pipelines
- Cloud storage
- APIs
- AI & ML models
- Data streaming
- Kubernetes
- Cloud computing
- Real-time analytics
Data Standard
The overarching standards of data architecture are called data standards, and they are used in areas like data schemas and security
Data Schemas
Data schemas The architecture is in charge of establishing data standards that define what types of data will pass through it.
These standards can be met by developing a data schema. The data schema stipulates:
Every entity should be gathered. Contact information schema, for example, could include a person’s name, phone number, email address, and place of employment.
The type of data that each piece should contain. Name, for example, is text data, phone number is integer data, email is text data, and workplace is text data.
The entity’s relationship to other entities in the database, such as where it came from and where it’s going.
The majority of businesses will version their data schema. Companies will increasingly rely on relational databases as data becomes more prevalent.
Data Security
Structures and patterns also help to define the architecture’s security rules. These can be depicted in the architecture and schema by illustrating where data is passed and when it travels from point A to point B.
- Encrypting data during travel
- Restricting access to individuals
- Anonymizing data to decrease the value of the information upon receipt by receiving party
- Additional actions
Conclusion
When considering anything related to data which is arguably everything you should always consider data architecture.
