Data architecture is a sub-discipline of IT architecture. The data architecture takes a holistic view of fundamental structures and processes related to data or information. In this article, we’ll take a look at what it’s all about and review some common mistakes people make when working with data architecture!
The data architect is responsible for defining the future situation, alignment during development, and the necessary follow-up to ensure that the design is done according to the original architectural specifications. It considers these data systems as a strategic resource of the organization, representing them independently of the processes of the different units that use them. Its main purpose is to design data structures in an organized way, thus providing a basis for the construction of flexible and integrated information systems.
Data is a common good, users need adequate access to data and security is essential. Shared data assets such as product catalogs, fiscal calendar dimensions, and KPI definitions require a common vocabulary. Reduce the number of data moves required to reduce costs, and optimize business agility.
What is Data Architecture?
Data architecture is the process of designing, organizing, and managing the data within an organization. It enables businesses to extract value from their data and make better decisions. Data architecture should be tailored to the individual company’s needs and should include a detailed understanding of the business’ customers, products, and services.
A data architecture should establish data standards for all of its data systems as a vision or model of possible interactions between those systems. Data integration, for example, must depend on data architecture standards. A data architecture partially describes the structures between the data used by a company and those used by the software applications of the different computers it uses. Data architecture makes it possible to create a map of a company’s data, applications, and sites. Essential to the realization of a project, data architecture describes how data is processed, stored and used in a system. It provides criteria for data processing that make it possible to design data flows and also to control the flow of data in the system.
Data architecture can be broken down into four key areas: data modeling, database design, data access, and data warehousing. Each of these areas has its own set of requirements and should be handled by a separate team member.
Data modeling involves creating a conceptual model of the data. This helps you understand how your data is organized and enables you to create tables to store your information.
Database design involves creating the physical structure of your database. This includes setting up tables and deciding how each row will be stored.
Data access refers to how you will interact with your database. This includes deciding which SQL queries will be used to retrieve information from your database and configuring your application’s response time settings.
Data warehousing refers to storing all of your company’s data in one place so that it can be easily accessed by management.
Different Types of Data Architectures
There are many different types of data architectures.
- Federated architecture : The objective of creating a federated database is to give users a single view of the data present on several systems that are a priori heterogeneous. This is a typical problem when concentrating or merging companies: making the different systems coexist while allowing them to interoperate in a harmonious way.
- Ansi/Sparc architecture : The principle of the Ansi/Sparc Architecture is to separate into three distinct levels in order to be able to easily differentiate the physical implementation and the logical representation of the data as well as the possibility of creating external schemas that are used to create virtual subbases.
- Distributed architecture : Distributed Architecture is a system that manages a collection of logically linked BDs, spread across different sites by providing a means of access that makes distribution transparent.
- BD Scalable Architecture (BDS) : The principle of this architecture lies in multiplying the data processing sites on the same network. In this way by accumulating resources (Memory, disk space …) it is possible to process in parallel a large amount of information.
- Simplified architecture : It offers an alternative to the complexity of the Lambda Architecture. Indeed, this architecture merges the real-time and batch layers. Therefore, it is possible to use the Data Stream brick as a place of persistence. In practice, retention on this type of system is limited, only the Kafka tool allows to store a deep history of data using a cluster sized for this use case. The data is therefore routed and stored via tools such as Apache Kafka before being processed and sent to the “Serving Layer”.
Examples of different projects
Data architectures have been used in a variety of different projects. Whether it is for a small business, an online application, or even a product, data architecture can help make sure that the project is successful.
A project that could benefit from a data architecture is the creation of an online store. In order to design the store correctly, it is important to understand which products are being sold and how many customers are interested in each product. Additionally, it is important to figure out how much space the store will need and what type of hosting would be best for the store.
No matter what type of project you are working on, having a data architecture in mind can help make sure that everything runs smoothly.
Features and related technologies of a data architecture
A data architecture can provide benefits for your business, such as improved data quality, faster data access, and reduced complexity. When you design your data architecture, you need to consider the following:
- Data flow: How data is accessed and processed within your organization?
- Data structure: How your data is organized?
- Data definition: How you identify and describe your data?
Data flow determines how data is accessed and processed. You need to decide which systems access data and how it is processed within those systems. For example, if you have a customer management system that processes orders, you need to decide which fields in an order record are used by the system and which fields are used for processing (such as shipping information).
Data structure determines how your data is organized. Your data structure should reflect the way you use your data. For example, if you store customer information in a table called Customers, you should create tables for each type of information that needs to be stored.
Data type is how information is stored and manipulated in the system. It can be a text field or a number field, for example. You also should consider how you want to store dates and currency values.
Data validation controls what should be put into your data fields and what it means when it’s entered into a field.
Security determines who has access to your system, who can update the data, and who can view the data. For example, if you need to give different employees permission to use different systems on your network, then you might have separate accounts for each employee that grant each person access to only those areas of data that are assigned as his or her responsibilities in the company.