Types of Data Architecture
Business agility depends on a well planned data architecture as it allows businesses to make data-driven choices and swiftly adjust to changing business contexts. There are 2 approaches on which types of data architecture are categorized.
- Centralized Data Architecture: In this framework, all data (being stored and managed) are done in a central repository, which might be a data warehouse. It presents a single coherent view, but it is possible that these unitary solutions can face scalability hurdles. By combining data from many sources into one place, this method attempts to facilitate data management, analysis, and integrity maintenance. A common association of centralized data architecture is with conventional monolithic data infrastructure, which manages data storage, cleansing, optimization, output, and consumption from a single central place.
- Decentralized Data Architecture: In a decentralized data architecture, data processing and storage are distributed among many nodes or systems, enabling each domain to handle its own data while guaranteeing that it is still available to the whole business. In contrast, centralized data architecture gathers and controls all data in one central area. Data is spread all over different servers or databases in this scenario with each department or business unit independently, within the system, managing its data.
Common Types of Data Architecture are:
1. Cloud architecture: Cloud architecture is the combination of technological elements to create a cloud that allows sharing over a network and resource pooling using virtualization technologies. This architecture comprises of a network, servers and storage, a cloud-based delivery mechanism, and a front-end platform (client or device used to contact the cloud). These technologies working together provide a cloud computing architecture that enables programs to function and gives end users access to cloud resources.
2. Event-driven architecture (EDA): Event-driven architecture (EDA) is a software design paradigm that allows for flexible connection between system components. It consists of tiny, decoupled services that publish, consume, or route events representing state changes or modifications. This design pattern is contemporary, scalable, and robust, allowing for more innovation and user experience improvement. In an event-driven architecture, there are both producers and consumers. Producers identify events and produce messages, which are subsequently forwarded to event consumers via an event channel and processed. The event processing system responds to the event message, resulting in an action downstream.
3. Hybrid architecture: Hybrid architecture is combination of different architectural styles, systems, or approaches to create a unified and efficient solution. Hybrid architecture is used in various domains such as:
- Cloud computing: It is the exchange of applications and data across public and private clouds via the combination of these two architectures. Taking use of the economics of cloud-based storage and processing power, this strategy allows companies to adjust resources as required.
- Building Design and Construction: In order to design structures that are both effective and flexible, hybrid architecture combines elements of architecture, construction, and development. This method emphasizes community-focused design and building for high-density, low- and mid-rise housing typologies.
- Computer science: In computer science, hybrid architectures are those that integrate one or more special-purpose devices with a general-purpose computer. With this method, excellent performance and efficiency are attained in certain jobs or applications.
4. Peer-to-Peer (P2P) architecture: A peer-to-peer (P2P) architecture distributes jobs or workloads across peers. In a peer-to-peer network, each node serves as both a client and a server, acting as both “clients” and “servers” to the network’s other nodes. This network configuration varies from the client-server approach, which typically involves communication with and from a central server.
- P2P networks are decentralized, which means that no one server or authority controls the network. Instead, each participant or peer has the same powers and obligations.
- Self-organizing systems include peer-to-peer networks. As peers join and exit the network, it dynamically adapts and reorganizes.
- Peer-to-peer networks enable direct communication between peers. Peers may communicate with one another directly, providing for efficient and real-time communication.
5. Data fabric: Data fabric is a machine-enabled data integration architecture that uses metadata assets to unify, connect, and manage diverse data environments. It is a new method to data handling that use a network-based design rather than point-to-point connections, resulting in a unified data management architecture that enables companies to benefit from an extendable and convergent data layer. Data fabric is intended to ease data access and enable self-service data consumption for an organization’s specific processes, therefore establishing a reliable data foundation for AI and analytics.
6. Data meshes: A data mesh is a decentralized data architecture that organizes data based on certain business domains, giving data producers greater control over a given dataset. This technique is intended to address advanced data security concerns via distributed, decentralized ownership, and it is especially useful for increasing data demands throughout an enterprise. The data mesh idea is often likened to microservices because it requires a cultural change in how businesses see their data, treating it as a product rather than a byproduct of a process.
What is Data Architecture?
Data architecture is the body of rules that defines within the firm how data is gathered, kept, managed, and utilized. The data architecture is the toolset, policies, and standards that help in managing the handling of data assets properly. Data is a vital asset in this respect so it can drive decision-making and also make data available and useful.
In this article, we will understand and explore the meaning, types, frameworks and delve into the depth of What is Data Architecture?
image