Data Architecture is the efficient road map for a business to achieve its milestones, providing a framework for how data is processed through the backend system of an organization. Making the important specific data assets accessible to particular employees while keeping them end-to-end encrypted. Through better data architecture, one can manage, control, and utilize their data assets across various decision-making.
A better Data Architecture has three fundamental objectives:
- How Data is acquired and stored in the IT system of the Business.
- Arrangement of all data so acquired and stored in the systems and their better integration.
- It should include models, standards, policies, and rules for the use of the data assets.
Major Roles in Data Architecture Designs and Development.
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Major Roles in Data Architecture Designs and Development.
Data exists in many formats such as text, audio, numbers, or images. A well-defined data structure can consume any sort of data no matter the format or the structure. Analyzing all this data helps you to gain new insights. But before integrating it into your business one should have good conceptual clarity about it. Beginning with the understanding of major roles in data structure development and designs can bridge the inefficiency gap in your organization.
Data Modelers
A data modeler shall be assigned for proper monitoring and representation of data while maintaining accuracy. They also create data models that determine the data structures, these models help to communicate to business people.
Data Scientist
A Data Scientist is a well-qualified, trained, and knowledgeable individual who accumulates all the data from different sources or databases by using algorithms. Their main objective is to associate with patterns and trends of the market and analyze it with its business problem to conclude.
Data Architects
A kind of visionary. They evaluate various alternatives and determine an efficient way to develop and design a database keeping various factors in mind. Simply they need to manage the acquisition of various database technologies, which is economical and could easily handle all the data flows through.
Data Engineers
Data Architects and Engineers are two of the most important roles for integrating data structures in a business. Like Architects, Engineers are builders, building channels in the database to make data flow through. Their main objective is to generate and deliver the data accumulated from multiple data sources.
Technologies that Drive the Data Architecture.
Take it as a production of any article. With the help of various technologies, raw materials transform into a finished product. Similarly, all the physical documentation of a business needs a mediator like in the above case for the transformation into Data Architecture. Here mediator includes various technologies like Blockchain, automation, the Internet of Things, and machine learning.
Cloud Native in Data Architecture
Cloud Native is different from cloud computing. It is a layout for building webscale applications on the cloud. Cloud Native is more available and scalable and promises to ship new features without compromising availability and performance. It helps in making quicker changes in response to business people.
Scalable data pipelines
The conveyance of fast-transformed data from the source to the target shall be compatible with upcoming future problems of large volumes of data. However, a data architecture supporting the micro-batch data burst will be compatible with future demands.
Consistent Data Integration
For efficient sharing of data across different systems of organization. An application’s new feature should not cause any complication while integration for instance modern data architecture blends with legacy applications using standard API interfaces to avoid any potential difficulties.
Automated SaaS Platform
SaaS (Software as a service) provides you varieties of application and software on demand. Without paying for unnecessary licenses and hassles for the installation of software in your system you could get what you want. With an automated SaaS platform, you can easily connect your data architecture with the platform like Rivery (cloud naive) platform with no maintenance.
Popular Data Architecture Frameworks
DAMA- DmBOK (Data Management Body of Knowledge) is exclusively developed for the management of data in an organization on multiple scales. It also explains all the guiding principles and provides functions, roles, and policies for data management
Zachman Framework Enterprise Architecture was created by Zachman at IBM during the 1980s. The data column includes several layers that conform to the compliance standards of the data architecture framework. It consists of semantic physical and enterprise data models.
The Open Group Architectural Framework offers a high-level data framework that offers a systematic approach to organizing data structures to develop enterprise software and packages. It mainly focuses on reducing errors and managing timelines with better effective costs with informational technology to produce favorable outcomes.
Data Architecture vs Data Modelling.
More and More organizations are recognizing the business value of data which raises the question of “How does managing data influence data analytics, business decisions, and revenue?”. Data Modelling and data architecture are two key factors of data management
Data Modelling takes a micro view of data while data architecture takes a macro view of data. It is a process of creating a visual of data entities which can be thought of as a diagram showing attributes of data elements. Represents the relationship that exists between those various elements. Whereas Data Architecture documents the organization’s Data Assets and maps how this data flows through the system. The main goal is to make a solid foundation for data processing, management, and usage.
Next Generation Data Architecture.
Next-Gen Data Architectures
Data Fabric
Often organizations get their data piled up unorganized, which makes their processing platform chaotic. A data fabric architecture works in a fashion that securely collects diverse data without changing its location and components. Rather than changing the components of data, data fabric makes data more accessible by connecting it with analytics and users anytime.
Data Mesh
Data mesh is the enterprise data management framework that distributes the ownership and operation of data to business domains. It enables domain-specific data producers and consumers to accumulate store analyze and manage data without relying on the centralized data management team. It promotes:
- Decentralized data ownership
- Contextualization of data
- Collaboration
- Flexibility in choosing data technology stacks
Data Lakehouse
In order to tackle the drawbacks of a singular database, the newest form of data architecture is evolved as data lakehouse. Similar to its name, Data Lakehouse is technically a combination of data lake and data warehouse. Data Lakehouse is cost-efficient. In order to tackle all business challenges, implementing a data lakehouse is crucial. It is a flexible architecture as it makes organizations or businesses address structured or unstructured data through a single interface.
