Features and Principles of Data Architecture

What is data architecture?

The data architecture defines a standard set of products and tools that an organization uses to manage data. But it is much more than that. It defines the processes for capturing, transforming, and delivering usable data to business users. But more importantly, it identifies the people who will consume that data and their unique requirements. A good data architecture flows from right to left: from data consumers to data sources.

A data architecture should establish data standards for all of its data systems as a view or model of the eventual interactions between those data systems. Data integration, for example, should depend on its standards since data integration requires data interactions between two or more data systems

Data architecture features

The data architecture is built around certain characteristics:


Automation removes the friction that makes legacy data systems tedious to set up. Processes that used to take months to build can now be completed in hours or days using cloud-based tools. So, if a user wants to access different data, automation allows the architect to design a pipeline to deliver it quickly. As new data comes in, data architects can quickly integrate it into the architecture. But to create an adaptive architecture where data flows continuously, data architects automate everything.


Security is built into modern data architecture, ensuring that data is available based on business-defined need-to-know. Therefore, A good data architecture also recognizes existing. And also emerging threats to data security and ensures compliance with legislation such as HIPAA and GDPR.

User orientation

Previously, information was static, and access was restricted. But most of the leaders didn’t be guaranteed to get what they needed or required. However, what was accessible. In modern data architecture, business users can confidently define requirements. It is the same as data architects can bring data together and create solutions to access it in ways that meet business goals. A good data architecture continually evolves to meet new and changing customer information needs.


Any information engineering should be strong, with high accessibility, fiasco recuperation, and reinforcement/reestablish capacities.

Versatile information pipelines

Data architectures support real-time data flow and micro-batch data bursts to take advantage of emerging technologies.


Compelling information design depends on information structures that support cooperation. A good data architecture breaks down silos by combining data from all parts of the organization, along with external sources as needed, in one place to eliminate competing versions of the same data. In this environment, data is not exchanged between business units or aggregated but rather is considered an asset shared by the entire company.

Powered by AI

The data architecture uses machine learning and artificial intelligence to build the data objects, tables, views, and models that keep the data flowing. Intelligent data architecture takes automation to a new level, using machine learning (ML) and artificial intelligence (AI) to adjust, alert and recommend solutions to new conditions. ML and AI can identify data types, identify and correct data quality errors, create structures for incoming data, identify relationships to gain new insights, and recommend related data sets and analyses.


Flexibility permits organizations to increase or down on a case-by-case basis. The cloud enables fast and affordable scalability on demand. Elasticity allows administrators to focus on problem resolution. But elastic architectures free administrators from having to fine-tune capacity, throttle usage if necessary, and endlessly overbuy hardware. Elasticity also gives rise to applications and use cases, such as on-demand development and test environments, analytical sandboxes, and prototyping playgrounds.


Simplicity beats complexity inefficient data architecture. Strive for simplicity in data movement, data platforms, data assembly frameworks, and analytics platforms.  So, the simplest architecture is the best architecture. To reduce complexity, organizations should strive to limit data movement and duplication and advocate for a uniform database platform, and data assembly framework. But analytics platform, despite howls from proponents of “ best in class.”


A modern data architecture must be flexible enough to support many business needs. So, it has to support multiple types of business users, load operations and update rates, query operations, deployments, data processing engines, and pipelines.


Governance is the key to self-service. Modern data architecture defines access points so that each type of user can satisfy its information needs. So, Data scientists must have access to raw data on the landing pad or, better yet, a purpose-built sandbox where they can mix raw corporate data with their data.

Native Cloud

Modern data architectures support elastic scaling, high availability, end-to-end security for data in motion and data at rest, and cost and performance scalability.

Seamless data integration Data

Designs incorporate inheritance applications utilizing standard API interfaces. So, they are advanced for sharing information across frameworks, geologies, and associations.

Principles of data architecture

Data is a shared asset.

A modern data architecture must eliminate departmental data silos and offer all stakeholders a complete business view. But users need proper access to data. So, modern data architectures must offer interfaces that make it easy for users to consume data using tools appropriate to their work.

Security is essential.

Modern data architectures must be designed for security and must directly support data policies and access controls on the raw data.

Common vocabularies ensure common understanding. Shared information resources, for example, item indexes, financial schedule aspects, and KPI definitions require a typical jargon. This is so that it assists with avoiding debates during the investigation.

The data must be restored.

Invest in basic functions that perform data care.

Data flows must be optimized for agility. So, reduce the number of times data must be moved to reduce cost, increase data freshness, and optimize business agility.

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