Data architecture is the design of data for use in defining the target state and the subsequent planning needed to achieve the target state. It is used to describe the structure, governance, logic, and standards for any data assets within an organization.
In information technology, data architecture is composed of models, policies, rules or standards that govern which data is collected, and how it is stored, arranged, integrated, and put to use in data systems and in organizations. A sound architecture reduces complexity and enhances flexibility by identifying duplication and providing mechanisms for sharing common data elements.
Data architecture is commonly viewed as one of several architecture domains that form the pillars of enterprise architecture. The others include business architecture, application architecture and technology architecture. Enterprise architects typically develop all of these architectures concurrently to ensure they are consistent with each other and meet the organization's strategic goals.
Data architecture often overlaps with information architecture, which focuses on how data is organized within an organization's information systems to meet business needs. Data integration is part of data architecture because it deals with combining data from multiple sources; in practice, the two disciplines are often combined into one function called data management.
Data professionals responsible for building or managing a data infrastructure may work specifically on data modelling or data management, or they may be generalists who oversee all aspects of an organization's information systems.
In enterprise software development, data architecture is regarded as one of the four cornerstones of enterprise architecture. Data architecture provides principles, patterns, and practices for organizing and describing an information system in terms of its data components.
Data architecture is one of four domains within the broader area of enterprise architecture, which also includes business architecture, application architecture and technology architecture.
The scope of data architecture includes:
Standards for how data elements are named, defined, structured and shared across disparate systems.
Data structures that represent the entities used by an organization as well as their relationships.
The policies, practices and systems used to manage information assets in an efficient manner.
Information that describes the attributes of the data in a database, including the meaning, origin and usage of each individual data element; its format; who owns it; who may access it; where it is stored; when it was created; when it expires; and so on.
What is collected and how it's used within the data organization and externally
Data architecture is a crucial aspect of any data-driven organization. It involves designing, building, and maintaining the data infrastructure that supports an organization's data needs. For data engineers, creating effective data architecture is essential for ensuring that data is available, reliable, and secure. Some examples of data architecture intended for data engineers include data lakes, data warehouses, and data pipelines.
Data lakes are large, centralized repositories that store raw data in its native format. They are designed to support a wide range of data sources and allow for easy data integration and processing. Data warehouses, on the other hand, are designed to store structured and processed data that can be easily accessed and analyzed. They are typically used for business intelligence and reporting purposes.
Data pipelines are another important aspect of data architecture that data engineers must consider. These are systems that move data from one place to another, transforming it along the way. They are used to extract, transform, and load (ETL) data from different sources into a centralized location.
By leveraging these and other data architecture components, data engineers can build robust and scalable data infrastructures that support the needs of their organizations.
It's important for organizations to have access to effective data management tools to support their data architecture needs. One tool that can be helpful is Secoda, a platform that provides data discovery and metadata management capabilities. With Secoda, users can gain access to features such as automated metadata management, data lineage tracking, and data cataloging. These features can help data engineers and other data professionals better manage their data infrastructures, making it easier to find and use data assets across the organization.