What does the integration layer primarily focus on within a data architecture?

The integration layer in a data architecture is primarily concerned with data transformation and summarization. This layer acts as a key bridge between raw data sources and the analytical processes or data presentation layers. Its primary function is to process incoming data from various sources, transforming it into a consistent format that can be easily analyzed. This involves tasks such as cleaning, aggregating, and summarizing the data to make it more meaningful for end-users or downstream systems.

In this context, data transformation includes operations like converting formats, enriching data through the addition of calculated fields, and pivoting data for analysis. Summarization refers to condensing large volumes of detailed data into summary metrics or key performance indicators, which are more useful for decision-making.

While data visualization, data storage, and data access management are essential components of a comprehensive data architecture, they belong to different layers or aspects of the architecture. For instance, data visualization pertains to how data is presented and interacted with visually, data storage focuses on where the data is physically or logically kept, and data access management deals with permissions and access rights. These components work in conjunction with the integration layer, but the defining purpose of the integration layer is specifically centered around transforming and summarizing data.