The Enterprise Data Architecture

At VoltDB we talk a lot about the term enterprise data architecture. Such an architecture  supports fast data created by a multitude of new end points (machines, M2M, mobile devices, etc.), operationalizes the use of that data in applications, and moves data to a data lake for deep, long-term storage and analytics. The enterprise data architecture can be represented as a data pipeline that unifies applications, analytics, and application interaction across multiple functions, products, and disciplines.

Most businesses understand that data exists on a time continuum; it is not stationary. In almost every business, data moves from function to function to inform business decisions at all levels of the organization. The actions companies take with data are increasingly correlated to the data’s age. Immediately after data is created, it is highly interactive and for each event, of greatest value. This is where the opportunity exists to perform high-velocity operations on “new” or “incoming” data—for example, to place a trade, make a recommendation, serve an ad, or inspect a record. This is the beginning of a data management pipeline.

Shortly after data enters the pipeline, it can be examined relative to other data that also has arrived recently, e.g., by examining network traffic trends, composite risk by trading desk, or the state of an online game leaderboard. Queries on live data are commonly referred to as “real-time analytics.”

As data begins to age, the nature of its value begins to change; it becomes useful in a historical context and relative to other sources of data. With the adoption of fast and big data technologies, a trend is emerging in the way data management applications are being architected, designed, and developed. A central tenet underlies modern data architecture design: The value in data is not purely from historical insights. There is a natural push for analytics to be visible closer and closer to real time. As this occurs, it becomes obvious that taking action on this information, in real time, the instant it is created, is the ultimate goal of an enterprise data architecture. As a result, the historically separate functions of the “application” and the “analytics” begin to merge.

Enterprises are examining how they build new applications and new analytics capabilities. This natural progression quickly takes people to the point at which they realize they need a unifying architecture to enable and simplify building data-driven applications across the company, encompassing application interaction to exploratory analytics. Application interactions are now part of the pipeline. The result of this work is the modern enterprise data architecture.

Interacting with fast data is a fundamentally different process than interacting with big data that is at rest, requiring systems that are architected differently. To learn more about the how to design and implement an enterprise data architecture, check out VoltDB’s ebook, “Fast Data and the New Enterprise Data Architecture.”