Introduction

Real-time analytics essentially means that data is provided for analysis almost immediately once it is collected. Users can view, analyze, and understand data from a system in real time.

There are two types of real-time analytics:

- On-demand real-time analytics: On-demand real-time analytics requires a user or system to request a query of the data in order for the analysis to be performed and the results delivered to the user or system. This is known as an extract approach because the data is extracted to answer a specific question at that time.

- Continuous real-time analytics: Continuous real-time analytics does not require a query request. Instead, certain events trigger user alerts or system responses in a more proactive and ongoing manner. This is known as a push approach because the scan runs constantly in the background and is then pushed to the organization at pre-set intervals.

In order to better understand the concept of real-time analytics, it is necessary to resort to the concept of software architecture. The software architecture of a system describes its main components, their relationships, and how they interact with each other. Fundamentally, it serves as a model and communication mechanism. Provides an abstraction to manage system complexity; establishes communication and coordination between components. It serves as the basis for subsequent architectural discussions and recommendations.

In the case of real-time analytics systems, there is an architectural reference model, which proposes the following layers (see, Figure 1):

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Serana Belluci

Product Designer

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Enhancing Cybersecurity

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