A real-time big data analytics architecture is a system designed to process, analyze, and act on large volumes of data in real time. What’s missing in the below infographic?
This type of architecture is typically used to support applications that require fast and accurate data processing, such as fraud detection, real-time recommendation engines, and social media analytics.
A real-time big data analytics architecture typically consists of the following components:
Data ingestion: This is the process of collecting the data from the data sources and transferring it to the data storage system. Data ingestion may involve using a data pipeline or other tool to collect the data in real time.
Data processing: This is the component or system that processes the data for analysis. Data processing may involve filtering, cleaning, and transforming the data, as well as applying algorithms to extract insights and perform analytics.
Data sources: These are the systems or devices that generate the data that will be analyzed. Examples of data sources include sensors, web logs, social media platforms, and mobile devices.
Data storage: This is the system or component that stores the collected data for analysis. Data storage systems for real-time big data analytics may include distributed file systems, NoSQL databases, or in-memory data stores.
Data visualization: This is the component or system that presents the results of the data analysis in a visual format, such as graphs, charts, and maps. Data visualization tools may be used to present the results to users or to trigger real-time actions based on the results.
Data action: This is the component or system that takes actions based on the results of the data analysis. This may include triggering alerts, sending notifications, or updating other systems or applications.
Overall, a real-time big data analytics architecture is designed to quickly and accurately process and analyze large volumes of data in real time, and to take actions based on the results of the analysis.
