What type of system processing requires low-latency data retrieval?

Real-time analytics systems are designed specifically to handle data in a manner that allows for immediate processing and insight generation. In these systems, the requirement for low-latency data retrieval is crucial because they often deal with rapidly changing data and are utilized for decision-making in environments where timing is critical. For instance, applications in financial services, fraud detection, and real-time monitoring systems depend on data being analyzed as it arrives, enabling organizations to respond immediately to emerging trends or anomalies.

In contrast, batch processing systems typically handle data in large volumes but not in real time, with processing occurring at scheduled intervals, making them unsuitable for applications needing immediate insights. Data warehousing solutions primarily focus on storing large amounts of historical data for reporting and analysis, where latency is less of an issue. Data entry systems involve the capture of data but do not inherently require low-latency retrieval for analysis or reporting purposes. Thus, the emphasis on immediate responsiveness in real-time analytics clearly positions it as the correct choice for low-latency data retrieval requirements.