A. Processing data records continuously.
Streaming analytics refers to the real-time or continuous processing of data as it is ingested. The defining characteristic of streaming analytics is its ability to process data in real time, allowing organizations to gain insights or take actions immediately as data flows in from various sources. This is essential for applications that require quick responses, such as monitoring systems, fraud detection, and real-time recommendation engines.
Why the other options are incorrect:
B. Processing data records in batches: Batch processing involves processing data in large, discrete chunks or batches over a specific time period. This is the opposite of streaming analytics, which focuses on real-time processing. Batch processing is typically used for workloads where real-time processing isn't necessary and is more efficient for large-scale data processing at scheduled intervals.
C. Processing a one-off data backfill: A one-off data backfill refers to a situation where historical data is processed to fill in missing information or to reprocess data after an issue has been detected. This is not a characteristic of streaming analytics, which is concerned with continuously processing data as it comes in, rather than handling historical or backlog data.
D. Accessing data with high latency: Streaming analytics is focused on low-latency data processing, meaning it is designed to provide near-instantaneous analysis. High latency in data access is contrary to the purpose of streaming analytics, which aims to minimize delay and enable real-time decision-making.
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