Streaming analytics and batch analytics are two different approaches to processing and analyzing data.
Streaming Analytics:
- Definition: Streaming analytics involves processing data in real-time as it flows in. This allows for immediate insights and actions based on the data.
- Example: An e-commerce company uses streaming analytics to monitor transactions as they happen. If a sudden spike in purchases of a particular product is detected, the system can automatically trigger a restock order or a targeted marketing campaign.
- Use Case: Real-time fraud detection, monitoring social media trends, and IoT data processing.
Batch Analytics:
- Definition: Batch analytics processes data that has been collected over a period of time, typically in large batches. This approach is used when real-time analysis is not required, and the focus is on processing large volumes of historical data.
- Example: A retail company analyzes sales data from the past quarter to identify trends and patterns for future inventory planning.
- Use Case: Historical performance analysis, reporting, and long-term trend analysis.
In the context of cloud services, platforms like Tencent Cloud offer services tailored to both streaming and batch analytics needs. For streaming analytics, services like Tencent Cloud StreamCompute provide real-time data processing capabilities. For batch analytics, Tencent Cloud's big data processing services, such as EMR (Elastic MapReduce), support large-scale data processing and analysis.