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Batch processing involves processing large volumes of data at regular intervals. It is a highly efficient method for handling significant amounts of data, typically in sizes that are too large to be processed in real-time. With batch processing, data is collected over a specific time range, stored, and then processed at once. This type of processing is commonly used in scenarios where data latency is not a critical factor, such as daily reporting, data warehousing, and offline analytics.
Azure provides several services for batch data processing, including Azure Data Lake Storage, Azure Data Factory, and Azure Databricks. Let’s take a look at how these services can be used:
Streaming data processing, also known as real-time data processing, is the ingestion, processing, and analysis of data in motion. Unlike batch processing, which operates on accumulated data, streaming data processing handles data as it arrives, enabling near real-time decision-making and feedback loops. This approach is suitable for scenarios where low latency is crucial, such as real-time analytics, monitoring, and anomaly detection.
Azure offers various services for streaming data processing that can handle high-throughput, real-time data streams. Let’s explore a few of these services:
It’s worth noting that Azure provides capabilities to bridge batch and streaming processing. For example, Azure Databricks allows you to process both batch and streaming data within the same environment, providing flexibility and scalability.
In conclusion, batch and streaming data processing are two distinct approaches to handle data in Azure. Batch processing is suitable for scenarios where data can be processed in large volumes at regular intervals, while streaming processing is ideal for real-time decision-making and near real-time insights. By leveraging the appropriate Azure services, you can efficiently process and analyze data based on your specific requirements.
Correct answer: c) Data is processed in large volumes at scheduled intervals.
Correct answer: a) Real-time processing of data as it is generated.
Correct answer: c) Efficient utilization of computing resources.
Correct answer: b) Stored temporarily for later processing.
Correct answer: c) Data processing occurs at scheduled intervals, in large volumes.
Correct answer: a) Immediate availability of processed results.
Correct answer: d) Scheduled processing of large data volumes.
Correct answer: b) Streaming processing
Correct answer: c) Cost-effective utilization of resources
Correct answer: b) It requires data to be stored before processing.
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