Azure Stream Analytics: Best Input Options for High Throughput and Low Latencies
Question
While working on one of your company's projects, your teammate wants to check the options for input to an Azure Stream Analytics task that needs high throughput and low latencies.
He is confused about the input that he should use in this case.
He approaches you and asks you to help him.
Which of the following Azure product would you suggest to him?
Answers
Explanations
Click on the arrows to vote for the correct answer
A. B. C. D. E.Correct Answer: D
Azure Event Hub is considered as a highly scalable event ingestion service that can take and process over million events within a second.
You can transform and store the data that is sent to the event hubs with the help of storage/batching adapters or real-time analytics provider.
Event Hubs are known for consuming the data streams from applications with high throughput and low latencies.
Option A is incorrect.
Azure table storage is a NoSQL store that is used for schemaless storage of structured data.
Option B is incorrect.
Azure Blob Storage should be used when you desire your application to support streaming and random-access scenarios.
Option C is incorrect.
Azure Queue Storage is used to allow asynchronous message queueing among application components.
Option D is correct.
In the given scenario, You should suggest Azure Event Hubs to your teammate as Event Hubs are the primary choice for consuming the data streams from applications with high throughput and low latencies.
Option E is incorrect.
Azure IoT Hub offers a cloud-hosted solution back end for connecting any device virtually.
To know more about Azure Event Hubs, please visit the below-given link:
For high throughput and low latency input to an Azure Stream Analytics task, the most appropriate Azure product to use would be Azure Event Hubs (Option D).
Azure Event Hubs is a high throughput and low latency data streaming service that can handle millions of events per second. It can collect and process large volumes of data from multiple sources and systems, making it ideal for scenarios that require real-time processing and analytics.
Azure Table Storage (Option A) is a NoSQL key-value storage service that is designed for structured data storage. It is optimized for storing large amounts of structured data, but it may not be suitable for high throughput and low latency data streaming scenarios.
Azure Blob Storage (Option B) is a fully-managed object storage service that is used for unstructured data storage, such as text and binary data. While it can store large volumes of data, it may not be the best choice for high throughput and low latency data streaming scenarios.
Azure Queue Storage (Option C) is a messaging service that enables reliable message delivery between application components. It is designed for scenarios that require asynchronous communication between application components, and may not be suitable for high throughput and low latency data streaming scenarios.
Azure IoT Hub (Option E) is a managed service for bi-directional communication between IoT devices and the cloud. While it can handle high volumes of device telemetry data, it may not be the best choice for scenarios that require low latency data streaming.
In summary, for scenarios that require high throughput and low latency input to an Azure Stream Analytics task, Azure Event Hubs would be the most appropriate option.
