Azure Event Hub and Stream Analytics

What is Azure Event Hub used for?

• Data storage and backup

• Machine learning model training

• Azure Event Hub is used for real-time data ingestion and event streaming.

• Web application hosting

How does Azure Event Hub handle data ingestion?

• Azure Event Hub requires manual partitioning for data ingestion.

• Azure Event Hub only supports single-threaded data ingestion.

• Azure Event Hub allows concurrent event ingestion through a partitioned architecture, ensuring high throughput and scalability.

• Data ingestion in Azure Event Hub is limited to 100 events per second.

What is the maximum retention period for events in an Azure Event Hub?

• 1 day

• 7 days

• 14 days

• 30 days

Explain the concept of partitions in Azure Event Hub.

• Partitions are a type of security feature in Azure Event Hub.

• Partitions are the physical servers that host Azure Event Hub.

• Partitions are used to store data permanently in Azure Event Hub.

• Partitions in Azure Event Hub are segments of the event stream that allow for parallel processing and scalability.

What is Stream Analytics in Azure?

• Stream Analytics is a data storage solution in Azure.

• Stream Analytics is a machine learning tool in Azure.

• Stream Analytics is a real-time analytics service in Azure for processing and analyzing streaming data.

• Stream Analytics is a batch processing service in Azure.

How does Stream Analytics process data from Event Hubs?

• Stream Analytics processes data from Event Hubs by ingesting real-time events and applying SQL-like queries for analysis.

• Stream Analytics uses machine learning algorithms exclusively for data processing.

• Stream Analytics requires data to be pre-processed before analysis.

• Stream Analytics only processes batch data from Event Hubs.

What programming languages can be used with Stream Analytics?

• SQL-like query language, C#, JavaScript, Python

• Ruby

• PHP

• Swift

What is the purpose of a Stream Analytics job?

• To create static reports from batch data.

• To visualize data in a dashboard without processing.

• The purpose of a Stream Analytics job is to analyze and process real-time data streams.

• To store historical data for future analysis.

How can you visualize data processed by Stream Analytics?

• Use Power BI or export to Azure Blob Storage for visualization.

• Create a static report in Word.

• Use Excel for real-time data processing.

• Visualize data directly in SQL Server.

What are the input sources for Stream Analytics?

• Google Cloud Pub/Sub

• Azure Event Hubs, Azure IoT Hub, Azure Blob Storage, Azure SQL Database

• Azure Data Lake

• Amazon S3