Supported Queries in Kinesis Analytics
Question
HikeHills.com (HH) is an online specialty retailer that sells clothing and outdoor refreshment gear for trekking, go camping, boulevard biking, mountain biking, rock hiking, ice mountaineering, skiing, avalanche protection, snowboarding, fly fishing, kayaking, rafting, road and trace running, and many more. HHruns their entire online infrastructure on java based web applications running on AWS.
The HH is capturing click stream data and use custom-build recommendation engine to recommend products which eventually improve sales, understand customer preferences and already using AWS Streaming capabilities to collect events and transaction logs and process the stream. HHis planning to use Kinesis Analytics to build SQL capabilities on streaming data.
What kind of queries is supported in Kinesis Analytics? Select 2 options.
Answers
Explanations
Click on the arrows to vote for the correct answer
A. B. C. D.Answer: A,D.
Option A is correct - Continuous Query is a query over a stream executes continuously over streaming data.
This continuous execution enables scenarios, such as the ability for applications to continuously query a stream and generate alerts.
https://docs.aws.amazon.com/kinesisanalytics/latest/dev/continuous-queries-Option B is incorrect - SQL queries in your application code execute continuously over in-applicationstreams.
An in-application stream represents unbounded data that flows continuously through your application.
Therefore, to get result sets from this continuously updating input, you often bound queries using a window defined in terms of time or rows.
These are also called windowed SQL.
You have stagger windows, Tumbling windows and Sliding windows query sub types.
https://docs.aws.amazon.com/kinesisanalytics/latest/dev/windowed-sql.htmlOption C is incorrect -Continuous Query is a query over a stream executes continuously over streaming data.
This continuous execution enables scenarios, such as the ability for applications to continuously query a stream and generate alerts.
https://docs.aws.amazon.com/kinesisanalytics/latest/dev/continuous-queries-Option D is correct -SQL queries in your application code execute continuously over in-applicationstreams.
An in-application stream represents unbounded data that flows continuously through your application.
Therefore, to get result sets from this continuously updating input, you often bound queries using a window defined in terms of time or rows.
These are also called windowed SQL.
You have stagger windows, Tumbling windows and Sliding windows query sub types.
https://docs.aws.amazon.com/kinesisanalytics/latest/dev/windowed-sql.htmlKinesis Analytics is a service provided by AWS that allows for the processing and analysis of streaming data in real-time. It supports running SQL queries on streaming data and can be used to gain insights and take actions based on data in motion.
The two types of queries that are supported in Kinesis Analytics are Continuous Queries and Windowed Queries.
Continuous Queries are SQL queries that are executed continuously over a stream of data. These queries can generate alerts or trigger actions based on specific conditions that are met within the data stream. Continuous Queries are typically used to monitor for specific patterns or anomalies in the data stream and to take action in real-time.
Windowed Queries are SQL queries that are executed over a specific window of time or data. The window can be fixed or sliding, and the queries are executed over the data that falls within the window. Windowed Queries can be used to analyze trends or patterns over time, such as aggregating data by hour or day, and can be useful for generating reports or visualizations.
In summary, Kinesis Analytics supports SQL queries on streaming data, including Continuous Queries that execute continuously over a stream of data and can generate alerts or trigger actions based on conditions met, and Windowed Queries that are executed over a specific window of time or data and can be useful for analyzing trends or patterns over time.
