Tick-Bank is a privately held Internet retailer of both physical and digital products founded in 2008
The company has more than six-million clients worldwide.
Tick-Bank's technology aids in payments, tax calculations and a variety of customer service tasks and serve as a connection between digital content makers and affiliate dealers, who then promote them to clients thereby assist in building revenue making opportunities for companies. Tick-Bank currently runs multiple java based web applications running on AWS and looking to enable web-site traffic analytics and also planning to extend the functionality for new web applications that are being launched.Tick-Bank uses KPL library to address event integration into the kinesis streams and thereby process the data to downstream applications for analytics.
With growing applications and customers, performance issues are hindering real time analytics and need an administrator to standardize performance, monitoring, manage and costs by kinesis streams. Please select 3 options.
Click on the arrows to vote for the correct answer
A. B. C. D. E. F.Answer: A, C, D.
Option A is correct - Splitting hot shards improve the performance.
Define a single shard for each web application of a kinesis stream.
Based on the usage generated through CloudWatch Metrics for each shard, split the hot shards or merge the cold shards.
This way we can improve the performance and reduce the costs for each stream.
if the performance is still not addressed, adapt to different streams per application.
https://docs.aws.amazon.com/streams/latest/dev/kinesis-using-sdk-java-resharding-strategies.html.
Option B is incorrect - Splitting cold shards does not improve the performance.
Define a single shard for each web application of a kinesis stream.
Based on the usage generated through CloudWatch Metrics for each shard, split the hot shards or merge the cold shards.
This way we can improve the performance and reduce the costs for each stream.
if the performance is still not addressed, adapt to different streams per application.
https://docs.aws.amazon.com/streams/latest/dev/kinesis-using-sdk-java-resharding-strategies.htmlOption C is correct - CloudWatch Metrics determine which are your "hot" or "cold" shards, that is, shards that are receiving much more data, or much less data, than expected.
You could then selectively split the hot shards to increase capacity for the hash keys that target those shards.
Similarly, you could merge cold shards to make better use of their unused capacity.
https://docs.aws.amazon.com/streams/latest/dev/kinesis-using-sdk-java-resharding-strategies.htmlOption D is correct - Merging cold shards improve performance as well as costs.
Define a single shard for each web application of a kinesis stream.
Based on the usage generated through CloudWatch Metrics for each shard, split the hot shards or merge the cold shards.
This way we can improve the performance and reduce the costs for each stream.
if the performance is still not addressed, adapt to different streams per application.
https://docs.aws.amazon.com/streams/latest/dev/kinesis-using-sdk-java-resharding-strategies.htmlOption E is incorrect - Merging hot shards does not improve performance.
Define a single shard for each web application of a kinesis stream.
Based on the usage generated through CloudWatch Metrics for each shard, split the hot shards or merge the cold shards.
This way we can improve the performance and reduce the costs for each stream.
if the performance is still not addressed, adapt to different streams per application.
https://docs.aws.amazon.com/streams/latest/dev/kinesis-using-sdk-java-resharding-strategies.htmlOption F is incorrect - CloudTrail does not provide this.
CloudWatch Metrics determine which are your "hot" or "cold" shards, that is, shards that are receiving much more data, or much less data, than expected.
You could then selectively split the hot shards to increase capacity for the hash keys that target those shards.
Similarly, you could merge cold shards to make better use of their unused capacity.
https://docs.aws.amazon.com/streams/latest/dev/kinesis-using-sdk-java-resharding-strategies.htmlTick-Bank is an internet retailer of both physical and digital products with over six-million clients worldwide. They currently run multiple java-based web applications on AWS and are looking to enable web-site traffic analytics while also extending the functionality for new web applications. Tick-Bank uses KPL library to address event integration into Kinesis streams and process the data to downstream applications for analytics. However, with growing applications and customers, performance issues are hindering real-time analytics and Tick-Bank needs to standardize performance, monitoring, manage and costs by kinesis streams.
To improve performance, monitoring, management, and cost-effectiveness of the Kinesis streams, Tick-Bank can take the following actions:
A. Use multiple shards to integrate data from different applications, reshard by splitting hot shards to increase capacity of the stream B. Use multiple shards to integrate data from different applications, reshard by splitting cold shards to increase capacity of the stream C. Use CloudWatch metrics to monitor and determine the "hot" or "cold" shards and understand the usage capacity E. Use multiple shards to integrate data from different applications, reshard by merging hot shards to reduce cost of the stream and improve performance
Option A suggests that Tick-Bank should use multiple shards to integrate data from different applications, and reshard by splitting hot shards to increase the capacity of the stream. This means that Tick-Bank will use more shards to process the data and split the hot shards to increase the capacity of the stream to handle more data. This approach will help to improve the performance of the stream by increasing its capacity.
Option B suggests that Tick-Bank should use multiple shards to integrate data from different applications, and reshard by splitting cold shards to increase the capacity of the stream. This means that Tick-Bank will use more shards to process the data and split the cold shards to increase the capacity of the stream to handle more data. This approach may not be very effective in improving performance, as the cold shards may not have much data to process.
Option C suggests that Tick-Bank should use CloudWatch metrics to monitor and determine the "hot" or "cold" shards and understand the usage capacity. CloudWatch metrics can provide detailed insights into the Kinesis stream's performance and help Tick-Bank identify hot and cold shards. By monitoring the usage capacity, Tick-Bank can optimize the Kinesis stream's performance, identify the bottlenecks, and take appropriate actions to improve performance.
Option E suggests that Tick-Bank should use multiple shards to integrate data from different applications, and reshard by merging hot shards to reduce the cost of the stream and improve performance. This means that Tick-Bank will use fewer shards to process the data and merge the hot shards to reduce the cost of the stream. This approach will help to reduce the cost of the Kinesis stream and improve its performance by reducing the number of shards.
Option D and F are not correct as they suggest resharding by merging cold shards and using CloudTrail metrics, respectively. These options may not be very effective in improving the performance, monitoring, management, and cost-effectiveness of the Kinesis stream.
In conclusion, Tick-Bank can improve the performance, monitoring, management, and cost-effectiveness of the Kinesis stream by using multiple shards to integrate data from different applications, resharding by splitting hot shards or merging hot shards, and using CloudWatch metrics to monitor and determine the "hot" or "cold" shards and understand the usage capacity.