Replicating High-End Analytics Pipeline with Low Latency on AWS
Question
Your company runs a high-end and a long-running analytics pipeline on your on-premises data centers.
The solution uses clusters of high configuration machines that are connected via a high throughput, low latency fiber network.
Due to the periodic hardware and networking issues, the setup uses a replica of clusters for redundancy and failover purposes.
The setup is now due for a major hardware upgrade and requires a considerable budget increase as well.
The management has decided to evaluate if AWS can be used and check whether it is possible to replicate the same setup with low latency.
Select two valid options to include in your suggestion.
Answers
Explanations
Click on the arrows to vote for the correct answer
A. B. C. D. E.Correct Answer: B and E.
Option A is INCORRECT because using the On-demand instances might not be a cost-efficient option for running a long-running application.
Option B is CORRECT because this option achieves the low latency network necessary for tightly-coupled node-to-node communication between the virtual machines in the same Availability Zone.
Option C is INCORRECT because as such Partitioned Groups do span over Availability Zones, which will defeat the requirement of the application of low latency communication.
Option D is INCORRECT because the spot instances may not be ideal for long-running applications within the partition groups.
Option E is CORRECT because using the RI would be the best option to get some upfront discount for a long-running application.
Sure, I can provide a detailed explanation of the options.
Option A: Use On-demand instances to minimize the cost. On-demand instances are instances that you can launch and pay for by the hour, with no upfront payment or long-term commitment. They are the most expensive type of EC2 instances, as they provide the most flexibility and convenience. However, they may not be the best option for cost optimization in this scenario since the high-end, long-running analytics pipeline requires a large number of instances that could quickly add up in cost.
Option B: Cluster Placement Groups Cluster Placement Groups are a feature that helps you to logically group instances within a single Availability Zone to work closely together. Instances within a placement group benefit from low network latency and high network throughput as they are placed in close proximity to each other. This option could be a good fit for the high-end, long-running analytics pipeline because it requires low latency and high throughput, which the placement group can provide. However, this option only provides redundancy within a single Availability Zone, so it does not provide geographic redundancy.
Option C: Placement Groups spread across two availability zones. This option provides a similar benefit to Option B, but with geographic redundancy. With placement groups spread across two Availability Zones, you can distribute your instances across multiple data centers, thereby increasing the resiliency of your application. This option could be a good fit for the high-end, long-running analytics pipeline, as it provides both low latency and high throughput and geographic redundancy. However, keep in mind that there will be additional cost implications associated with running instances across multiple Availability Zones.
Option D: Use Spot instances to minimize the cost. Spot instances are instances that are available at a significantly lower cost than on-demand instances. However, their pricing is variable and determined by supply and demand, so they can be terminated with just two minutes' notice. This option may not be suitable for the high-end, long-running analytics pipeline, as the risk of instance termination could lead to data loss or pipeline disruptions.
Option E: Use Reserved instances to minimize the cost. Reserved instances are instances that you purchase with a one- or three-year commitment upfront. They offer significant discounts compared to on-demand instances and are best suited for predictable workloads that require long-term infrastructure support. This option could be a good fit for the high-end, long-running analytics pipeline since it is a long-running workload, but it may not be the best fit for a dynamic environment that requires the flexibility to adjust instance types or regions.
In summary, options B and C may be the most appropriate choices for the high-end, long-running analytics pipeline, depending on the level of redundancy and cost requirements.
