Implementing a Real-Time Log Analysis Solution
Question
Your current log analysis application takes more than four hours to generate a report of the top 10 users of your web application.
You have been asked to implement a system that can report this information in real-time.
You need to ensure that the report is always up to date, and handle increases in the number of requests to your web application.
Which of the following is a cost-effective option to fulfill these requirements?
Answers
Explanations
Click on the arrows to vote for the correct answer
A. B. C. D.Correct Answer - C.
AWS Documentation mentions the below.
Amazon Kinesis makes it easy to collect, process, and analyze real-time, streaming data so you can get timely insights and react quickly to new information.
Amazon Kinesis offers key capabilities to cost-effectively process streaming data at any scale, along with the flexibility to choose the tools that best suit the requirements of your application.
With Amazon Kinesis, you can ingest real-time data such as application logs, website clickstreams, IoT telemetry data, and more into your databases, data lakes, and data warehouses, or build your own real-time applications using this data.
Amazon Kinesis enables you to process and analyze data as it arrives and respond in real-time instead of having to wait until all your data is collected before the processing can begin.
For more information on AWS Kinesis, please see the below link-
https://aws.amazon.com/kinesis/The best option for this scenario is to use Amazon Kinesis to stream your log data and configure a log-processing application to consume and process the log data. Option C is the correct answer.
Explanation:
Amazon Kinesis is a fully managed service that makes it easy to collect, process, and analyze real-time streaming data. With Kinesis, you can ingest, buffer, and process streaming data in real-time, and then route it to other services such as Amazon S3, Amazon Redshift, or Elasticsearch. By using Kinesis, you can build applications that process real-time data feeds, such as log data.
Option A suggests using CloudWatch Logs to publish log data and configure your application to auto-scale to handle the load on demand. While CloudWatch Logs can be used for log aggregation and analysis, it is not suitable for real-time processing of large volumes of data. Auto-scaling is a good option for handling varying loads, but it does not address the challenge of real-time processing.
Option B suggests publishing log data to an S3 bucket and using CloudFormation to create an auto-scaling group to scale your post-processing application. This option can work, but it involves several steps and may not be as cost-effective as using Kinesis. Moreover, S3 is not suitable for real-time processing of streaming data.
Option D suggests configuring an auto-scaling group to increase the size of your Amazon EMR cluster. Amazon EMR is a managed Hadoop framework for processing large amounts of data. While EMR can be used for real-time processing of data, it is not suitable for handling streaming data in real-time.
Therefore, the best option is to use Amazon Kinesis to stream your log data and configure a log-processing application to consume and process the log data. By using Kinesis, you can process data in real-time and scale your application to handle increasing loads. Kinesis also provides a cost-effective solution for real-time data processing, as you only pay for the data you stream and process.
