Real-Time Log Analysis for Web Applications

Answer - C.

When you see Amazon Kinesis as an option, this becomes the ideal option to process data in real-time.

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.

For more information on Amazon Kinesis, please visit the below URL:

To fulfill the requirement of generating real-time reports of the top 10 users of a web application, the chosen option needs to be cost-effective, scalable, and able to handle a high volume of requests.

Option A: Publish your data to CloudWatch Logs, and configure your application to autoscale to handle the load on demand.

CloudWatch Logs is a managed service provided by AWS for storing and analyzing logs from various sources. In this option, the log data is published to CloudWatch Logs, and the application is configured to autoscale to handle the increased load on demand.

Pros:

• CloudWatch Logs provides a scalable solution for log data storage and analysis.
• Autoscaling ensures that the application can handle increased load without manual intervention.
• CloudWatch Logs has a pricing model based on the volume of data ingested and stored, making it a cost-effective solution.

Cons:

• Generating real-time reports may still be a challenge as the logs need to be processed and analyzed before generating the report.
• The time taken to analyze the logs may depend on the volume of data ingested and the complexity of the analysis required.

Option B: Publish your log data to an Amazon S3 bucket. Use AWS CloudFormation to create an Auto Scaling group to scale your post-processing application configured to pull down your log files stored in an Amazon S3.

In this option, log data is published to an S3 bucket, and an Auto Scaling group is created using CloudFormation. The Auto Scaling group is configured to scale the post-processing application that pulls the log files stored in S3.

Pros:

• S3 provides a scalable and durable storage solution for log data.
• CloudFormation simplifies the deployment and management of resources.
• Autoscaling ensures that the application can handle increased load without manual intervention.

Cons:

• Generating real-time reports may still be a challenge as the logs need to be processed and analyzed before generating the report.
• The time taken to analyze the logs may depend on the volume of data ingested and the complexity of the analysis required.

Option C: Post your log data to an Amazon Kinesis data stream, and subscribe to your log-processing application so that it is configured to process your logging data. Configure your application to autoscale to handle the load on demand.

Amazon Kinesis is a managed service provided by AWS for real-time processing of streaming data. In this option, log data is posted to a Kinesis data stream, and the log-processing application is subscribed to the stream. Autoscaling is configured to handle the increased load on demand.

Pros:

• Kinesis provides a scalable and durable solution for real-time processing of streaming data.
• Autoscaling ensures that the application can handle increased load without manual intervention.
• Real-time processing ensures that reports are generated in real-time.

Cons:

• Kinesis can be expensive for low volume workloads.
• The log-processing application needs to be configured to process data from Kinesis data stream.

Option D: Create a multi-AZ Amazon RDS MySQL cluster, post the logging data to MySQL, and run a map reduce job to retrieve the required information on user counts.

In this option, the logging data is posted to a multi-AZ RDS MySQL cluster, and a map reduce job is run to retrieve the required information on user counts.

Pros:

• RDS MySQL provides a scalable and managed solution for database storage and analysis.
• Map reduce job can efficiently process large volumes of data.

Cons:

• RDS MySQL can be expensive for high volume workloads.
• Generating real-time reports may be a challenge as the logs need to be processed and analyzed before generating the report.
• Map reduce job may take time to process large volumes of data.

Based on the requirements and constraints, Option C - Post your log data to an Amazon Kinesis data stream