You are hired as the new head of operations for a SaaS company. Your CTO has asked you to make debugging any part of your entire operation simpler and as fast as possible. She complains that she has no idea what is going on in the complex, service-oriented architecture because the developers log to disk. It’s tough to find errors in logs on so many services. How can you best meet this requirement and satisfy your CTO?

Answer options:

A.Copy all log files into AWS S3 using a cron job on each instance. Use an S3 Notification Configuration on the PutBucket event and publish events to AWS Lambda. Use the Lambda to analyze logs as soon as they come in and flag issues.
B.Begin using CloudWatch Logs on every service. Stream all Log Groups into S3 objects. Use AWS EMR cluster jobs to perform adhoc MapReduce analysis and write new queries when needed.
C.Copy all log files into AWS S3 using a cron job on each instance. Use an S3 Notification Configuration on the PutBucket event and publish events to AWS Kinesis. Use Apache Spark on AWS EMR to perform at-scale stream processing queries on the log chunks and flag issues.
D.Begin using CloudWatch Logs on every service. Stream all Log Groups into an AWS Elasticsearch Service Domain running Kibana 4 and perform log analysis on a search cluster.

Answer – D
Amazon Elasticsearch Service makes it easy to deploy, operate, and scale Elasticsearch for log analytics, full-text search, application monitoring, and more. Amazon Elasticsearch Service is a fully managed service that delivers Elasticsearch’s easy-to-use APIs and real-time capabilities along with the availability, scalability, and security required by production workloads. The service offers built-in integrations with Kibana, Logstash, and AWS services, including Amazon Kinesis Firehose, AWS Lambda, and Amazon CloudWatch so that you can go from raw data to actionable insights quickly.
For more information on Elastic Search, please refer to the below link:
https://aws.amazon.com/elasticsearch-service/