1 00:00:00,330 --> 00:00:02,340 ‫So let's summarize everything we've learned 2 00:00:02,340 --> 00:00:03,810 ‫in the monitoring section. 3 00:00:03,810 --> 00:00:05,490 ‫The first ones is around CloudWatch 4 00:00:05,490 --> 00:00:06,960 ‫and CloudWatch has multiple flavors. 5 00:00:06,960 --> 00:00:09,160 ‫There is CloudWatch metrics to monitor the performance 6 00:00:09,160 --> 00:00:12,480 ‫of your AWS services and billing metrics. 7 00:00:12,480 --> 00:00:13,320 ‫CloudWatch alarms 8 00:00:13,320 --> 00:00:14,910 ‫if you want to automate notifications when 9 00:00:14,910 --> 00:00:17,880 ‫a metric goes outside of a specific range. 10 00:00:17,880 --> 00:00:20,340 ‫And then you can automate it to perform EC2 actions 11 00:00:20,340 --> 00:00:22,380 ‫such as reboots, et cetera. 12 00:00:22,380 --> 00:00:24,330 ‫You can also send notifications that are clean 13 00:00:24,330 --> 00:00:27,030 ‫to the SNS service based on a metric 14 00:00:27,030 --> 00:00:28,830 ‫going over certain limits. 15 00:00:28,830 --> 00:00:31,050 ‫CloudWatch Logs are used to collect log files 16 00:00:31,050 --> 00:00:34,410 ‫from EC2 instances, servers and lender functions 17 00:00:34,410 --> 00:00:36,660 ‫and they're centralized within one service. 18 00:00:36,660 --> 00:00:39,540 ‫And CloudWatch events also called Event Bridge is a way 19 00:00:39,540 --> 00:00:41,651 ‫for you to react to events in AWS 20 00:00:41,651 --> 00:00:45,690 ‫or to trigger a rule based on the specific schedule. 21 00:00:45,690 --> 00:00:47,430 ‫Now, if you want to audit API calls 22 00:00:47,430 --> 00:00:48,600 ‫made within your account, 23 00:00:48,600 --> 00:00:50,460 ‫you should use the CloudTrail service, 24 00:00:50,460 --> 00:00:52,710 ‫and on top of it there is CloudTrail Insights 25 00:00:52,710 --> 00:00:55,075 ‫which is for you to get an automated analysis 26 00:00:55,075 --> 00:00:57,420 ‫of your CloudTrail events. 27 00:00:57,420 --> 00:00:59,850 ‫Amazon x-Ray is used to trace requests made 28 00:00:59,850 --> 00:01:01,410 ‫through your distributed applications 29 00:01:01,410 --> 00:01:04,020 ‫and this is very helpful when you want to do 30 00:01:04,020 --> 00:01:06,363 ‫performance analysis or root cause analysis 31 00:01:06,363 --> 00:01:07,770 ‫especially when you have errors 32 00:01:07,770 --> 00:01:10,590 ‫and all your applications are talking with one another. 33 00:01:10,590 --> 00:01:13,590 ‫The AWS Health Dashboard gives you the status 34 00:01:13,590 --> 00:01:16,950 ‫of all the AWS services across all regions 35 00:01:16,950 --> 00:01:21,000 ‫whereas the AWS Account Health Dashboard is talking 36 00:01:21,000 --> 00:01:23,790 ‫about the AWS events that only impact 37 00:01:23,790 --> 00:01:26,580 ‫your specific infrastructure. 38 00:01:26,580 --> 00:01:27,870 ‫Finally, we have CodeGuru, 39 00:01:27,870 --> 00:01:30,300 ‫CodeGuru is a way for you to perform automated 40 00:01:30,300 --> 00:01:32,340 ‫code reviews using machine learning 41 00:01:32,340 --> 00:01:35,340 ‫and also application performance recommendations, again, 42 00:01:35,340 --> 00:01:36,810 ‫by monitoring the performance 43 00:01:36,810 --> 00:01:38,520 ‫of your application in production 44 00:01:38,520 --> 00:01:40,800 ‫and applying yet against some machine learning. 45 00:01:40,800 --> 00:01:41,633 ‫So that's it, 46 00:01:41,633 --> 00:01:44,500 ‫I hope you liked it and I will see you in the next lecture.