1 00:00:00,600 --> 00:00:05,380 So now we need to talk about all the use cases of what you can do with the pipeline. 2 00:00:05,430 --> 00:00:08,190 And as you can see there is a lot of different services we can use. 3 00:00:08,250 --> 00:00:12,430 So there is this page called best practices in use cases and I do recommend you use it. 4 00:00:12,600 --> 00:00:16,260 And they do give you some examples around how to use good pipeline. 5 00:00:16,350 --> 00:00:21,180 For example keep battling with as three could commit and could deploy it or it could by playing with 6 00:00:21,180 --> 00:00:27,570 third parties such as actual providers just get up and Jenkins could star could build and test code. 7 00:00:27,620 --> 00:00:27,860 Yes. 8 00:00:27,870 --> 00:00:28,270 Yes. 9 00:00:28,290 --> 00:00:33,150 Let's take beanstalk it lambda and cloud formation so there is a lot of different use cases for code 10 00:00:33,150 --> 00:00:33,770 by plane. 11 00:00:33,800 --> 00:00:38,280 And I do recommend you just read through them and understand how they work and what they're trying to 12 00:00:38,280 --> 00:00:39,510 do at a very high level. 13 00:00:40,230 --> 00:00:43,220 So if we go back to you could buy right now and edit our pipeline. 14 00:00:43,350 --> 00:00:48,240 I just want to show you all the various actions we can have in there and just talk to them about at 15 00:00:48,240 --> 00:00:55,500 a high level so that we can understand exactly what we could do with cut pipeline so all possible use 16 00:00:55,500 --> 00:00:55,980 cases. 17 00:00:56,400 --> 00:00:58,430 So the first one is manual approval. 18 00:00:58,430 --> 00:00:58,980 We've seen this. 19 00:00:58,980 --> 00:01:04,470 This is when we want to have some kind of review before we deploy it into production could build is 20 00:01:04,470 --> 00:01:06,870 when we build or test our code. 21 00:01:06,870 --> 00:01:11,880 So when we test our code we don't produce any artifacts but when we build our code we can produce artifacts 22 00:01:11,910 --> 00:01:16,380 and pass them on to the next stage for example code pipeline could deflate. 23 00:01:16,530 --> 00:01:19,320 Jenkins we'll see this in this entire section. 24 00:01:19,320 --> 00:01:23,220 This is a way to integrate with a third party build service. 25 00:01:23,220 --> 00:01:28,050 Now for the deployment options we have cloud formations so it is possible to deploy an entire cloud 26 00:01:28,050 --> 00:01:30,480 formation templates using code pipeline. 27 00:01:30,570 --> 00:01:36,270 And so it is quite common for people because code confirmation is infrastructure as code to store that 28 00:01:36,270 --> 00:01:42,270 code into could commit to update the cloud formation templates include commit maybe validate it using 29 00:01:42,270 --> 00:01:43,040 could build. 30 00:01:43,170 --> 00:01:48,030 And then finally deploy it's using code pipeline and the cloud formation integration. 31 00:01:48,030 --> 00:01:50,820 So it is definitely a very common pattern. 32 00:01:50,820 --> 00:01:52,950 Could deploy we've seen this at great length. 33 00:01:52,950 --> 00:01:59,230 So we'll be good Elastic Beanstalk if we wanted to deploy our application on an elastic band stock target 34 00:01:59,520 --> 00:02:05,010 and have automated rolling upgrades for example on its service catalog if we wanted to deploy stuff 35 00:02:05,010 --> 00:02:08,480 to service catalog which is in the end of the day a cloud formation template. 36 00:02:08,490 --> 00:02:13,290 And we'll see service kind of log in this course later on and access skills easy yes if we wanted to 37 00:02:13,290 --> 00:02:15,190 deploy a docker containers. 38 00:02:15,510 --> 00:02:21,390 So yes we can do it simply or using blue green deployments and for blue green deployments we'll use 39 00:02:21,390 --> 00:02:27,570 could deploy in the back in the backend or deploy to Amazon as three and we've seen this in this course 40 00:02:27,870 --> 00:02:34,410 where we first for example can upload a zip archive artifact into an S3 Buckets in our account in another 41 00:02:34,410 --> 00:02:39,890 region for example we could invoke an eight US the function and I'll be talking about this in greater 42 00:02:40,170 --> 00:02:42,510 detail in the next lecture. 43 00:02:42,510 --> 00:02:49,770 In terms of sources we can pull code from code commits easy R for docker containers as three for a zip 44 00:02:49,770 --> 00:02:51,120 archives and github for code. 45 00:02:51,570 --> 00:02:56,660 So we would use could commit as three and get hub when it comes to integrating code and building it. 46 00:02:56,850 --> 00:03:02,850 Maybe we'll use ACR if we want to have a continuous delivery pipeline that has EPR as the source and 47 00:03:02,930 --> 00:03:05,070 easy s as the deployment targets. 48 00:03:05,070 --> 00:03:07,000 So that is definitely something that's possible. 49 00:03:07,200 --> 00:03:12,960 By the way if you do specify multiple sources every time the pipeline is run each source will be refreshed 50 00:03:12,990 --> 00:03:17,000 and the code will be will be pulled again now for testing. 51 00:03:17,070 --> 00:03:23,490 We can test with code builders in this in this section we can have device farm where we test our applications 52 00:03:23,490 --> 00:03:27,230 on many actual physical device and this is called database device farm. 53 00:03:27,270 --> 00:03:30,460 So this is good when we want to do some kind of load testing and so on. 54 00:03:30,540 --> 00:03:37,780 Jenkins also has a testing feature blaze meter and ghost UI inspector and run scope API monitoring. 55 00:03:37,830 --> 00:03:42,960 These are more external services but they allow you to run different kinds of testing such as load testing 56 00:03:43,200 --> 00:03:46,110 UI testing monitoring and so on. 57 00:03:46,110 --> 00:03:52,200 So you have a lot of different possibilities with code pipeline obviously and this is definitely a very 58 00:03:52,200 --> 00:03:53,510 handy use case. 59 00:03:53,520 --> 00:03:58,530 So one thing we should notice is that there's a risk lined up and we learned that we can run anything 60 00:03:58,530 --> 00:04:04,620 we want and I'll be talking about it in the very next lecture but so here we have all the possible options 61 00:04:04,650 --> 00:04:05,880 for a code pipeline. 62 00:04:05,880 --> 00:04:10,650 I do recommend you have a look through the use cases and I'll have the link obviously in the resources 63 00:04:11,510 --> 00:04:15,580 the use cases for you to understand how to use good by planning with difference. 64 00:04:15,600 --> 00:04:18,330 I tend to use cases for example with E. 65 00:04:18,330 --> 00:04:22,310 Yes for continued delivery of container based applications to the cloud. 66 00:04:22,530 --> 00:04:24,180 And I will see you in the next lecture.