1 00:00:00,360 --> 00:00:02,610 Hello, and welcome to today's lesson on 2 00:00:02,610 --> 00:00:05,040 What Does Azure Have to Offer? 3 00:00:05,040 --> 00:00:08,010 In this lesson, we are going to be taking a look at 4 00:00:08,010 --> 00:00:10,770 some of the basic services that you're going to find 5 00:00:10,770 --> 00:00:13,170 in the DP-203 exam. 6 00:00:13,170 --> 00:00:17,820 Yes, that's it. Simple, straightforward, but very important. 7 00:00:17,820 --> 00:00:20,283 So let's dive into this short lesson. 8 00:00:22,370 --> 00:00:26,050 Introducing Azure Blob, we'll start off here. 9 00:00:26,050 --> 00:00:30,430 Azure Blob is a primary storage service in Azure, 10 00:00:30,430 --> 00:00:33,020 and yes it includes Data Lakes. 11 00:00:33,020 --> 00:00:36,340 So we'll be diving deeper into how you enable Data Lakes 12 00:00:36,340 --> 00:00:37,580 and what that looks like. 13 00:00:37,580 --> 00:00:41,390 But for now, Azure Blob: primary storage service in Azure 14 00:00:41,390 --> 00:00:43,670 that also includes Data Lakes. 15 00:00:43,670 --> 00:00:46,380 Now this is going to be a fairly short lesson 16 00:00:46,380 --> 00:00:48,000 and very high level, 17 00:00:48,000 --> 00:00:50,380 so don't follow our friend, Mr. Squirrel here 18 00:00:50,380 --> 00:00:52,570 and run off into the bushes. 19 00:00:52,570 --> 00:00:55,260 We're going to be diving deeper into all of these services 20 00:00:55,260 --> 00:00:56,870 as we go through the course. 21 00:00:56,870 --> 00:00:59,720 My objective is just to give you a high-level overview 22 00:00:59,720 --> 00:01:02,070 in case you're not familiar with what they are. 23 00:01:03,130 --> 00:01:06,370 So, our next one is Azure Data Factory. 24 00:01:06,370 --> 00:01:10,870 Azure Data Factory is orchestration. It's pipelines. 25 00:01:10,870 --> 00:01:13,760 It's a way that we move data through phases 26 00:01:13,760 --> 00:01:15,570 in our cloud project. 27 00:01:15,570 --> 00:01:18,790 And it's also a way that we orchestrate or administer 28 00:01:18,790 --> 00:01:21,980 the entire pipeline of our cloud project 29 00:01:21,980 --> 00:01:23,173 from start to finish. 30 00:01:25,170 --> 00:01:26,990 Azure Synapse Analytics. 31 00:01:26,990 --> 00:01:28,880 So if you deal in structured data, 32 00:01:28,880 --> 00:01:32,620 Azure Synapse Analytics is definitely the way to go. 33 00:01:32,620 --> 00:01:35,310 Azure Synapse Analytics is actually a compilation 34 00:01:35,310 --> 00:01:38,280 of quite a few different services smashed together. 35 00:01:38,280 --> 00:01:42,750 So we have Data Lakes and database administration 36 00:01:42,750 --> 00:01:46,480 and data pipelines, and a whole bunch of different services 37 00:01:46,480 --> 00:01:47,740 that are kind of cobbled together 38 00:01:47,740 --> 00:01:49,860 into a very slick interface 39 00:01:49,860 --> 00:01:52,730 that allows you to manage structured data. 40 00:01:52,730 --> 00:01:55,310 And so we'll dive into that quite a bit in this course, 41 00:01:55,310 --> 00:01:58,323 but that is a very critical service for Azure. 42 00:02:01,360 --> 00:02:03,100 Azure Stream Analytics. 43 00:02:03,100 --> 00:02:07,090 So Azure Stream Analytics is one of the main services 44 00:02:07,090 --> 00:02:09,910 that we're going to talk about when we look at streaming. 45 00:02:09,910 --> 00:02:13,010 So this gives us the ability to stream data 46 00:02:13,010 --> 00:02:15,910 through our system and do some light transformations 47 00:02:15,910 --> 00:02:16,743 as we go. 48 00:02:18,560 --> 00:02:21,474 Last, but not least, Azure Databricks. 49 00:02:21,474 --> 00:02:25,260 So Databricks is a service that provides ETL 50 00:02:25,260 --> 00:02:27,450 (extract, transform and load) 51 00:02:27,450 --> 00:02:30,770 analytics and machine learning at a massive scale. 52 00:02:30,770 --> 00:02:33,600 For the DP-203, think transformation. 53 00:02:33,600 --> 00:02:34,850 It's really going to be the service 54 00:02:34,850 --> 00:02:37,910 that is going to allow us to transform data, 55 00:02:37,910 --> 00:02:40,820 especially at a massive scale, 56 00:02:40,820 --> 00:02:43,230 when we start talking about big projects. 57 00:02:43,230 --> 00:02:45,540 Databricks is a fantastic tool for that, 58 00:02:45,540 --> 00:02:47,340 so we'll be taking a look there. 59 00:02:47,340 --> 00:02:50,620 So now let's take a look at a diagram 60 00:02:50,620 --> 00:02:53,040 that kind of walks through how some of these services 61 00:02:53,040 --> 00:02:54,540 would play together. 62 00:02:54,540 --> 00:02:59,540 So we have our edge devices, or our ingestion system. 63 00:02:59,560 --> 00:03:01,000 So that's #1. 64 00:03:01,000 --> 00:03:03,330 And we'll talk a little bit about ingestion in this course, 65 00:03:03,330 --> 00:03:05,440 but really for the DP-203, 66 00:03:05,440 --> 00:03:07,380 we're more interested in what happens 67 00:03:07,380 --> 00:03:09,270 once the data gets into the system. 68 00:03:09,270 --> 00:03:11,563 We have our ingestion here at #1. 69 00:03:12,410 --> 00:03:14,360 As we get into #2, 70 00:03:14,360 --> 00:03:16,300 we're going to be talking about analyzing 71 00:03:16,300 --> 00:03:18,180 and transforming data. 72 00:03:18,180 --> 00:03:19,990 So when we talk about transformation, 73 00:03:19,990 --> 00:03:21,680 this could be something like Databricks, 74 00:03:21,680 --> 00:03:23,130 and you see it says streaming, 75 00:03:23,130 --> 00:03:25,120 Databricks can do batch and streaming. 76 00:03:25,120 --> 00:03:28,050 So don't get too hung up on this specific example. 77 00:03:28,050 --> 00:03:29,840 But we have our analyze and transform, 78 00:03:29,840 --> 00:03:31,453 once we have ingested the data. 79 00:03:32,490 --> 00:03:35,760 We also have storage. Storage is where, like I said, 80 00:03:35,760 --> 00:03:37,420 you could do SQL Data Warehouse, 81 00:03:37,420 --> 00:03:40,080 which is now Synapse Analytics. 82 00:03:40,080 --> 00:03:43,470 You could do SQL Database, storage, 83 00:03:43,470 --> 00:03:45,330 there's quite a few different solutions. 84 00:03:45,330 --> 00:03:49,110 For the DP-203, we're going to be looking at Synapse 85 00:03:49,110 --> 00:03:50,980 and we're going to be looking at that Blob storage, 86 00:03:50,980 --> 00:03:53,620 which is Blob and Data Lakes. 87 00:03:53,620 --> 00:03:56,500 And then of course we have insights that come out of this. 88 00:03:56,500 --> 00:04:00,330 So we could have Power BI to get some real-time reporting. 89 00:04:00,330 --> 00:04:02,090 And then the data is exported either 90 00:04:02,090 --> 00:04:03,920 into that Power BI dashboard 91 00:04:03,920 --> 00:04:08,490 or it's pulled into some other sort of step in the process 92 00:04:08,490 --> 00:04:11,870 where we enable a webhook or kick off an Azure function, 93 00:04:11,870 --> 00:04:13,910 or who knows what? 94 00:04:13,910 --> 00:04:16,500 So don't get too lost in this example. 95 00:04:16,500 --> 00:04:18,590 What I wanted to do was just quickly kind of 96 00:04:18,590 --> 00:04:21,750 throw some of these services up in a real-world example 97 00:04:21,750 --> 00:04:24,490 to show you what this actually looks like. 98 00:04:24,490 --> 00:04:26,910 Lastly, I can see it on your face. 99 00:04:26,910 --> 00:04:29,380 Hey, wait, we talked about 5 services. 100 00:04:29,380 --> 00:04:31,830 There's hundreds of services in Azure. 101 00:04:31,830 --> 00:04:35,970 You are correct. This is not an exclusive list. 102 00:04:35,970 --> 00:04:38,990 We are going to be focusing on the services 103 00:04:38,990 --> 00:04:41,890 that you need for the DP-203. 104 00:04:41,890 --> 00:04:43,230 Now, as we move through the course, 105 00:04:43,230 --> 00:04:45,340 we're going to talk about a few edge-case services 106 00:04:45,340 --> 00:04:46,990 a little bit, but we're going to stick closely 107 00:04:46,990 --> 00:04:48,350 to the most prominent services 108 00:04:48,350 --> 00:04:49,970 that you'd find on the DP-203, 109 00:04:49,970 --> 00:04:54,100 because we are interested in getting you to pass the exam. 110 00:04:54,100 --> 00:04:56,360 So just keep that in mind as we move through the course, 111 00:04:56,360 --> 00:04:58,543 and with that, we're on to the next lesson.