1 00:00:00,480 --> 00:00:02,830 ‫Now let's talk about Amazon Kinesis. 2 00:00:02,830 --> 00:00:04,000 ‫So, for the exam, 3 00:00:04,000 --> 00:00:06,920 ‫Kinesis is equal to real-time big data streaming 4 00:00:06,920 --> 00:00:08,260 ‫and it's all you should know. 5 00:00:08,260 --> 00:00:09,840 ‫And I'm going to give you a little bit more information 6 00:00:09,840 --> 00:00:11,750 ‫because there will be not enough, I guess, 7 00:00:11,750 --> 00:00:13,380 ‫for your own understanding. 8 00:00:13,380 --> 00:00:16,980 ‫So Kinesis is as a managed service used to collect, process 9 00:00:16,980 --> 00:00:20,400 ‫and analyze real-time streaming data at any scale. 10 00:00:20,400 --> 00:00:22,010 ‫It's too detailed to know all these things 11 00:00:22,010 --> 00:00:23,160 ‫for the Cloud Practitioner Exam 12 00:00:23,160 --> 00:00:25,060 ‫but still I'm going to give them to you. 13 00:00:25,060 --> 00:00:25,970 ‫It's good to know. 14 00:00:25,970 --> 00:00:27,720 ‫So there's, Kinesis data streams 15 00:00:27,720 --> 00:00:30,390 ‫which is a low latency streaming service to ingest it 16 00:00:30,390 --> 00:00:32,930 ‫at scale from hundreds of thousands of sources 17 00:00:32,930 --> 00:00:35,070 ‫and the source could be whatever can produce data 18 00:00:35,070 --> 00:00:37,330 ‫for example, a truck, a boat, an IOT device 19 00:00:37,330 --> 00:00:39,000 ‫whatever you can think of. 20 00:00:39,000 --> 00:00:40,870 ‫Then you have Kinesis Data Firehose 21 00:00:40,870 --> 00:00:42,450 ‫which is to load these streams 22 00:00:42,450 --> 00:00:46,830 ‫into places that we know already, such as Amazon S3, 23 00:00:46,830 --> 00:00:49,610 ‫Redshift, ElasticSearch, et cetera, et cetera. 24 00:00:49,610 --> 00:00:51,100 ‫We have Kinesis Data Analytics 25 00:00:51,100 --> 00:00:53,690 ‫which is to perform real-time analytics 26 00:00:53,690 --> 00:00:55,730 ‫on the stream using the SQL language. 27 00:00:55,730 --> 00:00:57,740 ‫And finally Kinesis Video Streams 28 00:00:57,740 --> 00:00:59,390 ‫to monitor real-time video streams 29 00:00:59,390 --> 00:01:01,830 ‫for analytics or machine learning. 30 00:01:01,830 --> 00:01:05,410 ‫So at the CCP level, all you need to know 31 00:01:05,410 --> 00:01:06,760 ‫is that Kinesis is used 32 00:01:06,760 --> 00:01:08,440 ‫for real-time big data streaming, 33 00:01:08,440 --> 00:01:10,210 ‫but the sub-services of Kinesis 34 00:01:10,210 --> 00:01:11,530 ‫really line up this way. 35 00:01:11,530 --> 00:01:13,550 ‫So we have Amazon Kinesis streams 36 00:01:13,550 --> 00:01:15,410 ‫that are going to be getting data 37 00:01:15,410 --> 00:01:18,690 ‫from click streams, IOT devices, metrics, 38 00:01:18,690 --> 00:01:20,880 ‫log servers, all these kind of things. 39 00:01:20,880 --> 00:01:22,740 ‫Then we can use Kinesis Data Analytics 40 00:01:22,740 --> 00:01:24,790 ‫if we wanted to analyze this data 41 00:01:24,790 --> 00:01:26,830 ‫and produce output in real-time. 42 00:01:26,830 --> 00:01:29,030 ‫And then we could use Kinesis Firehose 43 00:01:29,030 --> 00:01:32,320 ‫to send these outputs directly into destinations 44 00:01:32,320 --> 00:01:34,070 ‫such as an Amazon S3 buckets 45 00:01:34,070 --> 00:01:35,890 ‫or an Amazon Redshift database 46 00:01:35,890 --> 00:01:37,440 ‫where we can analyze this data 47 00:01:37,440 --> 00:01:40,330 ‫and perform more analytics down the road if we wanted to. 48 00:01:40,330 --> 00:01:42,330 ‫So that's it for Kinesis, I hope you liked it. 49 00:01:42,330 --> 00:01:44,280 ‫And I will see you in the next lecture.