‫Now let's talk about Amazon Kinesis.
‫So, for the exam,
‫Kinesis is equal to real-time big data streaming
‫and it's all you should know.
‫And I'm going to give you a little bit more information
‫because there will be not enough, I guess,
‫for your own understanding.
‫So Kinesis is as a managed service used to collect, process
‫and analyze real-time streaming data at any scale.
‫It's too detailed to know all these things
‫for the Cloud Practitioner Exam
‫but still I'm going to give them to you.
‫It's good to know.
‫So there's, Kinesis data streams
‫which is a low latency streaming service to ingest it
‫at scale from hundreds of thousands of sources
‫and the source could be whatever can produce data
‫for example, a truck, a boat, an IOT device
‫whatever you can think of.
‫Then you have Kinesis Data Firehose
‫which is to load these streams
‫into places that we know already, such as Amazon S3,
‫Redshift, ElasticSearch, et cetera, et cetera.
‫We have Kinesis Data Analytics
‫which is to perform real-time analytics
‫on the stream using the SQL language.
‫And finally Kinesis Video Streams
‫to monitor real-time video streams
‫for analytics or machine learning.
‫So at the CCP level, all you need to know
‫is that Kinesis is used
‫for real-time big data streaming,
‫but the sub-services of Kinesis
‫really line up this way.
‫So we have Amazon Kinesis streams
‫that are going to be getting data
‫from click streams, IOT devices, metrics,
‫log servers, all these kind of things.
‫Then we can use Kinesis Data Analytics
‫if we wanted to analyze this data
‫and produce output in real-time.
‫And then we could use Kinesis Firehose
‫to send these outputs directly into destinations
‫such as an Amazon S3 buckets
‫or an Amazon Redshift database
‫where we can analyze this data
‫and perform more analytics down the road if we wanted to.
‫So that's it for Kinesis, I hope you liked it.
‫And I will see you in the next lecture.