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In this section, we are going to learn kinesis and Kinesis data streams.

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So we are developing Kinesis data streams using a lambda to process Kinesis data streams.

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For that purpose, let me start with to explain to what is Kinesis and what is Kinesis data stream.

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So Amazon Kinesis make it easy to collect, process and analyze real time streaming data.

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Kinesis offers key capabilities to cost efficiently process streaming data at any scale.

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With Amazon Kinesis, we can ingest real time data such as video audio application logs, website clicks,

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streams and Iot telemetry data for machine learning and analytics and so on.

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So Amazon Kinesis enables to process and analyze data and respond instantly instead of having wait until

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all data collected before the processing began.

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So there are lots of benefits we are using to kinesis.

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We can say that real time kinesis enabled to ingest buffer and process streaming data in real time and

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also it is fully managed, kinesis is fully managed and runs streaming applications which are required

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to manage any infrastructure and it is scalable.

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Kinesis can handle any amount of streaming data and process data from the hundreds of thousands sources.

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So very low latency.

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And if you talk about the use case about the kinesis, we can say that we link video analytics applications,

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building real time applications, analyzing Iot device data and so on.

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So there's also one more terminology, which is the Kinesis data stream.

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So far I have explained the Kinesis and there's also Kinesis data streams in this sub topic of the Kinesis.

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So we can use Amazon Kinesis data streams to collect and process large streams and data records in real

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time.

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We can create data processing applications known as Kinesis Data Stream Applications, a typical Kinesis

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data stream applications with data from a data stream as a data record.

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There is also a common use cases for the Kinesis data streams for using the exhilarate log and data

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feed intake and processing real time metrics and reporting real time data analytics and complex stream

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processing.

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These are the main use cases with using Kinesis data stream.

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And now I would like to show you to Kinesis data streams, high level architecture here.

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You can see the image on the slide, which is the high level architecture of Kinesis data stream.

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The process you can see that we have producers, kinesis stream and consumers and these are the outputs.

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So basically the producers continuously push data to kinesis data streams and consumers process the

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data in real time.

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Consumers can store their results using the services like Amazon, Dynamic DB, Redshift, S3 and so

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on.

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So as you can see the image, there are some of the core concepts that we should know.

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The first core concept is the Amazon Kinesis data stream.

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This is the including set of charts and we can call the items into data as a data record and we can

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call also there is a capacity estimate, a data stream capacity mode, determine how capacity is managed

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and how you are charged for the usage for our data stream.

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And we have a core concept of the producers.

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Basically producers put records into Amazon Kinesis streams and we have consumers, consumers get records

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from Amazon Kinesis data stream and process them and we have charts into Amazon.

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Kinesis Streams Chart is a unique identifier sequence of data records in a stream.

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So as you can see that we have learned Amazon Kinesis streams, but during this section we will focus

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on how to process Kinesis Stream with using ADA Lambda.