In this section, we are going to learn kinesis and Kinesis data streams.
So we are developing Kinesis data streams using a lambda to process Kinesis data streams.
For that purpose, let me start with to explain to what is Kinesis and what is Kinesis data stream.
So Amazon Kinesis make it easy to collect, process and analyze real time streaming data.
Kinesis offers key capabilities to cost efficiently process streaming data at any scale.
With Amazon Kinesis, we can ingest real time data such as video audio application logs, website clicks,
streams and Iot telemetry data for machine learning and analytics and so on.
So Amazon Kinesis enables to process and analyze data and respond instantly instead of having wait until
all data collected before the processing began.
So there are lots of benefits we are using to kinesis.
We can say that real time kinesis enabled to ingest buffer and process streaming data in real time and
also it is fully managed, kinesis is fully managed and runs streaming applications which are required
to manage any infrastructure and it is scalable.
Kinesis can handle any amount of streaming data and process data from the hundreds of thousands sources.
So very low latency.
And if you talk about the use case about the kinesis, we can say that we link video analytics applications,
building real time applications, analyzing Iot device data and so on.
So there's also one more terminology, which is the Kinesis data stream.
So far I have explained the Kinesis and there's also Kinesis data streams in this sub topic of the Kinesis.
So we can use Amazon Kinesis data streams to collect and process large streams and data records in real
time.
We can create data processing applications known as Kinesis Data Stream Applications, a typical Kinesis
data stream applications with data from a data stream as a data record.
There is also a common use cases for the Kinesis data streams for using the exhilarate log and data
feed intake and processing real time metrics and reporting real time data analytics and complex stream
processing.
These are the main use cases with using Kinesis data stream.
And now I would like to show you to Kinesis data streams, high level architecture here.
You can see the image on the slide, which is the high level architecture of Kinesis data stream.
The process you can see that we have producers, kinesis stream and consumers and these are the outputs.
So basically the producers continuously push data to kinesis data streams and consumers process the
data in real time.
Consumers can store their results using the services like Amazon, Dynamic DB, Redshift, S3 and so
on.
So as you can see the image, there are some of the core concepts that we should know.
The first core concept is the Amazon Kinesis data stream.
This is the including set of charts and we can call the items into data as a data record and we can
call also there is a capacity estimate, a data stream capacity mode, determine how capacity is managed
and how you are charged for the usage for our data stream.
And we have a core concept of the producers.
Basically producers put records into Amazon Kinesis streams and we have consumers, consumers get records
from Amazon Kinesis data stream and process them and we have charts into Amazon.
Kinesis Streams Chart is a unique identifier sequence of data records in a stream.
So as you can see that we have learned Amazon Kinesis streams, but during this section we will focus
on how to process Kinesis Stream with using ADA Lambda.