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Amazon Kinesis Data Streams | High Level Architecture | Explained

Автор: Pythoholic

Загружено: 2020-08-04

Просмотров: 10679

Описание:

Let's talk about Kinesis Data Streams.
Kinesis Data Streams
Amazon Kinesis Data Streams is a scalable and durable real-time data streaming service that can continuously capture gigabytes of data per second from hundreds of thousands of sources. SO, when you read data steams think of KDS.

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Every unit of data stored by KDS is a data record.
And a group of DATA RECORD makes up data steam.
And data records in data streams are distributed into SHARDS.
When it comes to KINESIS, we have a default shart limit of 500 per stream in the US East (N. Virginia), US West (Oregon), and Europe (Ireland). For all other regions, the default shard quota is 200 shards per data stream.

And the PRODUCER is the one responsible to put the data records into SHARDS and a CONSUMER gets data records from SHARDS.

Timelines for your convenience:
00:00 // Intro
00:20 // Kinesis Data Streams Deep Dive
05:20 // Data Streams and How it works?
08:18 // Determining the Initial size of Kinesis Data Streams
16:55 // Datastreams Producers
19:42 // Datastreams Consumers, Quota and Limits
22:16 // Kinesis Data Streams High Level Architecture
22:58 // Kinesis Security
27:48 // Outro

Disclaimer: The content provided in the channel are not affiliated in any way to the organization. We provide information here on the channel based on the knowledge we have on the topic. We advise our viewers to please do their own research and read more about them from the source provided by the organization to get a better outlook on the topic that has been covered.
These videos are just to provide you a platform to learn, and there can be mistakes and we are always trying to improve based on your feedback. we recommend viewers to have an open mind. Please support the channel to get more content like these in the future.

#RoadToAWS #AWSSolutionsArchitectAssociate2020 #Pythoholic

Amazon Kinesis Data Streams | High Level Architecture | Explained

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