« Kinesis » : différence entre les versions
Ligne 38 : | Ligne 38 : | ||
* transform source records using AWS lambda functions | * transform source records using AWS lambda functions | ||
* deliver data to a specified destination | * deliver data to a specified destination | ||
Similar to Data Streams but acts as an ETL. | Similar to Data Streams but acts as an ETL.<br> | ||
You can use a Kinesis Data Streams as source. |
Version du 17 mai 2024 à 08:30
Description
Allows to gather data from different sources using data stream or fire hoses, and get that data to a destination in the expected format.
Allows to collect, process and analyze data in near real-time at scale.
Kinesis data streams are used in places where an unbounded stream of data needs to worked on in real time.
And Kinesis Firehose delivery streams are used when data needs to be delivered to a storage destination, such as S3.
Common use cases
- pattern detection
- click stream analysis (who is clicking on what and when)
- log processing for machine learning
- anomyly detection in IoT devices
Benefits
- fully managed service (focus on data and don't worry about the underlying system)
- can handle large amount of data
- can consume process and buffer data in real-time
- allows production of real-time metrics and reporting
Kinesis Data Stream
Move data from sources to a destination while having analytics, monitoring alerts, and connections to other services.
Common use cases
- log and data intake and procesing
- real-time metrics, reporting, and data analytics
Benefits
- ensures data durability and elasticity
Producers
Producers put records into Kinesis Data Streams.
Consumers
Consumers get records from Kinesis Data Streams and process them.
Kinesis Firehose
- ingest data from different sources
- transform source records using AWS lambda functions
- deliver data to a specified destination
Similar to Data Streams but acts as an ETL.
You can use a Kinesis Data Streams as source.