The advantages and limitations of using AWS IoT Analytics, Amazon Kinesis, or AWS Lambda

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How can we efficiently process and analyze high volumes of IoT data ingested by AWS IoT Core? What are the advantages and limitations of using AWS IoT Analytics, Amazon Kinesis, or AWS Lambda in combination with other big data services like Amazon Redshift or Amazon EMR for this purpose?

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Hi sdtslmn,

AWS IoT Analytics is a service which allows you to ingest, process, store and analyse/visualise your data. You can use so called pipeline activities to enrich your data for example with meta data. In case you change your pipeline activities you can also reprocess your data. You can store your data in a service managed area or in your own Amazon S3 bucket.

Amazon Kinesis services are for streaming data and run real time analytics on it. But Kinesis is not a long term storage. Amazon Redshift is a data warehouse which allows you to run SQL analytics on your data. Amazon EMR can be used to process and analyse data.

AWS IoT Analytics is more a whole solution whereas services like Amazon Kinesis, Amazon EMR or Amazon Redshift are building blocks which you could use to build your own solution.

It depends on your use-case what which services would be a good fit for you. Do you need to store data in long term, how do you want to consume the data, are you looking for real time or batch analytics, etc.

Cheers,
Philipp

AWS
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