Summary: This week allows to understand the DE lifecycle, from the steps required to get the data and deliver to the other fields that data engineers use to improve the system, we also have hands-on experience creating a full pipeline
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Finally I understand the difference between Kinesis data stream and Kinesis firehose. One of the most difficult part of studying the AWS cloud solution architecture was to understand the data solutions that they have. Now, everything makes sense.
I’m glad you got the difference can you share what’s your insight? How would you explain the difference between both?
Hi @pastorsoto. Both are services used during the ingestion phase and both can handle streaming data, but Kinesis data stream allows performing real-time analytics during the ingestion process, while Firehose is more intended to store data in data lakes and warehouses, to later analytics (near-real time).
So, Now I need to understand the use cases for each one, I’m not pretty sure, but I think the first one is a kind of combination of ingestion and serving to provide insights in real time from IoT devices or sensor measurements, and the second one is required when still is a need of real time analytics, but with a more flexible response time for the analytics. So, I need to keep making progress to further clarify the use cases.
Great insight!! This type of content provides a valuable piece for the reader and also improve your online presence. My suggestion would post the difference and find some quick use case examples to support your finding.
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