A brief explanation of each layer:
Data Consumption: This is where you will import the data from all the various source systems, some of which may be streaming the data. Others may only provide data once a day.
Stream Layer: It provides for incremental updating, making it the more complex layer. It trades accuracy for low latency, looking at only recent data. Data in here may be only seconds behind, but the trade-off is the data may not be clean.
Batch Layer: It looks at all the data at once and eventually corrects the data in the stream layer. It is the single version of the truth, the trusted layer, where there is usually lots of ETL and a traditional data warehouse. This layer is built using a predefined schedule, usually once or twice a day, including importing the data currently stored in the stream layer.
Presentation Layer: Think of it as the mediator, as it accepts queries and decides when to use the batch layer and when to use the speed layer. Its preference would be the batch layer as that has the trusted data, but if you ask it for up-to-the-second data, it will pull from the stream layer. So it’s a balance of retrieving what we trust versus what we want right now.
I hate the fact that this is named “lambda.” That’s a term which is way too overloaded in computer science. You have the architecture, lambda functions, and AWS lambda, all of which are utterly different and yet end up in the same conversation. This ends up confusing people unless you very specifically say things like “We’re going to use the AWS lambda service to create lambda functions to feed data from sensors into our lambda architecture.” And even then people still get confused.