IoT stuff is the easiest stuff to understand when it comes to high-volume data processing because it's a wide problem (ie: you have a whole lot of devices sending data continuously) instead of a deep problem (you have a few sources sending lots of data continuously).
IoT tends to talk more about HA (high availability) etc than more 'normal' architecture books, because it's about practical solutions.
I can't think of any right now (I looked on my bookshelf and it looks like the book was lent out). But any IoT architecture book will give you the basics.
Then maybe go look at the introductions to storm/kafka/spark.
One thing to remember that's surprisingly important is to understand how the upstream people are using your data. The shape of data in your datastore should match what upstream the consumers need.
The more it matches their use cases the less work it'll be to transform the data. Transformation == work == time.
IoT tends to talk more about HA (high availability) etc than more 'normal' architecture books, because it's about practical solutions.
I can't think of any right now (I looked on my bookshelf and it looks like the book was lent out). But any IoT architecture book will give you the basics.
Then maybe go look at the introductions to storm/kafka/spark.