Technically, the architecture promoted in the book is "grid computing" -- that is, a fully distributed set of resources that work together to accomplish a common task.
Many commercial grid computing products try to be all in one -- that is, handle storage and computation. They don't apply to every problem because they only have one kind of storage meant for certain kinds of tasks.
The architecture in Big Data is a general-purpose way to compute arbitrary functions on arbitrary data, at scale and in realtime. Every data problem you'd ever want to do can be described as a function on data, which is why this architecture is so general-purpose. I recommend reading Chapter 1 in the book (which is free to download from the webpage for the book) where we explain these ideas much further.
Many commercial grid computing products try to be all in one -- that is, handle storage and computation. They don't apply to every problem because they only have one kind of storage meant for certain kinds of tasks.
The architecture in Big Data is a general-purpose way to compute arbitrary functions on arbitrary data, at scale and in realtime. Every data problem you'd ever want to do can be described as a function on data, which is why this architecture is so general-purpose. I recommend reading Chapter 1 in the book (which is free to download from the webpage for the book) where we explain these ideas much further.