Interesting. Do you have a source on that?
People are just beginning to take advantage of these data sources but table stakes is being able to continuously ingest and index many millions of complex spatial relationships every second, which by itself is something no popular Big Data platform supports.
The (fun!) computer science challenge of it is that the systems have to be unbelievably scalable, but you can't use hash partitioning or range partitioning, and many things you were taught about database engine design turn out to be completely wrong in this context. It is an area ripe for innovation and growth.
I'm sure there are some IoT apps sending 1PB/day, but there are plenty that don't.
If you look at every company that is working in this space, one of the first things you will notice is that they all use custom storage engines that do a full operating system bypass i.e. they manage all the system resources in userspace. If you do not do this, you cannot reliably get the necessary throughput out of the system for IoT. As far as I know, no scalable storage+execution engine in open source is designed like this yet. It requires much more computer science sophistication and lines of code to implement compared to traditional storage engines, so not the kind of thing you hack together over the weekend.
Also as far as throughput Netflix is doing 1.5 trillion (yes trillion) transactions per day in production on Cassandra.
I know of a production IoT system in the private sector that does 1.5 trillion (quasi-)transactions every 10 minutes, so almost three orders of magnitude higher throughput. Cassandra is an okay choice for storing IoT data but it isn't real-time in the sense that you can do immediate, fast queries about the relationships across those records as they are hitting the system.
That depends on how you are using Cassandra. Typically, you are expected to know your query patterns up front, and so you will lay your data out accordingly when ingesting. When done properly, this allows for ~1ms queries that return completely up-to-date results.
Also, to nitpick, these are not transactions; these are "operations" or some other word that doesn't imply what the word "transactions" implies.
As a quibble on the suggested use cases, this platform will not work for the Internet of Things generally for two reasons. First, it lacks support for the spatial data types, including polygons, and operations, including spatial joins, that are typical of those types of data models. Second, typical commercial IoT data sources are often on the order of 100TB-1PB per day, which implies continuous insert rates far beyond what this architecture can support and still have real-time queries. It is why Internet of Things is a completely different class of Big Data problem that does not fit on platforms like Hadoop or Spark that were not specifically designed for it.
You can design databases for Internet of Things workloads but this isn't an example of one. However, Crate should work great for more traditional Big Data workloads.