Redis is very powerful in storing and retrieving the data from database.The number of read write per second they are offerring is still great.
Another great thing they are giving is geo-spatial datatype they are throwing to customers was mind blowing.
I personally admire the redis flash is also maturing.
Any company that is using the big data must consider redis for leveraging the uses of nosql capabilities.
I would suggest the people who are handling more api's,people who are having more data can move towards redis.
Concurrency and consistency. Architecting a solution to count millions and billions of items can get complex when events are captured in different regions, and they all need to converge in one place. Data consistency becomes an issue if many processes or threads are updating the same count concurrently. Locking techniques avoid consistency problems and deliver transactional level consistency, but slow down the solution.
One of the common use cases in metering is to track usage against time and to limit resources after the time runs out. In Redis, one can set a time-to-live value for the keys. Redis will automatically disable the keys after a set timeout.
Redis data structures come with built-in commands that are optimized to execute with maximum efficiency in memory (right where the data is stored). Some data structures help you accomplish much more than the counting of objects. For example, the Set data structure guarantees uniqueness to all the elements.
Sorted Set goes a step further by ensuring that only unique elements are added to the set, and allowing you to order the elements based on a score. Ordering your elements by time in a Sorted Set data structure, for example, will offer you a time-series database. With the help of Redis commands you could get your elements in a certain order, or delete items that you don’t need anymore.
Hyperloglog is another special data structure that estimates counts of millions of unique items without needing to store the objects themselves or impact memory.