Enrico Signoretti of Juku writes:
Data grows (steadily… and exponentially) and nothing gets thrown away. Since data adds up, the concept of the “data lake” has taken shape. Even systems created for big data are starting to sense this problem and system architects are beginning to think differently about storage.
I’m going to take Hadoop as an example because this gives a good idea of a hyper converged infrastructure, doesn’t it?
Today, most Hadoop clusters are built on top of a HDFS (Hadoop Distributed File system). HDFS characteristics make this FS much cheaper, reliable and scalable than many other solutions but, at the same time it’s limited by the cluster design itself.
A great look at the types of convergence (or lack thereof) in the market. Hyperconvergence isn’t for everyone. Read on to find out what may work best for you.
Read more: Hyper-convergence, divergence and micro-convergence
