Big data and scientific compute use case
When we think of large clouds of compute, storage, and network, many think of parallel processing, large amounts of data, and data analytics. However, in order to get to that panacea of cloud computing, we have to first design and orchestrate a solution. With data being generated from almost every device and being delivered through the network, it's very difficult to collect and store this data and almost impossible to perform analytics with traditional tooling. There are many approaches to solving this issue. Some of these are:
- Hadoop: Based on a filesystem called Hadoop Distributed File System (HDFS) and related technologies such as Map/Reduce
- NoSQL: MongoDB, Cassandra, CouchDB, Couchbase, and so on
- NewSQL: InnoDB, Scalebase, and newer technologies such as NuoDB
Hadoop is the leader in the market right now, but all of the solutions mentioned previously are based on the CAP Theorem (Brewer's conjecture). Unlike RDBMS, which was the leading solution to...
