Hadoop

It’s open source nature
it’s community support
its being configurable
its maintenance

Apache Spark can be considered as an alternative because of its similar capabilities around processing and storing big data. The reason we went with Hadoop was the literature available online and integration capability with platforms like R Studio. The popularity of Hadoop has helped us in debugging issues and solving problems at a faster rate.

I feel that this is a highly reliable and scalable solution computing technology that is highly capable of processing large data sets across multiple servers and thousands of machines in a well-defined and distributed manner. Apache Hadoop can automatically scale up the number of servers and machines that are needed to process, store, and analyze data sets. It also handles explosions in data with big data technology. Apache Hadoop is good at handling all node failures as well.

Different departments of my organization have been getting the benefit from Apache Hadoop as it serves the purpose of saving lives when large amounts of data is unable to be converted and processed in a timely manner from a node or a simple computer. Hadoop also has an easier process of configuration in a clustered environment. Additionally, from my experience, I have noticed that Hadoop provides great scalability and redundancy. Also, it provides enterprise-level support from a variety of vendors. Lastly, I think that a great positive fact of Hadoop is its horizontal scaling.

MariaDB - Better to be already in the cloud you will use it for. Issues have improved as it has matured over the year.s
CockroachDB - Not nearly as performant (even out of the box) as Apache Hadoop. More configurations required just to make it work. In memory cacheing is an issue.