Data Storage – the type of big data technology comprises infrastructure designed to fetch, store, and manage big data. Data is organized in a manner that allows easy access, usage, and processing by various programs. Some of the top big data tools for this type of work with data include:
The Hadoop technology stack comes with a framework for distributed storage & big data processing using the MapReduce programming model. Some of the perks of this Apache big data stack include its ability to quickly manage distributed data (store and process) at reasonable costs. Big data technologies like Hadoop are scalable, resilient to failure, and very flexible. The downsides of Hadoop include poor security, low performance with fewer big data files, & the ability to support batch processing only, etc. You can use big data technologies spark tools as an alternative to the Hadoop technology stack.
MongoDB is one of the big data database technologies – a NoSQL program that uses documents similar to JSON. It provides an alternative schema to that of Relational Databases. This enables it to handle several data types that come in vast amounts across Distributed Architectures. The main advantages of MongoDB are flexibility and scalability. This is because it is easy to utilize replica sets on this big data database. On the other hand, MongoDB has the weakness of low speed and increased data size.
This is a Database Management System software that handles big data for businesses. It can eliminate duplicate files as it sorts out and stores huge volumes of information for reference. The latest version of this software can handle big data sets with high ingest rates. Also, it comes with multi-tenancy features and supports cloud storage. Rainstor can shrink large data for storage in the cloud. A terabyte can be reduced to 25GB. Other perks of Rainstor include lower costs.