Best Big Data Software for NVMesh

MongoDB is a distributed database that supports document-based applications and is designed for modern application developers. No other database is more productive. Our flexible document data model allows you to ship and iterate faster and provides a unified query interface that can be used for any purpose. No matter if it's your first customer, or 20 million users worldwide, you can meet your performance SLAs in every environment. You can easily ensure high availability, data integrity, and meet compliance standards for mission-critical workloads. A comprehensive suite of cloud database services that allows you to address a wide range of use cases, including transactional, analytical, search, and data visualizations. Secure mobile apps can be launched with native, edge to-cloud sync and automatic conflicts resolution. MongoDB can be run anywhere, from your laptop to the data center.

SAP HANA is an in-memory database with high performance that accelerates data-driven decision-making and actions. It supports all workloads and provides the most advanced analytics on multi-model data on premise and in cloud.

Apache Hadoop is a software library that allows distributed processing of large data sets across multiple computers. It uses simple programming models. It can scale from one server to thousands of machines and offer local computations and storage. Instead of relying on hardware to provide high-availability, it is designed to detect and manage failures at the application layer. This allows for highly-available services on top of a cluster computers that may be susceptible to failures.

Apache Spark™, a unified analytics engine that can handle large-scale data processing, is available. Apache Spark delivers high performance for streaming and batch data. It uses a state of the art DAG scheduler, query optimizer, as well as a physical execution engine. Spark has over 80 high-level operators, making it easy to create parallel apps. You can also use it interactively via the Scala, Python and R SQL shells. Spark powers a number of libraries, including SQL and DataFrames and MLlib for machine-learning, GraphX and Spark Streaming. These libraries can be combined seamlessly in one application. Spark can run on Hadoop, Apache Mesos and Kubernetes. It can also be used standalone or in the cloud. It can access a variety of data sources. Spark can be run in standalone cluster mode on EC2, Hadoop YARN and Mesos. Access data in HDFS and Alluxio.