Gartner defines the market for cloud database management systems (DBMSs) as the market for software products that store and manipulate data and that are primarily delivered as software as a service (SaaS) in the cloud. Cloud DBMSs may optionally be capable of running on-premises, or in hybrid, multicloud or intercloud configurations. They can be used for transactional and/or analytical work. They may have features that enable them to participate in a wider data ecosystem. They typically persist data using proprietary components in a durable manner, enabling a full range of create, read, update and delete operations.
Gartner defines a data science and machine learning platform as an integrated set of code-based libraries and low-code tooling. These platforms support the independent use and collaboration among data scientists and their business and IT counterparts, with automation and AI assistance through all stages of the data science life cycle, including business understanding, data access and preparation, model creation and sharing of insights. They also support engineering workflows, including the creation of data, feature, deployment and testing pipelines. The platforms are provided via desktop client or browser with supporting compute instances or as a fully managed cloud offering.
Reviews for 'Data and Analytics - Others'
The market for ESP platforms consists of software subsystems that perform real-time computation on streaming event data. They execute calculations on unbounded input data continuously as it arrives, enabling immediate responses to current situations and/or storing results in files, object stores or other databases for later use. Examples of input data include clickstreams; copies of business transactions or database updates; social media posts; market data feeds; images; and sensor data from physical assets, such as mobile devices, machines and vehicles.
Hadoop distributions are used to provide scalable, distributed computing against on-premises and cloud-based file store data. Distributions are composed of commercially packaged and supported editions of open-source Apache Hadoop-related projects. Distributions provide access to applications, query/reporting tools, machine learning and data management infrastructure components. First introduced as collections of components for any use case, distributions are now often delivered as part of a specific solution for data lakes, machine learning or other uses. They subsequently grow into additional, expanded roles, competing with both older technologies like database management systems (DBMSs) and newer ones like Apache Spark.