Gartner defines AI platforms for data science and machine learning (DSML) as platforms that support end-to-end AI model and agent development and life cycle management using diverse data science and AI techniques. These platforms enable data preparation, model building, deployment and governance, and are delivered as fully managed cloud services or on-premises infrastructure for AI experts and business users. AI platforms for DSML primarily target AI experts, including data scientists and AI engineers, providing a comprehensive suite of data science and AI techniques for augmented and automated decision making. These platforms support building models using data-intensive methods such as machine learning, as well as techniques such as simulation and optimization. They handle all types of data — tabular, image, video and text — for multimodal applications, such as computer vision, natural language processing (NLP) or composite AI, that combines optimization with machine learning.
Cloud Computing refers to products and services that enable the delivery, management, and optimization of computing resources over the internet. This category includes markets that focus on empowering organizations to seamlessly store, migrate, manage, and optimize workloads across diverse cloud environments, including public, private, hybrid, and multi-cloud models.
Gartner defines the market for cloud database management systems (DBMSs) as software products that store and manipulate data and are primarily delivered as platform as a service (PaaS) 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 typically persist data using a combination of proprietary and open components in a durable manner, enabling a full range of create, read, update and delete operations. They are used by application end users, designers, developers and operators of large database systems.
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.
Gartner defines metadata management solutions as applications to enable the collection, analysis and orchestration of metadata related to organizational data assets. These solutions enable workflow and operational support to make data easy to find, use and manage. They do this by collating metadata in any form from within its own application and third-party systems, and providing the ability to search, analyze and make decisions on the collated results. They also provide transparent cross-referencing over all related metadata, and derive insights from data (such as usage patterns and performance) through analysis of metadata to support a wide range of data-driven initiatives.