Computer data storageComputer data storage is a technology consisting of computer components and recording media that are used to retain digital data. It is a core function and fundamental component of computers. The central processing unit (CPU) of a computer is what manipulates data by performing computations. In practice, almost all computers use a storage hierarchy, which puts fast but expensive and small storage options close to the CPU and slower but less expensive and larger options further away.
Data storageData storage is the recording (storing) of information (data) in a storage medium. Handwriting, phonographic recording, magnetic tape, and optical discs are all examples of storage media. Biological molecules such as RNA and DNA are considered by some as data storage. Recording may be accomplished with virtually any form of energy. Electronic data storage requires electrical power to store and retrieve data. Data storage in a digital, machine-readable medium is sometimes called digital data.
Object storageObject storage (also known as object-based storage) is a computer data storage that manages data as objects, as opposed to other storage architectures like which manages data as a file hierarchy, and block storage which manages data as blocks within sectors and tracks. Each object typically includes the data itself, a variable amount of metadata, and a globally unique identifier. Object storage can be implemented at multiple levels, including the device level (object-storage device), the system level, and the interface level.
Foundation modelsA foundation model (also called base model) is a large machine learning (ML) model trained on a vast quantity of data at scale (often by self-supervised learning or semi-supervised learning) such that it can be adapted to a wide range of downstream tasks. Foundation models have helped bring about a major transformation in how artificial intelligence (AI) systems are built, such as by powering prominent chatbots and other user-facing AI.
Cloud storageCloud storage is a model of computer data storage in which the digital data is stored in logical pools, said to be on "the cloud". The physical storage spans multiple servers (sometimes in multiple locations), and the physical environment is typically owned and managed by a hosting company. These cloud storage providers are responsible for keeping the data available and accessible, and the physical environment secured, protected, and running. People and organizations buy or lease storage capacity from the providers to store user, organization, or application data.
Large language modelA large language model (LLM) is a language model characterized by its large size. Their size is enabled by AI accelerators, which are able to process vast amounts of text data, mostly scraped from the Internet. The artificial neural networks which are built can contain from tens of millions and up to billions of weights and are (pre-)trained using self-supervised learning and semi-supervised learning. Transformer architecture contributed to faster training.
Machine learningMachine learning (ML) is an umbrella term for solving problems for which development of algorithms by human programmers would be cost-prohibitive, and instead the problems are solved by helping machines 'discover' their 'own' algorithms, without needing to be explicitly told what to do by any human-developed algorithms. Recently, generative artificial neural networks have been able to surpass results of many previous approaches.
Storage area networkA storage area network (SAN) or storage network is a computer network which provides access to consolidated, block-level data storage. SANs are primarily used to access data storage devices, such as disk arrays and tape libraries from servers so that the devices appear to the operating system as direct-attached storage. A SAN typically is a dedicated network of storage devices not accessible through the local area network (LAN). Although a SAN provides only block-level access, built on top of SANs do provide file-level access and are known as s.
Training, validation, and test data setsIn machine learning, a common task is the study and construction of algorithms that can learn from and make predictions on data. Such algorithms function by making data-driven predictions or decisions, through building a mathematical model from input data. These input data used to build the model are usually divided into multiple data sets. In particular, three data sets are commonly used in different stages of the creation of the model: training, validation, and test sets.
Write amplificationWrite amplification (WA) is an undesirable phenomenon associated with flash memory and solid-state drives (SSDs) where the actual amount of information physically written to the storage media is a multiple of the logical amount intended to be written. Because flash memory must be erased before it can be rewritten, with much coarser granularity of the erase operation when compared to the write operation, the process to perform these operations results in moving (or rewriting) user data and metadata more than once.
Generative pre-trained transformerGenerative pre-trained transformers (GPT) are a type of large language model (LLM) and a prominent framework for generative artificial intelligence. The first GPT was introduced in 2018 by OpenAI. GPT models are artificial neural networks that are based on the transformer architecture, pre-trained on large data sets of unlabelled text, and able to generate novel human-like content. As of 2023, most LLMs have these characteristics and are sometimes referred to broadly as GPTs.
Solid-state storageSolid-state storage (SSS) is a type of non-volatile computer storage that stores and retrieves digital information using only electronic circuits, without any involvement of moving mechanical parts. This differs fundamentally from the traditional electromechanical storage, which records data using rotating or linearly moving media coated with magnetic material. Solid-state storage devices typically store data using electrically-programmable non-volatile flash memory, however some devices use battery-backed volatile random-access memory (RAM).
Thin clientIn computer networking, a thin client is a simple (low-performance) computer that has been optimized for establishing a remote connection with a server-based computing environment. They are sometimes known as network computers, or in their simplest form as zero clients. The server does most of the work, which can include launching software programs, performing calculations, and storing data.
Rich clientIn computer networking, a rich client (also called heavy, fat or thick client) is a computer (a "client" in client–server network architecture) that typically provides rich functionality independent of the central server. This kind of computer was originally known as just a "client" or "thick client," in contrast with "thin client", which describes a computer heavily dependent on a server's applications. A rich client may be described as having a rich user interaction.
Client (computing)In computing, a client is a piece of computer hardware or software that accesses a service made available by a server as part of the client–server model of computer networks. The server is often (but not always) on another computer system, in which case the client accesses the service by way of a network. A client is a computer or a program that, as part of its operation, relies on sending a request to another program or a computer hardware or software that accesses a service made available by a server (which may or may not be located on another computer).
Energy storageEnergy storage is the capture of energy produced at one time for use at a later time to reduce imbalances between energy demand and energy production. A device that stores energy is generally called an accumulator or battery. Energy comes in multiple forms including radiation, chemical, gravitational potential, electrical potential, electricity, elevated temperature, latent heat and kinetic. Energy storage involves converting energy from forms that are difficult to store to more conveniently or economically storable forms.
Classical conditioningClassical conditioning (also respondent conditioning and Pavlovian conditioning) is a behavioral procedure in which a biologically potent physiological stimulus (e.g. food) is paired with a neutral stimulus (e.g. the sound of a musical triangle). The term classical conditioning refers to the process of an automatic, conditioned response that is paired with a specific stimulus. The Russian physiologist Ivan Pavlov studied classical conditioning with detailed experiments with dogs, and published the experimental results in 1897.
Wear levelingWear leveling (also written as wear levelling) is a technique for prolonging the service life of some kinds of erasable computer storage media, such as flash memory, which is used in solid-state drives (SSDs) and USB flash drives, and phase-change memory. There are several wear leveling mechanisms that provide varying levels of longevity enhancement in such memory systems. The term preemptive wear leveling (PWL) has been used by Western Digital to describe their preservation technique used on hard disk drives (HDDs) designed for storing audio and video data.
Flash memoryFlash memory is an electronic non-volatile computer memory storage medium that can be electrically erased and reprogrammed. The two main types of flash memory, NOR flash and NAND flash, are named for the NOR and NAND logic gates. Both use the same cell design, consisting of floating gate MOSFETs. They differ at the circuit level depending on whether the state of the bit line or word lines is pulled high or low: in NAND flash, the relationship between the bit line and the word lines resembles a NAND gate; in NOR flash, it resembles a NOR gate.
Thermal energy storageThermal energy storage (TES) is achieved with widely different technologies. Depending on the specific technology, it allows excess thermal energy to be stored and used hours, days, months later, at scales ranging from the individual process, building, multiuser-building, district, town, or region. Usage examples are the balancing of energy demand between daytime and nighttime, storing summer heat for winter heating, or winter cold for summer air conditioning (Seasonal thermal energy storage).
