Stream processingIn computer science, stream processing (also known as event stream processing, data stream processing, or distributed stream processing) is a programming paradigm which views streams, or sequences of events in time, as the central input and output objects of computation. Stream processing encompasses dataflow programming, reactive programming, and distributed data processing. Stream processing systems aim to expose parallel processing for data streams and rely on streaming algorithms for efficient implementation.
Data managementData management comprises all disciplines related to handling data as a valuable resource. The concept of data management arose in the 1980s as technology moved from sequential processing (first punched cards, then magnetic tape) to random access storage. Since it was now possible to store a discrete fact and quickly access it using random access disk technology, those suggesting that data management was more important than business process management used arguments such as "a customer's home address is stored in 75 (or some other large number) places in our computer systems.
Open dataOpen data is data that is openly accessible, exploitable, editable and shared by anyone for any purpose. Open data is licensed under an open license. The goals of the open data movement are similar to those of other "open(-source)" movements such as open-source software, open-source hardware, open content, open specifications, open education, open educational resources, open government, open knowledge, open access, open science, and the open web. The growth of the open data movement is paralleled by a rise in intellectual property rights.
Vector processorIn computing, a vector processor or array processor is a central processing unit (CPU) that implements an instruction set where its instructions are designed to operate efficiently and effectively on large one-dimensional arrays of data called vectors. This is in contrast to scalar processors, whose instructions operate on single data items only, and in contrast to some of those same scalar processors having additional single instruction, multiple data (SIMD) or SWAR Arithmetic Units.
Embedded systemAn embedded system is a computer system—a combination of a computer processor, computer memory, and input/output peripheral devices—that has a dedicated function within a larger mechanical or electronic system. It is embedded as part of a complete device often including electrical or electronic hardware and mechanical parts. Because an embedded system typically controls physical operations of the machine that it is embedded within, it often has real-time computing constraints. Embedded systems control many devices in common use.
Extract, transform, loadIn computing, extract, transform, load (ETL) is a three-phase process where data is extracted, transformed (cleaned, sanitized, scrubbed) and loaded into an output data container. The data can be collated from one or more sources and it can also be output to one or more destinations. ETL processing is typically executed using software applications but it can also be done manually by system operators. ETL software typically automates the entire process and can be run manually or on reoccurring schedules either as single jobs or aggregated into a batch of jobs.
Data mappingIn computing and data management, data mapping is the process of creating data element mappings between two distinct data models.
Transactional memoryIn computer science and engineering, transactional memory attempts to simplify concurrent programming by allowing a group of load and store instructions to execute in an atomic way. It is a concurrency control mechanism analogous to database transactions for controlling access to shared memory in concurrent computing. Transactional memory systems provide high-level abstraction as an alternative to low-level thread synchronization. This abstraction allows for coordination between concurrent reads and writes of shared data in parallel systems.
Lock (computer science)In computer science, a lock or mutex (from mutual exclusion) is a synchronization primitive: a mechanism that enforces limits on access to a resource when there are many threads of execution. A lock is designed to enforce a mutual exclusion concurrency control policy, and with a variety of possible methods there exists multiple unique implementations for different applications. Generally, locks are advisory locks, where each thread cooperates by acquiring the lock before accessing the corresponding data.
Online transaction processingOnline transaction processing (OLTP) is a type of database system used in transaction-oriented applications, such as many operational systems. "Online" refers to that such systems are expected to respond to user requests and process them in real-time (process transactions). The term is contrasted with online analytical processing (OLAP) which instead focuses on data analysis (for example planning and management systems).
Dataflow programmingIn computer programming, dataflow programming is a programming paradigm that models a program as a directed graph of the data flowing between operations, thus implementing dataflow principles and architecture. Dataflow programming languages share some features of functional languages, and were generally developed in order to bring some functional concepts to a language more suitable for numeric processing. Some authors use the term datastream instead of dataflow to avoid confusion with dataflow computing or dataflow architecture, based on an indeterministic machine paradigm.
DataflowIn computing, dataflow is a broad concept, which has various meanings depending on the application and context. In the context of software architecture, data flow relates to stream processing or reactive programming. Dataflow computing is a software paradigm based on the idea of representing computations as a directed graph, where nodes are computations and data flow along the edges. Dataflow can also be called stream processing or reactive programming. There have been multiple data-flow/stream processing languages of various forms (see Stream processing).
Two-phase commit protocolIn transaction processing, databases, and computer networking, the two-phase commit protocol (2PC, tupac) is a type of atomic commitment protocol (ACP). It is a distributed algorithm that coordinates all the processes that participate in a distributed atomic transaction on whether to commit or abort (roll back) the transaction. This protocol (a specialised type of consensus protocol) achieves its goal even in many cases of temporary system failure (involving either process, network node, communication, etc.
Atomic commitIn the field of computer science, an atomic commit is an operation that applies a set of distinct changes as a single operation. If the changes are applied, then the atomic commit is said to have succeeded. If there is a failure before the atomic commit can be completed, then all of the changes completed in the atomic commit are reversed. This ensures that the system is always left in a consistent state. The other key property of isolation comes from their nature as atomic operations.
Three-phase commit protocolIn computer networking and databases, the three-phase commit protocol (3PC) is a distributed algorithm which lets all nodes in a distributed system agree to commit a transaction. It is a more failure-resilient refinement of the two-phase commit protocol (2PC). A two-phase commit protocol cannot dependably recover from a failure of both the coordinator and a cohort member during the Commit phase. If only the coordinator had failed, and no cohort members had received a commit message, it could safely be inferred that no commit had happened.
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.
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.
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.
Commitment orderingCommitment ordering (CO) is a class of interoperable serializability techniques in concurrency control of databases, transaction processing, and related applications. It allows optimistic (non-blocking) implementations. With the proliferation of multi-core processors, CO has also been increasingly utilized in concurrent programming, transactional memory, and software transactional memory (STM) to achieve serializability optimistically. CO is also the name of the resulting transaction schedule (history) property, defined in 1988 with the name dynamic atomicity.
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.
