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A data hub is a center of data exchange that is supported by data science, data engineering, and data warehouse technologies to interact with endpoints such as applications and algorithms. [1]
A data hub differs from a data warehouse in that it is generally unintegrated and often at different grains. It differs from an operational data store because a data hub does not need to be limited to operational data.
A data hub differs from a data lake by homogenizing data and possibly serving data in multiple desired formats, rather than simply storing it in one place, and by adding other value to the data such as de-duplication, quality, security, and a standardized set of query services. A data lake tends to store data in one place for availability, and allow/require the consumer to process or add value to the data.
Data hubs are ideally the "go-to" place for data within an enterprise, so that many point-to-point connections between callers and data suppliers do not need to be made, and so that the data hub organization can negotiate deliverables and schedules with various data enclave teams, rather than being an organizational free-for-all as different teams try to get new services and features from many other teams.
In computing, a data warehouse, also known as an enterprise data warehouse (EDW), is a system used for reporting and data analysis and is considered a core component of business intelligence. Data warehouses are central repositories of integrated data from one or more disparate sources. They store current and historical data in one single place that are used for creating analytical reports for workers throughout the enterprise. This is beneficial for companies as it enables them to interrogate and draw insights from their data and make decisions.
In computing, a database is an organized collection of data stored and accessed electronically through the use of a database management system. Small databases can be stored on a file system, while large databases are hosted on computer clusters or cloud storage. The design of databases spans formal techniques and practical considerations, including data modeling, efficient data representation and storage, query languages, security and privacy of sensitive data, and distributed computing issues, including supporting concurrent access and fault tolerance.
In computer science, an instruction set architecture (ISA), also called computer architecture, is an abstract model of a computer. A device that executes instructions described by that ISA, such as a central processing unit (CPU), is called an implementation.
Logistics is a part of supply chain management that deals with the efficient forward and reverse flow of goods, services, and related information from the point of origin to the point of consumption according to the needs of customers. Logistics management is a component that holds the supply chain together. The resources managed in logistics may include tangible goods such as materials, equipment, and supplies, as well as food and other consumable items.
Inventory or stock refers to the goods and materials that a business holds for the ultimate goal of resale, production or utilisation.
In computing, extract, transform, load (ETL) is a three-phase process where data is extracted, transformed 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.
An organizational structure defines how activities such as task allocation, coordination, and supervision are directed toward the achievement of organizational aims.
In computing, a word is the natural unit of data used by a particular processor design. A word is a fixed-sized datum handled as a unit by the instruction set or the hardware of the processor. The number of bits or digits in a word is an important characteristic of any specific processor design or computer architecture.
Federated search retrieves information from a variety of sources via a search application built on top of one or more search engines. A user makes a single query request which is distributed to the search engines, databases or other query engines participating in the federation. The federated search then aggregates the results that are received from the search engines for presentation to the user. Federated search can be used to integrate disparate information resources within a single large organization ("enterprise") or for the entire web.
A dimension is a structure that categorizes facts and measures in order to enable users to answer business questions. Commonly used dimensions are people, products, place and time.
Dimensional modeling (DM) is part of the Business Dimensional Lifecycle methodology developed by Ralph Kimball which includes a set of methods, techniques and concepts for use in data warehouse design. The approach focuses on identifying the key business processes within a business and modelling and implementing these first before adding additional business processes, as a bottom-up approach. An alternative approach from Inmon advocates a top down design of the model of all the enterprise data using tools such as entity-relationship modeling (ER).
In information science and information technology, single source of truth (SSOT) architecture, or single point of truth (SPOT) architecture, for information systems is the practice of structuring information models and associated data schemas such that every data element is mastered in only one place, providing data normalization to a canonical form.
A column-oriented DBMS or columnar DBMS is a database management system (DBMS) that stores data tables by column rather than by row. Benefits include more efficient access to data when only querying a subset of columns, and more options for data compression. However, they are typically less efficient for inserting new data.
Azure DevOps Server is a Microsoft product that provides version control, reporting, requirements management, project management, automated builds, testing and release management capabilities. It covers the entire application lifecycle and enables DevOps capabilities. Azure DevOps can be used as a back-end to numerous integrated development environments (IDEs) but is tailored for Microsoft Visual Studio and Eclipse on all platforms.
Operational database management systems, are used to update data in real-time. These types of databases allow users to do more than simply view archived data. Operational databases allow you to modify that data, doing it in real-time. OLTP databases provide transactions as main abstraction to guarantee data consistency that guarantee the so-called ACID properties. Basically, the consistency of the data is guaranteed in the case of failures and/or concurrent access to the data.
Master data management (MDM) is a technology-enabled discipline in which business and information technology work together to ensure the uniformity, accuracy, stewardship, semantic consistency and accountability of the enterprise's official shared master data assets.
In information systems, applications architecture or application architecture is one of several architecture domains that form the pillars of an enterprise architecture (EA).
Data vault modeling is a database modeling method that is designed to provide long-term historical storage of data coming in from multiple operational systems. It is also a method of looking at historical data that deals with issues such as auditing, tracing of data, loading speed and resilience to change as well as emphasizing the need to trace where all the data in the database came from. This means that every row in a data vault must be accompanied by record source and load date attributes, enabling an auditor to trace values back to the source. The concept was published in 2000 by Dan Linstedt.
Agile Business Intelligence (BI) refers to the use of Agile software development for BI projects to reduce the time it takes for traditional BI to show value to the organization, and to help in quickly adapting to changing business needs. Agile BI enables the BI team and managers to make better business decisions, and to start doing this more quickly.
A data lake is a system or repository of data stored in its natural/raw format, usually object blobs or files. A data lake is usually a single store of data including raw copies of source system data, sensor data, social data etc., and transformed data used for tasks such as reporting, visualization, advanced analytics and machine learning. A data lake can include structured data from relational databases, semi-structured data, unstructured data and binary data. A data lake can be established "on premises" or "in the cloud".
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