The most popular data terms on our website and a few from other popular sites, reflect the current hot data topics. Here’s our list with some additional context. 

Data fabric

Data-driven organizations need to be able to utilize data in the most efficient way possible, enabling easy access and delivery to drive digital services and real-time analytics. A data fabric offers a valuable framework to attain this goal. This architectural design aims to integrate, manage and enable easy access to enterprise data, wherever it resides. The goal of the data fabric is to streamline and simplify the management of diverse data types and data sources, to ensure that data is accessible to stakeholders and that digital apps provide a unified data experience. The data fabric breaks down silos and empowers teams with the information they need to make informed business decisions.

Key components of a data fabric include: 

• Data Ingestion: integrates diverse data sources such as databases, applications, and sensors into the fabric 
• Data Processing: includes data cleansing and transformation 
• Data Catalog and Metadata Management: provides a view of the data’s significance, location, and lineage
• Active metadata: as defined by Gartner, active metadata is the “continuous analysis of all user, system, and infrastructure reports and data governance that enable alignment and exception cases between data and their actual experiences.”
  Active metadata is usually augmented with ML spanning operational, business, and social metadata, as well as basic technical metadata. 

• Data Governance and Security: management and control of an organization’s data, covering the policies and processes that ensure data quality, integrity, security, privacy, and compliance 

• Automation: streamlines data integration and enables efficient data processing, integration, management, and governance; simplifies metadata management by automatically capturing metadata from various data sources and extracting specific attributes that ensures that metadata is up-to-date and accurate. 

A data fabric is designed to streamline and simplify the management of diverse data types and data sources, providing organizations with a cohesive data framework so that they can efficiently and effectively utilize their data across enterprise systems.

Interested in what data fabric can do for your organization? Download The Data Fabric Handbook 

Data democratization

The goal of data democratization is to make data accessible to a wide range of people within an organization, who can then utilize the data to expedite decisions based on data-driven insights. Data democratization breaks down the organizational, behavioral, and technological barriers that typically create data silos and prevent data from being shared among teams and applications. Data democratization can spark innovation by enabling employees to uncover valuable insights and propose innovative solutions, without relying on IT specialists or data professionals. With more accessible data and reduced dependence on manual processes, teams can benefit from unique insights that can be explored and take quicker action on crucial business. Developers and other teams can benefit from a wider scope of business and technical considerations to develop the data-driven services that are the foundation of modern business applications. On the flip side, with more users accessing greater volumes of data, organizations must take crucial steps to ensure proper data governance, to ensure that internal data standards and policies are applied to the availability, integrity, security, and usability of the data in the organization. Without comprehensive data governance, information may fall into the wrong hands, which can lead to severe consequences.

An Operational Data Hub, such as Smart DIH from GigaSpaces, is designed to facilitate data democratization by breaking down data silos, making data accessible, and enabling data teams to share data via microservices.  

To learn more about data democratization download this handbook

Real time data 

With 2.5 quintillion bytes of data created every day, the most valuable insights will be in the most up-to-date data. Real time data refers to two aspects, the speed at which the data is collected and integrated, and the speed at which the data is processed; both are essential to be able to produce the valuable insights that lead to smart business decisions. Real-time streaming data is increasingly important for mission critical transactions. For example, financial service companies use real-time analysis of transactions to spot fraud and halt transactions before they take place, resulting in significant cost savings. In point-of-sale systems, such as ecommerce, this real time data enables cross-sell and up-sell when customers are ready to check out. The Internet of Things (IoT) leverages real-time streams for real-time data from sensors and devices, enabling automation, predictive maintenance, and smart city initiatives.

Applying smart AI technology such as ML algorithms to real-time data sets helps businesses to achieve what Gartner analysts have termed “continuous intelligence in which real-time analytics are integrated into business operations.” 

Data grid 

A data grid is an architecture or set of services that provides the ability to access, modify and transfer extremely large amounts of geographically distributed data. This architecture is designed to distribute compute functions across multiple nodes, to take advantage of expanded storage capacity and computing power, so that organizations can meet SLAs with stringent response times. The nodes can be distributed by having individual units clustered in a single or multiple sites, or spread across multiple remote locations. Data grid software running on individual machines enables the grid to leverage the collective power to perform complex processing tasks for applications that include trade processing, ecommerce and payment processing systems among others. 

In memory data grids (IMDGs) store information in memory to achieve very high performance. By replicating data, synchronizing it across multiple servers IMDGs ensure the resiliency of the system and high availability of the data, even if a single node fails, the data is still accessible from other copies. A data grid approach breaks large, complex tasks into smaller subtasks and assigns them to individual nodes, so that multiple tasks or microservices can be run in parallel. IMDGs create a fast data layer for operations such as data look-up, storing states, and maintaining operational and business metrics. They can function as a primary data store that uses asynchronous persistence mechanisms (write-behind capability) to write the data into back-end databases.

Data tiering

Data tiering classifies data based on priority and business requirements, storing crucial data in high-availability locations and archiving less critical data. This mechanism can improve the overall performance of the data storage and retrieval, by keeping frequently accessed data in high-performance storage. Based on business requirements, data can be stored on premises, in the cloud or in hybrid configurations. Data tiering provides a clear and organized way to store different types of data, reduces expenses and increases data availability. 

Hot Data

Data that requires frequent access is referred to as hot data, and may include documents, photos, and project files. Placing the high-priority data in RAM enables ultra-fast response times for critical or intensively used data. Data that is aged, or that no longer fits the criteria (business rules) for the hot tier will be removed from this tier.

Cold Data

Cold data refers to data that is read-only and is accessed less frequently. This data doesn’t affect day-to-day business demands. Cold data may include financial, and customer patient records, videos, photos or backup, and disaster recovery data. Since persistent storage is usually less expensive than RAM, data that is needed less frequently by digital applications is fetched from that storage. 

Tiered storage reduces the RAM footprint significantly. The system should be configured with the expected capacity (RAM and disk space) for each tier. As data is stored according to performance and capacity requirements, data management becomes highly efficient, and infrastructure costs are minimized. Tiered storage can improve data efficiency by ensuring that data is stored on the appropriate tier. Data tiering can make it easier and faster to recover data in the event of a disaster, as only the most critical data from the high-performance tier needs to be recovered.

Last words

In-depth knowledge of the hottest current data terms may not result in a promotion, but it does demonstrate grasp of the evolving data landscape and assists in maintaining a relevant skill set. We hope that you found this post relevant. Check out the GigaSpaces Glossary for more information related to data, architecture and real time processing.