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What is Data Services?

Data Services

Data services (also known as data as a service) are sets of small, independent, and not directly connected functions that enhance. Organize, share, or calculate information collected and stored in data storage volumes. They allow traditional data to enhanced since they improve its resistance, availability, and validity. In addition, they add specific characteristics that are not usually included since their creation, as is the case of metadata.

How do Data Services Work?

Data services are self-contained units of software purposes that add features to data that it does not yet possess. As a result, they can increase their availability, resiliency, and understandability, making them more helpful to users and programs.

These functions transform inputs into results. The inputs are different data sets that have not been process for a particular purpose. Instead, they have their original configuration stored on physical, virtual, or cloud storage volumes. The results are usually:

What is Data Services Use for?

They are those that are store in storage volumes. These services pull raw data from sources (e.g., customer records from online transaction processing (OLTP) databases; stuff damage information from data warehouses; and images and videos of data lakes) and apply principles of control, organization, and maintenance that make them useful for applications and easy for users to access. They are an essential element of great data strategies because they make sense of the massive sets of organized, semi-structured, and unstructured data.

Data in Motion

They are the ones that move from the place where they are stored to an application or platform, usually immediately. Data services can create channels that allow them to move from one end to another without interruption. For example, thanks to these services, companies can work with data as it is generate, moving from batch to event-based processing. They also help ensure they are never delete from their source, allowing multiple endpoints to simultaneously use the same data point. This serves to create scalable and event-driven architectures.

Data in Action

It is operational data group into sets and use in modeling software, data science, and data analysis. In addition. Data services help improve your access to high-performance, intelligent platforms for data processing, such as deep learning and AI/ML tools. Depending on the service in question, the data in action could span small, independent, and loosely connected services, typically containerized and orchestrated with a Kubernetes platform.

Some storage patterns for Kubernetes

Application Development in the cloud is impossible without the right data services, allowing developers and analysts to work together as they move from one system to another. Multiple code change commits that use the same data can extend design periods. However, a data service like Red Hat® OpenShift® Data Foundation reduces the time dependency of concurrent builds.

Comparison between Traditional Storage and Data Services

It refers to the actual collection and preservation of basic digital information, that is, the bits and bytes behind applications, network protocols, documents, multimedia content, address books, user preferences, etc. For example, the data-saving process is done if you save a document and select a location. What the user sees from the data warehouse generally stays with the infrastructure and is not related to capacity. For example, there is no built-in functionality to view the files, blocks, or objects stored on a workstation, cloud storage provider, and an external hard drive. Therefore, examining data storage is a highly manual and monolithic task.

Data Services

It is software that uses the data stored in traditional storage volumes as inputs to generate specific results or software that improves their resistance, availability, and validity, enhancing them. Users typically network with data services as part of an application, making for a very flexible and customizable process. For example, Red Hat Open Shift Data Foundation extracts the storage infrastructure so that data can be store in many different places but acts as a single, permanent repository.

Who Uses Data Services?

Massachusetts Open Cloud (MOC) is a nonprofit initiative of various universities, government organizations, and businesses that uses data services. It was create to develop a shared cloud infrastructure for businesses, governments, and nonprofits to analyze big data. MOC used Red Hat Ceph Storage, a software-defined storage service. To organize and share large amounts of data with the various entities running custom platforms to analyze it.

With no previous experience with Open Shift Container Storage, our team was able to set up two different Open Shift clusters and perform a full Db2 Warehouse performance validation in less than two weeks.

Piotr Mierzejewski

Director of Db2 Development, IBM Data & AI

Why Choose Red Hat?

Our data facilities work well with all data storage providers and are also a great complement to cloud application development.

Use the data center or cloud of your choice, and deploy that data to ever-evolving applications built in the cloud. With our services, you can improve your company’s old data and send it directly to your applications to obtain relevant information to solve the future’s biggest challenges.

Conclusion

A database is an organized collection of data store in a computer system controlled by a database management system (DBMS). They contain large amounts of information and allow multiple users to quickly and securely access and query it for use. They can be anything from email lists to employee information.

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