Data services bring more business value to data and can therefore be implemented as part of cloud-native applications (an integral part of an open hybrid cloud IT strategy)
1)What is a data service?
A data service (or “data as a service”) is a collection of small, independent, and loosely coupled functions that augment, organize, share, or compute information collected and held in data storage volumes.
Data services can enhance traditional data by increasing its resiliency, availability, and validity, and adding corresponding characteristics to data it does not have, such as metadata.
2)How does the data service work?
A data service is an independent unit of software functionality that provides data features that it does not already possess. Data services can improve the availability, resiliency, and understandability of data, thereby making data more useful to users and programs.
Data service functions turn inputs into outputs. Input refers to various raw datasets (data not processed for a specific purpose) configured in their native format and stored in physical, virtual, or cloud-based storage volumes. And the output usually refers to:
- Organizable: Consolidation, batch processing, and structuring of data, typically from structured (databases), semi-structured (data warehouses), or unstructured ( data lakes ) ) sources.
- Transportable: Data moves from its original location across the network to an endpoint (such as an application or platform).
- Disciplined: The processing of data, usually as part of data modeling, analytics, or artificial intelligence/machine learning (AI/ML) software.
3)What are data services for?
Data is kept in storage volumes.
Data services extract raw data from sources (such as customer records in an online transaction processing (OLTP) database, property damage information in a data warehouse, and images or videos in a data lake), apply governance principles, and organize and maintain it so that Make data available to applications and accessible to users.
Data services are an important part of a big data strategy, as it enables large-scale collection of structured, semi-structured, and unstructured data stored in various places.
Software-defined storage is a static data service
Data is moved from its storage source to an application or platform, usually in real-time
. Data services can create data pipelines to help data move continuously between multiple endpoints.
For example, data services can help businesses move from batch-oriented data processing to event-driven data processing by processing data as soon as it is generated.
Additionally, data services help ensure that data is never actually removed from the source, allowing multiple endpoints to use the same data point at the same time. Take advantage of this to create scalable event-driven architectures.
Group active data into datasets for use by data science, data analysis, and data modeling software.
Data services help improve data access for high-performance, intelligent data processing platforms such as AI/ML and deep learning tools. Depending on the data service, data in motion may involve a collection of small, independent, and loosely coupled services, typically packaged in containers and orchestrated by the Kubernetes platform.
Here are some Kubernetes storage patterns
Cloud-native application development cannot take place without data services to assist developers and data scientists as data moves between systems.
Multiple codes commits using the same data can increase build times, while data services such as Red Hat® OpenShift® Data Foundation can reduce the time dependencies of concurrent builds.
4)Traditional storage and data services
The actual collection and retention of raw digital information (the bits and bytes behind applications, network protocols, documents, media, contacts, user preferences, etc.).
Every time you save a document and choose a location, you go through the data storage process. Users typically look at data storage at the infrastructure level and rarely associate it with storage volumes.
For example, there is often no native way to view every file, block, or object saved across workstations, cloud storage providers, and external hard drives, making exploration of data storage reliant on manual operations, and method comparisons single.
Software that uses data held in traditional data storage volumes as input to create specific outputs; or software that amplifies traditional data by increasing its resiliency, availability, and effectiveness
. Users typically interact with data services as part of an application, making the process very flexible and customizable.
For example, the data services provided by
Red Hat OpenShift Data Foundation abstract the storage infrastructure so that data can be stored in many different locations and still act as a single persistent store.
5)Who is using data services?
Massachusetts Open Cloud (Massachusetts Open Cloud, MOC) is using data services
. MOC is a not-for-profit initiative sponsored by universities, government agencies, and businesses.
The initiative aims to develop a common cloud-based infrastructure for big data analytics for businesses, governments, and nonprofits.
MOC uses Red Hat Ceph Storage, a software-defined storage service, to organize and share large amounts of data with multiple entities running custom data analytics platforms.