What is the difference between service and data service

Connect with us. Sign up. Term of the Day. Best of Techopedia weekly. News and Special Offers occasional. Data Services. Techopedia Explains Data Services. What Does Data Services Mean? Techopedia Explains Data Services The category of data services is quite broad. Share this Term. Platform as a Service Infrastructure as a Service. In this section each benefit is explained in more detail:. Scope of data services : data services are primarily concerned with actions on data entities - period.

Thus data services scope include various manipulations on data entities, aggregation of data across multiple disparate data sources, a facility to consume data interfaces from a variety of platforms using a variety of transport protocols, mapping between logical interface with physical provider interfaces, and graceful error handling of data service errors.

Data sourcing and transfer of very large data extracts could use data services as well although traditionally those areas use ETL and data profiling tools. Business process orchestration logic and execution of line of business rules are out of scope for data services since they inhibit reuse. Schema design needs to follow several guidelines and best practices and it is important to review the key ones here. Data services consumption patterns : Data services consumption needs to be examined from several perspectives:.

I am Vijay Narayanan, a software development team lead building reusable data services and business process automation components working for a financial services firm. I have worked on several software projects ranging from single user systems to large, distributed, multi-user platforms with several services. Join a community of over , senior developers. View an example. You need to Register an InfoQ account or Login or login to post comments.

But there's so much more behind being registered. Your message is awaiting moderation. Thank you for participating in the discussion. See Bill Poole's excellent explanation. Sriram Are data services an anti-pattern? Just like everything else in architecture, the answer is based on the technology, business, and organizational contexts. The number of data sources, the complexity of data entity structure and data access patterns, volume of data exchange, performance, and degree of decoupling needed between consumers and providers all play into choosing the implementation characteristics of data services.

If your organization has several silos of data including a mixture of legacy repositories and each data source integration is unique and costs resources to build and test become factors as well. There are specific concerns and techniques when implementing read-only services vs. Isn't this just rehashing the entity services idea yet again?

Personally I totally agree with Bill Poole's analysis. There's a good discussion of this on InfoQ itself actually: www. Instead SOA Data Services are enterprise data management end-points that expose highly optimized engines for working on all types of data. By embracing DaaS, companies can improve the agility of data workloads, reduce time-to-insight, and increase the reliability and integrity of their data.

Learn what DaaS means, why and how companies are leveraging it, and how to get started with a cloud-first, DaaS-based strategy for data integration, storage, and management. DaaS is similar to software as a service, or SaaS, a cloud computing strategy that involves delivering applications to end-users over the network, rather than having them run applications locally on their devices.

Just as SaaS removes the need to install and manage software locally, DaaS outsources most data storage, integration, and processing operations to the cloud.

While the SaaS model has been popular for more than a decade, DaaS is a concept that is only now beginning to see widespread adoption. That is due in part to the fact that generic cloud computing services were not initially designed for handling massive data workloads; instead, they catered to application hosting and basic data storage as opposed to data integration, analytics, and processing.

Processing large data sets via the network was also difficult in the earlier days of cloud computing, when bandwidth was often limited. Today, however, the advent of low-cost cloud storage and bandwidth, combined with cloud-based platforms designed specifically for fast, large-scale data management and processing, has made DaaS just as practical and beneficial as SaaS. Compared to on-premises data storage and management, DaaS provides several key advantages with regard to speed, reliability, and performance.

They include:. DaaS solutions have been slower to catch on than SaaS and other traditional cloud-based services. However, as DaaS matures and the cloud becomes increasingly central to modern business operations, a number of organizations are now leveraging DaaS successfully. PointsBet uses a cloud-based data solution to manage its unique compliance and scaling requirements. The company can easily adjust its operations to meet the flucuating demand of online gaming and ensure its operating within local and international government regulations.