Streaming andProcessing
Most organizations are processing data in the cloud in some capacity and increasing investments in new cloud-based initiatives. As cloud use cases spur the streaming and processing of data to increase application portability, automation, maintenance, security, and standardization, the effectiveness of cloud models is helping organizations anticipate and respond to changes (such as during the pandemic) to increase the adoption of cloud services.
As cloud services are essential in digital transformation across industries, regions, and boundaries, use cases such as smart cities, connected vehicles in automobiles, virtual healthcare, and more push for new data-streaming processing-speed requirements. Using cloud computing increases the ability to assess risks proactively,
helps the organization’s strategic planning, and supports mainstream operations. Cloud has a huge impact on business applications, business operating models, and IT architectures. The influence of cloud on IT operating models extends through IT operations management, security, networking, and business functions; e.g., clouds are becoming more prominent in technology purchasing. Serverless applications help organizations spend less time on infrastructure and more time on business functionality. Composable applications have the ability to provide pre-packaged functional capabilities to support businesses’ quest to be agile, dynamic, and adaptive.
Here are some of the examples of specialized services that provide solutions for high with fast streaming and fast processing:
(QCaaS) Quantum computing as a service provides access to quantum computing (QC) systems and associated services, enabling them to explore enterprise-relevant use cases and devise quantum algorithms for a highly specialized set of problems; e.g., optimization, simulation, search, linear systems, and security-related use cases.
SaaS operations (SaaSOps) are the operational aspects of business end users who use SaaS applications in the cloud, and those who deploy their own software in the cloud and want to operate the software like a SaaS provider. It also includes other functionalities, such as ITOps, PlatformOps, SecOps, AIOps, MLOps, and site reliability engineering (SRE) capabilities. SaaSOps remains a fragmented model. Many SaaSOps aspects are performed separately. However, currently, there is no consolidated approach from SaaS providers to address data warehouse observability.
SaaS (software as a service)-centric APIs are cloud application services whose primary interface method is programmatic, accessed via request- or event-based APIs. The intent of SaaS-centric APIs is to contribute a set of Application Programming Interface (API) building blocks for further development of custom application functionalities, as well as experience and related processes. SaaS-centric APIs allow packaged business functionality, such as banking, commerce, or mapping services, to be used as building blocks in larger applications. This empowers organizations to create application experiences that are more advanced than relying on starting from scratch. This translates to business empowerment and faster, safer, and more efficient innovation.
Cloud high-performance computing services (HPC) offer modernized supercomputing environments for data processing that lead to digital innovation with integrated cloud services, automation, elasticity, and AI-based management. Cloud High performance Computing (HPC) market providers offer platforms that support scalable access to specialized HPC architectures, enabling enterprises to address diverse and complex use cases, including traditional computational engineering applications and advanced AI.
Edge computing uses distributed computing, in which data storage and processing are placed in the location of data creation and use, respectively. Edge computing locates data and workloads and optimizes for factors like latency, bandwidth, autonomy, and regulatory/security considerations. Edge-computing locations are found near places where physical sensors and digital systems converge to the cloud or a centralized on-premises data center. Edge computing improves efficiency and cost control by keeping computation processing close to the edge, where the data is generated or acted upon, for better automation and quality control, offering more business opportunities and growth with better customer experience and real-time business interactions.
Artificial intelligence (AI) services are used to automate tasks, improve efficiency, and make better decisions. AI services can be used in a variety of industries, including health care, finance, retail, and manufacturing. This provides a single or a collection of components and services on the cloud; e.g., AI modeling tools, APIs for pre-built services, and associated middleware that enables the designing, building, training, deployment, and consumption of machine learning (ML) models running on the cloud. These services include vision, language, and automated ML services to create new models and customize pre-built models. The adoption of these services is rapidly accelerating among developers and citizen data scientists for use with language, vision, and ML applications to automate and accelerate the achievement of business objectives.
Some of the capabilities of AI services are natural language processing (NLP) of human language, which can be used for machine translation, text analytics, and chatbots. Computer vision enables computers to see and understand the world around them and can be used in use cases for image recognition, object detection, and facial recognition. Machine (M/C) learning enables computers to learn from data without being programmed by a human element and can be used in use cases such as fraud detection, predictive analytics, and customer segmentation. Robotics deals with the design, construction, operation, and application of robots and can be used in use cases such as manufacturing, health care, and logistics.
