Cloud analytics describes the application of analytic algorithms in the cloud against data in a private or public cloud to then deliver a result of interest. Cloud analytics involves deployment of scalable cloud computing with powerful analytic software to identify patterns in data and to extract new insights. More and more businesses rely on data analysis to gain a competitive advantage, to advance scientific discovery, or to improve life in all sorts of ways. Data analytics has therefore become an increasingly valuable tool as the quantity and the value of data continue to climb.
Cloud analytics is often associated with artificial intelligence (AI), machine learning (ML), and deep learning (DL). And it is commonly used in industry applications such as scientific research in genomics or in oil and gas fields, business intelligence, security, Internet of Things (IoT), and many others. In fact, any industry can benefit from data analytics to improve organizational performance and to drive new value.
By leveraging AI and other analytics approaches, organizations of all sizes can quickly make data-driven decisions to gain efficiencies in their products and services. The cloud is an indispensable platform that enables quick experimentation of ideas through proofs of concept (POCs) and provides a rich software ecosystem for building AI applications and for training DL models.
AI is increasingly being used in multiple industry verticals to support important business needs such as automating business processes, providing cognitive insights through data analysis, and interacting with customers with natural language processing. DL, the next level of ML, is effective at learning from large volumes of data to mimic the human brain’s pattern recognition (for example, images, speech, and text).
Cloud infrastructure analytics, a subset of cloud analytics, focuses on the analysis of data that’s associated with IT infrastructure, on the premises or in the cloud. The goal is to identify I/O patterns, to evaluate application performance, to identify policy compliance, and to support capacity management and infrastructure resilience.
How Cloud Analytics Can Help Drive Growth and Scalability
Data analytics isn’t a new concept. The term “big data” was introduced in the late 90’s to describe large data sets often found in specific industries such as energy, financial services, healthcare, space travel, and other scientific disciplines. The ability to analyze and extract insights from large data sets, data analytics, accelerated with the introduction of analytics software, such as Apache Hadoop. As analytics technologies and workloads moved to the cloud they became known as Cloud Analytics. Cloud Analytics has rapidly increased the ease, accessibility, and capability of performing complex data analysis on very large data sets.
Cloud Analytics is particularly interesting for serveral reasons:
- The amount of data collected around the world is growing at staggering rates and much of it is being created and pooled in the cloud or at IOT endpoints.
- Services delivered in the cloud are much easier to deploy as they are delivered as an automated service and they don’t require deployment and maintenance of physical hardware.
- The cloud business model enables a user to turn services on and off as needed. This consumption approach allows customers to pay only for what they use when they use it, thereby removing the responsibility of procuring and managing capital infrastructure as well as reducing data center space.
- The cloud allows users to deploy the right quantity of IT resources to match the problem at hand. Dynamic resizing of resources means that users can easily apply compute and storage and scale them as needs change. Users are spared the requirement to procure a fixed capacity of physical IT equipment for all of their data analysis projects.
- Building a hybrid analytics solution is effective for users who wish to leverage the cloud to test a new analytics project as a POC before committing to investments on-premises.
Cloud Analytics empowers organizations to:
- Test genomic data to better understand genetic disease and how to offer cures
- Identify patterns in speech, images and videos in order to improve customer satisfaction and improve customer service
- Study buying behavior to improve product availability and delivery
- Identify patterns of disease reporting to improve availability of medicine and vaccines
- Analyze hybrid cloud infrastructures to improve application performance and optimize IT costs.
NetApp's Cloud Analytics Solutions
NetApp offers solutions designed to help improve the performance, management and protection of data. Data is at the core of any analytics project and NetApp offers the IT solutions across the hybrid cloud to help accelerate data analysis and improve data availability.
Cloud Volumes Service
NetApp Cloud Volumes Service is a cloud-native file service with support for NFS and SMB protocols. It offers high I/O performance, high availability, and ease of use, with advanced features such as efficient snapshots and clones. Available on AWS and Google Cloud, Cloud Volumes Service is ideally suited for the most demanding analytic workloads.
Azure NetApp Files
Azure NetApp Files is a simple to use Microsoft native file service available on the Azure cloud portal. Azure NetApp Files offers exceptional performance with advanced data management and integration with Azure services to support the most performance sensitive file workloads.
NetApp Cloud Insights is a SaaS based product that offers real-time monitoring, troubleshooting and cost optimization for a customer’s global IT infrastructure. With scalability from small to large IT deployments, Cloud Insights is specifically designed for modern dynamic cloud technologies to ensure high performance and cost optimization.