Cloud computing has several clear business models. SaaS delivers software, upgrades and maintenance as a service, saving customers money by eliminating costs of ownership that the cloud provider now bears. Several technology factors contribute to SaaS' increasing popularity including protocol standardization, the ubiquity of Web browsing, access to broadband networks, and rapid application development. It’s not perfect – people have legitimate concerns about data security, governance, vendor lock-in, and data portability, but based on its success, the advantages of SaaS seem to be outweighing its challenges. And the market segment is growing fast.
Another cloud computing model is IaaS, where the customer outsources the compute infrastructure to a cloud provider. This model is gaining traction, especially for application development and testing. App developers are able to take the capital they would otherwise have to spend on buying computing gear and target it to specific development projects that are underway in Internet data centers. The problem with IaaS is that cloud software development doesn’t necessarily translate well into on-premises deployments and many developers prefer to develop SaaS.
Storage in the cloud is yet another business model with different dynamics. While SaaS and Iaas are strongly oriented towards cloud deployments, there are strong pressures driving cloud storage toward on-premises deployments. While storing data in the cloud for SaaS and IaaS computing is certainly important, the vast amount of data still resides on-premises where its growth is largely unchecked. If the cloud storage is going to succeed, it needs to become relevant to the people managing data in corporate on-premises data centers.
In this new era of big data, sensors can be included in almost everything made. This “Internet Of Things” generates mountains of new data with exciting potential to be turned into invaluable information. As a vendor, if you make a product or solution that when deployed by your customers produces data about its ongoing status, condition, activity, usage, location, or practically any other useful information, you can now potentially derive deep intelligence that can be used to improve your products and services, better satisfy your customers, improve your margins, and grow market share.
For example, such information about a given customer’s usage of your product and its current operating condition, combined with knowledge gleaned from all of your customers’ experiences, enables you to be predictive about possible issues and proactive about addressing them. Not only do you come to know more about a customer’s implementation of your solution than the customer himself, but you can now make decisions about new features and capabilities based on hard data.
The key to gaining value from this “Internet Of Things” is the ability to make sense out of the kind of big data that it generates. One set of current solutions addresses data about internal IT operations including “logfile” analysis tools like Splunk and VMware Log Insight. These are designed for a technical user focused on recent time series and event data to improve tactical problem “time-to-resolution”. However, the big data derived from customer implementations is generally multi-structured across streams of whole “bundles” of complexly related files that can easily grow to PB’s over time. Business user/analysts are not necessarily IT-skilled (e.g. marketing, support, sales…) and the resulting analysis to be useful must at the same time be more sophisticated and be capable of handling dynamic changes to incoming data formats.
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We define online backup as using the cloud to provide users with a highly scalable and elastic repository for their backup data. This is true across all online backup users but enterprise has specific requirements and some risks that consumer and SMB customers do not share. Consumer and SMB – including education and small government agencies – primarily require acceptable backup and restore performance, plus security and compliance reporting in their online backup. The enterprise needs these things too but they are dealing with additional pressures from backing up larger data sets across multiple remote sites and/or storage systems and applications. Here is what to know when you consider cloud backup vendors for your enterprise backup system.
Hadoop is coming to enterprise IT in a big way. The competitive advantage that can be gained from analyzing big data is just too “big” to ignore. And the amount of data available to crunch is only growing bigger, whether from new sensors, capture of people, systems and process “data exhaust”, or just longer retention of available raw or low-level details. It’s clear that enterprise IT practitioners everywhere are soon going to have to operate scale-out computing platforms in the production data center, and being the first, most mature solution on the scene, Hadoop is the likely target. The good news is that there is now a plethora of Hadoop infrastructure options to choose from to fit almost every practical big data need – the challenge now for IT is to implement the best solutions for their business client needs.
While Apache Hadoop as originally designed had a relatively narrow application for only certain kinds of batch-mode parallel algorithms applied over unstructured (or semi-structured depending on your definition) data, because of its widely available open source nature, commodity architecture approach, and ability to extract new kinds of value out of previously discarded or ignored data sets, the Hadoop ecosystem is rapidly evolving and expanding. With recent new capabilities like YARN that opens up the main execution platform to applications beyond batch MapReduce, the integration of structured data analysis, real-time streaming and query support, and the roll out of virtualized enterprise hosting options, Hadoop is quickly becoming a mainstream data processing platform.
There has been much talk that in order to derive top value from big data efforts, rare and potentially expensive data scientist types are needed to drive. On the other hand, there is an abundance of higher level analytical tools and pre-packaged applications emerging to support the existing business analyst and user with familiar tools and interfaces. While completely new companies have been founded on the exciting information and operational intelligence gained from exploiting big data, we expect wider adoption by existing organizations based on augmenting traditional lines of business with new insight and revenue enhancing opportunity. In addition, a Hadoop infrastructure serves as a great data capture and ETL base for extracting more structured data to feed downstream workflows, including traditional BI/DW solutions. No matter how you want to slice it, big data is becoming a common enterprise workload, and enterprise IT infrastructure folks will need to deploy, manage, and provide Hadoop services to their businesses.