Leading vendors are doing more than simply offering walls and fixes—they are increasingly emphasizing more holistic enterprise security approaches that guard data on many layers or levels. For example, as shown in a recent survey of 350 data managers that was conducted by Unisphere Research and sponsored by Oracle among members of the Independent Oracle Users Group (IOUG), there is a wide range of data which gets copied and proliferated for purposes of development, testing, and backup. In addition, according to the research report, “Closing the Security Gap: 2012 IOUG Enterprise Data Security Survey,” respondents worry more about human error than internal or external hackers as their greatest security risk—suggesting a need for well-rounded solutions that guard against accidental as well as intentional incidents.
The push to compete on analytics is the single most intensive force today that is shaping business technology strategies, and, as a result, the core of big data activity and spending has been focused on BI and analytics.
The BI and analytics space has been undergoing the most dramatic shift seen in the more than 2 decades BI tools have been in existence. While the previous generation of tools emphasized cubes and other front-end environments for analyzing and manipulating data from selected back-end sources, the emphasis is rapidly shifting to enterprise analytics. Leading vendors in this space now employ resources such as the cloud to support the delivery of insights from big data stores right to executives’ dashboards or consuming applications.
There are several forces continuing to boost the fortunes of BI and analytics vendors. Economics plays a leading role, since BI and analytics are key to helping companies recognize new market opportunities in today’s hyper-competitive global market. In addition, enterprises are facing a growing surge of data streaming in from a wealth of sources—from sensors to user-generated information. Big data-savvy organizations recognize that the wealth of data they are accumulating can be applied to address the most frequent issues that may arise in customer service or operations. Many organizations’ decisions, in fact, can be automated, and analytics plays a key role in enabling this process.
Cloud computing has become a mainstream business technology strategy, and accordingly, the database market is also becoming cloud-friendly. There are several ways in which leading vendors are approaching the cloud opportunity. For some database vendors, it means offering full-fledged data management and storage capabilities via a cloud or software as a service environment. Enterprises and end users would have no administrative concerns, they simply sign up, log in, and store their data.
The other side of the cloud opportunity for many leading vendors involves optimizing their databases or database products to take advantage of cloud resources. This could either take the form of positioning databases within a private cloud, as the core component of platform as a service. Or, databases may run in more traditional server settings but also draw upon application programming interfaces (APIs) or web services to enhance or expand functionality.
While there is substantial activity in the cloud database arena, these offerings are still in the early stages. Cloud-optimized databases can be deployed both on-premises, or within the cloud by a dedicated hosting service, public cloud, or private cloud. Private or hosted cloud solutions offer the option for enterprises still steeped in on-site legacy assets. There are solutions that help relational database environments integrate into emerging cloud environments, and are built specifically to distribute applications data across different clouds, regions, and data centers to assure uptime.
New Databases and Frameworks
Big data isn’t just about volume—it is part of the rise of unstructured and semi-structured data types that require capabilities that many relational database management systems weren’t built to handle. A new class of vendors has emerged to fill this gap—offering ways to store, manage, and deliver data beyond the boundaries of relational databases. More often than not, these new database forms are open source, meaning their users may download solutions and patches at a moment’s notice, unencumbered by licensing restrictions, and without the need to gain budget approval from their CFOs’ offices.
Some new solutions, in fact, may even still be relational systems but with a modern twist. NewSQL solutions, for one, are a class of modern relational database management systems that run in the cloud.
NoSQL (“Not only” SQL)-based solutions, which were specifically built to support unstructured or nonrelational data types, fall into four major categories. Key-value stores enable the storage of schema-less data, aligned as a key and actual data. Column family databases do not store data by rows, as is the case with relational databases, and instead store data within columns. Graph databases employ structures with nodes, edges, and properties to represent and store data, designed to end users to ask deeper and more complex questions across new or existing data stores. Document databases facilitate simple storage and retrieval of document aggregates.
For organizations seeing or seeking ways to process and manage big data stores of unstructured data—such as log files—Apache Hadoop offers a parallel-processing framework that works in tandem with the MapReduce analytics engine to capture and package big data for consumption as it comes into the enterprise. Hadoop is already being eagerly embraced by data managers and technologists as a way to manage and analyze mountains of data streaming in from websites and devices. While Hadoop and many of the tools in its adjoining ecosystem are open source, there is a growing support community of vendors that offer an array of value-added capabilities and services—everything from real-time processing to integration with more traditional data environments. As with other elements of the data management world in this era, leading vendors are promoting a holistic, enterprise approach, bringing Hadoop—which has been confined to IT and data shops—into the business mainstream.
Since its debut several decades ago, MultiValue technology has been recognized for highly flexible, deployable and manageable data solutions. MultiValue databases are known for their simplicity and relatively small footprints.
Given its flexibility, MultiValue historically has been well-positioned to be part of the distributed systems and cloud revolutions. These databases have long formed a bridge between the relational and non-relational worlds, serving as specialized and cache data stores within a range of venues where the costs of installing and maintaining relational databases were too prohibitive.
Leading vendors in the MultiValue arena are increasingly positioning MultiValue databases as prime candidates to underpin virtualized and cloud data environments. The rise of virtualization, in fact, opens up enterprise environments to a range of underlying data solutions that are uniquely targeted to the specialized applications that MultiValue supports. Vendors in this space understand the key role MultiValue can play in opening up enterprise data stores to distributed services, and have been focusing in delivering data products that address cloud, mobile technology, data analytics, and ready integration with other systems and databases.