haven’t been run within the same databases or servers due to fears that large queries or reports would impinge upon production systems. Not only does in-memory potentially remove the requirement of having a separate reporting database, but it can help to eliminate the latency and scripting involved in moving data between operational and reporting database environments, thus enabling business decision makers to take advantage of a more highly integrated environment to speed up decision making.
Make real-time enterprise a reality: In-memory means a significant enhancement in real-time—or near-real-time—analytics and business intelligence. For an incoming generation of business managers accustomed to the instantaneous speed of queries available over the web, the slow pace of queries within today’s enterprises is no longer acceptable. Typically, to get answers to questions, data must be moved from the correct sources, clustered, and categorized. With data loaded into in-memory environments, the impact on business decision making can be transformational. For example, instead of waiting for hours, or even days, to roll up data from various databases across the enterprise, insights may be available almost instantaneously from in-memory databases. In the words of one industry leader, in-memory enables a decision maker to “get an answer to a question in the meeting where it is asked.”
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Widen access to data analytics across the enterprise: Since databases loaded into in-memory environments require less setup, configuration and scripting by data professionals, there are more opportunities for nontechnical business users to go in and work with the data. Data analysis will not be restricted to a few analysts, which has been the case because of concerns about too many queries dragging down the system. The ability for various classes of business users to create their own analytics initiatives opens up new paths to innovation.
Support greater numbers of users: The rise of the connected enterprise means ever-expanding and unpredictable spikes in demand on systems. Application queries against databases are part of this demand, and web application performance can be severely hampered as user demand increases. The deployment of data on in-memory environments helps address these increased and unpredictable user loads.
In-memory will help DBAs and other data managers shift their focus away from tweaking, tuning, and building databases and toward feeding real-time, actionable information to business decision makers when they need it.
In-memory analytics will contribute to a reshaping of the way businesses are able to manage and compete on analytics. In combination with other high-speed media—such as flash storage, solid-state disks, and virtual tape—data analytics may be available to decision makers at lightning speeds. In-memory databases now make data-driven decision making a reality at all levels of the organization. In turn, this means more potential to transform the way enterprises process and act on information. This means new business models, new processes, and this may change the way enterprise applications for analytics and real-time decision making are used.