The Eight Disruptive Trends Shaping Today's Database Marketplace

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Relational databases have dominated the enterprise data scene, and continue to do so. The Unisphere Research Quick Poll finds the database landscape is dominated by Microsoft SQL Server, Oracle, IBM DB2, and MySQL—all relational. However, a majority also now support separate databases that handle unstructured or non-relational data types as well.

NoSQL databases continue to make their mark felt across enterprises, now present in a majority of organizations. The Unisphere Research Quick Poll finds 52% report having a NoSQL databases on premises. The most popular type of database is document database, followed by cloud-based NoSQL databases.

Types of NoSQL Databases Deployed

Document database  -28%

Cloud-based database - 22%

Key value store - 20%

Graph database  -14%

Column family database  -14%

XML/hierarchical database  -14%

Other - 5%

We do not have NoSQL - 37%

7. Open Source Matures Into a Responsible Young Adult

Open source once was seen as a source of free or cheap commodity software to collaborative solutions that are as adaptable as the business. Now, it has become a key part of vendors’ offerings, whether the vendor is an open source provider, or is building value-added components or services around core open source technology.

There are a range of open source frameworks, databases, tools, and applications now available. Most prominent in the data space, of course, is Apache Hadoop. In the Unisphere Research Quick Poll, close to one-third of enterprises, or 30%, report they have deployed the Hadoop framework in some capacity. The survey also finds another 26% are planning to adopt Hadoop within the next year. Significantly, 91% of respondents at Hadoop sites expect to be increasing their adoption and use of Hadoop over the next 3 years. For 36%, expansion plans are “significant.”

Key functions or applications supported include analytics and business intelligence, along with IT operational data. Many Hadoop implementations are also intended to support special projects as well.

Primary Hadoop Functions

Analytics/business intelligence  - 55%

IT operational data (logs, systems monitoring) - 45%

Special projects - 41%

Messaging/communication - 35%

Testing and pilot projects  -27%

Developer resources (configuration information) - 18%

Peripheral/branch office support  -14%

Core business functions (finance, production)  - 9%

Other  -9%

Open source is a catalyst for a great deal of innovation across the enterprise. It is reshaping the data market landscape, with solutions—built and designed by both vendors and communities—that are open and available to be plugged in to any situation. Today’s enterprise data applications can essentially be assembled from pre-built components available.

8. In-Memory Is Fast and Furious

With increasing requirements for real-time data delivery, there has been a rise in in-memory database offerings. Close to one-third of respondents to the quick poll, 32%, indicate they already run in-memory databases. More than three-fourths of respondents running in-memory sites, 76%, intend to step up their adoption, with 38% anticipating “significant” growth in their use of in-memory.

Key areas used in in-memory include analytics and business intelligence, core business applications such as finance and production, as well as testing and pilot projects. 

In-Memory Functions Supported

Analytics/business intelligence  - 58%

Core business functions (finance, production) -42%

Testing and pilot projects  -33%

IT operational data (logs, systems monitoring) - 25%

Messaging/communication  -21%

Special projects  -17%

Peripheral/branch office support  - 8%

Developer resources (configuration information)  - 4%

Other  - 0%

There are a range of in-memory technologies, now supported by key vendors, in which data and processing is moved into a machine’s random access memory. In-memory eliminates the process of pulling data off of disks, which adds a great deal of latency to information retrieval. In an environment with large datasets—scaling into the hundreds of terabytes—this will multiply into a bottleneck for rapid analysis, and limit the amount of data that can analyze at one time.

More than anything, IT managers appreciate the faster response times/reduced latency that in-memory technology provides, as cited by close to nine-tenths of IT managers using the technology. Flexibility and faster time to market are other advantages mentioned. 

Advantages of In-Memory Databases

Faster response times/reduced latency  - 88%

Greater flexibility  - 25%

More rapid deployments  -21%

Cost savings  -13%

More effective management/analysis of unstructured data  - 13%

Easier to manage - 8%

Easier for end users to access  - 8%

Other - 0%

This is perhaps the most exciting era the database industry has ever seen. As they face a hypercompetitive global economy, enterprises are reinventing and disrupting themselves at an unprecedented pace—and are embracing data to do so. 

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In their many years of existence, in-memory databases and technologies have meant one thing to most observers: running applications and serving up data at lightning-fast speeds. Now, as data increasingly evolves into a strategic enterprise asset, in-memory databases have implications beyond blazing bits and bytes. They are opening new opportunities for business innovation and growth.

Posted September 11, 2014