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Open-Source Databases: The Next Step in Modernization

Linton Ward
Linton Ward, Distinguished Engineer, OpenPower solutions-Photo by Jason Griego

Since then, data hasn’t just grown exponentially, it has transformed radically. Sensors and smart devices are tapping into the internet, sharing near-instantaneous information. Then there’s data generated through social media. Twitter and Facebook are vehicles for connecting to clients—and for those same clients to critique your business. And don’t forget about video. We aren’t that far removed from a time when high definition was nonexistent and bandwidth was costly. Now you can upload full-length movies easily and affordably.

“There's a full-on transition to this modern data platform that extends the traditional relational database and provides greater capabilities to reach consumers and end points more easily within the whole internet infrastructure. It's been incubating over the past five to 10 years, but now it's all over the place.”
—Linton Ward, Distinguished Engineer, OpenPower solutions

You may have heard as much as 80 percent of all data is unstructured—the 80-20 rule is something else that originated in the late 1990s. But structured or unstructured, data is data in this sense: With the right tools, it can be analyzed, and it can yield potentially valuable information.

“Structured data is the history of what we’ve done: How many did I sell, what sold with what, what time did I sell it? Those kind of questions,” says Ward. “So being able to do text analytics on non-relational data, paragraph-format data, is a very powerful way to provide context to the kinds of analytics that you can do on relational structured data.”

OSDB Types

Of course, some popular OSDBs—such as MariaDB and EnterpriseDB—are relational in design, and Ward notes that these solutions have found their place in the enterprise, serving as utility databases for applications, for example. But again,non-relational OSDBs—typically referred to as NoSQL databases—specifically allow new data types to be housed and mined.

These databases can be broadly classified into four types:

  • Document databases: These general-purpose systems store data in documents, which can contain one or multiple fields and can be queried based on any combination of fields. Many allow data to be structured in an object-oriented fashion.
  • Graph databases: These systems are designed to serve new types of applications that focus on storing simple and complex relationships in data, allowing for rapid execution of complex queries. Graph databases enable analysis of connected data, including social networks, spatial data, routing information for goods and money and recommendation engines.
  • Key-value databases: The most basic non-relational database type, these systems store key and value data in memory, including session information, user profiles, preferences and shopping cart data.
  • Wide column stores: These systems are similar to key-value databases, but provide significantly better performance and greater scalability. It’s possible to have thousands of columns in one table, and tables of hundreds of columns are common.

Many OSDBs—both relational and non-relational—can function in IBM Linux on POWER environments. (See "Enterprise-Ready Open-Source Databases"r more information.)

Not Free, but Cost-Effective

Getting started with an OSDB isn’t complicated—after all, you can simply go online and download one. Sometimes, that’s actually the most prudent course of action. Think about it: What better way to make a case for an open-source solution than to install and tinker with the product in a nonproduction environment?

Neil Tardy is a contributing writer to IBM Systems Magazine. Neil can be reached at ntardy@msptechmedia.com.


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