Under the Hood of a Hybrid Database (How It Works)

There’s a lot of chatter about hybrid databases these days.

That’s because, in an era where unstructured data is becoming increasingly common, organizations need versatile databases that enable them to manage, store, process and analyze all of their data—not just their structured data.

As companies move to hybrid cloud environments—according to Gartner, 70% of new database deployments leverage the cloud for at least one use case—they are forced to deal with the resulting data silos that are created. To simplify data management, many organizations are looking for solutions that enable them to easily manage their data across all on-premises and cloud deployments.

To accomplish this, leading organizations are moving to hybrid databases, but first let’s look at how “hybrid database” used to be defined.

Hybrid Database – The Old Definition

When people used to think of “hybrid databases”, they thought mainly of storage. The typical hybrid database, by the old definition, was something that combined in-memory and on-disk data storage, leveraging the benefits of each technology:

• In-memory data storage uses memory instead of disk space to store data, which accelerates response times and eliminates seek time when querying data. While in-memory data storage is generally a more expensive way to store data, it enables organizations to make decisions in less time because transactions are processed much faster.
• On-disk data storage is a considerably less expensive method of storing data. It’s also a slower way to process data. While in-memory data storage can retrieve data in nanoseconds per byte, on-disk data storage can retrieve data in milliseconds per byte (or microseconds for NAND flash SSDs). These fractions of seconds add up, significantly, when you’re dealing with large volumes of data. If you don’t need to process certain sets of data on a regular basis, you’re probably best off storing that data on-disk.

At a very basic level, hybrid databases enabled organizations to process enormous amounts of data quickly. Data that’s used often is stored in RAM while data that’s not used as frequently is stored, much more affordably, on disk. In many cases, the database will automatically classify which kinds of data need to be stored which way. If you need high performance, memory tables should be used. If it’s a question about storage, use disk tables. Apache Cassandra™, for example, uses a combination of memory and storage, a function geared toward leveraging memory to make writes fast and on-disk representation as efficient as possible.

But the hybrid database of today really has to do a lot more than with just storage.

As enterprises move to the cloud, more and of them will be deploying hybrid cloud databases, which are still “hybrid”, in the data storage sense, but they are, much more importantly, hybrid in the architecture sense, because they combine the use of public and private clouds.

The New Hybrid Database

In recent years, data storage costs have fallen back down to earth and storage performance has increased dramatically.

Simply put, many organizations are no longer concerned with data storage—or at least not anywhere near as concerned as they used to be five or 10 years ago.

Instead, they’re focused on deploying applications in hybrid cloud environments—and maximizing their returns on it.

Unfortunately, it’s not enough to simply move to a hybrid cloud environment and think you’ll reap all the rewards. Today’s leading organizations need a modern hybrid database— one that’s always on, always available and always consistent, providing a seamless experience every time—to ensure that the complex applications they develop and rely on work as designed.

To get the most out of complex hybrid architectures, organizations need data management strategies that are compatible. With teams spread out across the world and users hopping between web, mobile and desktop apps, organizations need to ensure that their database layer serves up consistent experiences. Otherwise, productivity can grind to a halt as customers and employees alike grow increasingly frustrated.

Good news: Powerful new hybrid databases are fully distributed, able to write and read data anywhere. Redundancy is baked into the cake with real-time multiple data center replication, which helps maintain geolocal latencies while guaranteeing uptime and scalability—right out of the box. 

For example, DataStax Enterprise (DSE)—an Active Everywhere hybrid database built on Apache Cassandra’s masterless architecture—provides organizations with the availability, reliability and scalability they need to thrive. DSE runs across multiple clouds and on-premises in one second cluster. 

Since DSE, which is distributed across data centers and cloud environments, doesn’t have any master nodes, each individual node is capable of performing read and write requests—accelerating performance that much more. In the event a node in a cluster or even an entire data center gets knocked offline, DSE automatically reroutes requests to available nodes and available data centers—giving you the peace of mind that comes with knowing your data is always accessible.

In today’s competitive business landscape, organizations can’t afford to move slowly. To this end, they need a flexible enterprise data layer that delivers the power and agility needed to stay nimble and well-positioned to respond to changing and dynamic market conditions.