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Modern applications require data, and that data usually needs to be globally available, easily accessible and served with reliable performance expectations. Today, much of the heavy lifting happens behind the scenes. Lets look at why the cloud factors into the importance of data replication for business applications.

What is data replication? Simply put, it is a set of processes to keep additional copies of data available for emergencies, backups or to meet performance requirements. Copies may be done in duplicate, triplicate or more depending on the potential risk of a failure or the geographic spread of an applications user base.

These multiple pieces of data may be chopped up into smaller pieces and spread around a server, network, data center or continent. This ensures data is always available and performance is unfailing in a scalable way.

There are many reasons for building applications that understand replication, with or without cloud support. These are basic topics that any developer has had to deal with, but they are even more important when applications go global and/or mobile. Then they need ways to keep data secure and located efficiently.

These particular areas are commonly discussed when talking about cloud data replication:

This refers to making sure all data is ready for use when requested, with the latest versions and updates. Availability is affected when concurrent sessions do not share or replicate their data effectively. By replicating the latest changes to other nodes or servers, it should be instantly available to users who are accessing those other nodes.

Keeping a master copy is important, but it is equally important to keep that copy up to date as much as possible for all users. This means also keeping child nodes up to date with the master node so everyone stays up to date.

Data replication helps reduce latency of applications by keeping copies of data close to the end user of the application. Modern cloud applications are built on top of different networks often located in geographic regions where their user base is most active. While the overhead of keeping copies synchronized and copied might seem extreme, the positive impact on the end-user experience cannot be overstated they expect their data to be close by and ready for use. If local servers have to go around the globe to fetch their data, the outcome is high latency and poor user experience.

Replication is especially important for backup and disaster management purposes, such as when a node goes down. Replicas that were synchronized can then help recover data on new nodes that may be added due to a recent failure. When a data infrastructure requires too much manual copying of data during a failure, there are bound to be issues.

Failover of broken resources can be automated more fully when there are multiple replicas available, especially in different geographic regions that may not be affected by a regional disaster. Applications that can leverage data replication can also take care to preserve user data; otherwise, they risk losing information when a device breaks or a data center is destroyed.

Some see data replication as something nice to have, but as you can see, its not only about backup and disaster management; its also about application performance. There are other benefits as well that you can find as part of enterprise disaster management and performance plans.

The backend systems of a data replication system help keep copies of data spread around and redundant. This requires multiple nodes in the form of clusters that can communicate internally to keep data aligned. Adding a new cluster, a new node or new piece of data would then be automatically synchronized with other nodes to replicate it.

But the application level also needs to understand how the replication works. While a form-based app might just want a set of database tables, it must also understand that the source database has replicas available. Applications must know how to synchronize data it has just collected, as in a mobile app, so other users will have access.

The smaller pieces of data that are synchronized are often known as partitions. Different partitions go on different hardware storage pools, racks, networks, data centers, continents, etc., so they are not all exposed to a single point of failure.

The potential for complexity is often the limiting factor for companies seeking to implement data replication. Having frontend and backend systems that handle it transparently is essential.

As you can see, data replication does not explicitly depend on using cloud resources. Enterprises have been using their internal networks for decades with some of the same benefits. But with the addition of cloud-based resources, the opportunity to have extremely high availability and performance is easier than ever.

Traditional data replication has now been extended beyond just replicating from a PC to a network or between two servers. Instead, applications can replicate to a global network of endpoints that serve multiple purposes.

Traditionally, replication was used to preserve data in case of a failure. For example, replicas could be copied to a node if there was a failure, but replicas could not be used directly by an application.

Cloud data replication extends the traditional approach by sending data to multiple cloud-based data services that stay in sync with one another.

Todays cloud services allow us to add yet another rung on this replication ladder, allowing replication between multiple clouds. This adds another layer of redundancy and reduces the risk of vendor lock-in. Hybrid cloud options also bring local enterprise data services into the mix with the cloud-based providers serving as redundant copies of a master system.

As you can imagine, there are multiple ways to diagram all these interconnections and layers of redundancy. This diagram shows a few of the common models.

(Source: Couchbase)

Though the potential for an unbreakable data solution is more possible than ever, it can also become complicated quickly. Hybrid cloud-based architectures have to accommodate many edge cases and variables that make it challenging for developers to build on their own.

Ideally, your data management backend can already handle this for you. Systems must expose options in an easy-to-understand way so that architects and developers can have confidence and reduce risk.

For example, we built Couchbase from the ground up as a multinode, multicloud replication environment so you wouldnt have to. Built-in options include easily adding/removing nodes, failing over broken nodes easily, connecting to cloud services, etc. This allows developers to select options and architectures they need for balancing availability and performance for their applications.

Couchbases cross datacenter replication (XDCR) technology enables organizations to deploy geo-distributed applications with high availability in any environment (on premises, public and private cloud, or hybrid cloud). XDCR offers data redundancy across sites to guard against catastrophic data-center failures. It also enables deployments for globally distributed applications.

Read our whitepaper, High Availability and Disaster Recovery for Globally Distributed Data, for more information on the various topologies and approaches that we recommend.

Ready to try the benefits of cloud data replication with your own applications? Get started with Couchbase Capella:

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Why Cloud Data Replication Matters - The New Stack