Data replication is a process by which data is made available outside of the systems that are used to produce or maintain it.
That’s the high-level definition of data replication. To understand what it means in practice, let’s get more detailed. We’ll look at a few alternative definitions, outline the key components of the process, and walk through use cases and examples of how data replication might be used in the real world.
Ready? Let’s dive in.
Accepted Definitions of Data Replication
To begin, let’s examine definitions of the process from a couple of commonly cited sources.
IBM offers a basic definition: “Replication is the process of copying data from a central database to one or more databases.” The takeaway: At a fundamental level, data replication is exactly what the term suggests – the copying of data.
But, practically, it involves more than just copying.
TechTarget explains the concept in slightly more detail:
“Database replication is the frequent electronic copying of data from a database in one computer or server to a database in another — so that all users share the same level of information. The result is a distributed database in which users can quickly access data relevant to their tasks without interfering with the work of others.”
This encapsulates two of the key factors that enterprise organizations associate with data replication: frequency and distributed access.
Key Components of Data Replication
Frequency and distributed access tend to be assumed implications in a data replication process. Let’s look at each.
Data replication implies frequency.
Most often, in data replication, data isn’t being copied one time and one time only (although there are use cases where data replication is comparatively infrequent). Instead, data replication usually involves data being updated frequently – every day or hour or transaction – to maintain integrity and value.
Data replication implies distribution.
Technically, data could be copied to the same server. But the term data replication implies that data is distributed from a source server (or servers) to a destination server (or servers).
Data Replication Use Cases
With these things considered, we can flesh out our definition by offering the most common use cases for data replication.
As Brian Storti correctly outlines, some of these are:
Data replication to minimize travel time.
Let’s say that a global enterprise has offices in Asia and in the US. They have a database in the US that’s being replicated to a database in Asia. When the customer service team in Asia goes to access data, they are able to query the database in their region.
If they were forced to query the database in the US, they’d experience a lag as the request traveled back and forth, potentially hindering their ability to offer live service.
Data replication to scale the capability to serve requests.
For example, an enterprise relies on a database to log customer transactions. This database is being replicated to a second database. When a customer service representative is asked to look up the details of a transaction, they do so by querying the second database. This way, the database that is actively logging transactions is unaffected.
If data replication was not happening and the customer service representative had to query the database logging customer transactions to look up the data, that database might be slowed down, potentially compromising its ability to function.
This use case is particularly valuable for serving reporting data.
Data replication for disaster recovery and business continuity.
Perhaps the simplest use case for data replication is to have another instance of data in case a server goes down. Because data replication implies frequency, applications may even be able to access the replicated data without any downtime.
Data replication for testing environments.
Data replication is helpful for test environments where it’s necessary to access up-to-date data, but where additional demands on a live application server are unwanted. For example, if an enterprise is testing out a new ticketing system, it might be helpful for them to do so with real data, without adding additional requests to their live system. Data replication can solve this challenge.
Data replication to populate apps that only work with modern data stores.
If you have a legacy database system that’s incompatible with an initiative to use modern applications, data replication can help. You can populate a modern database environment with data from the legacy system, where it can more easily be accessed by new applications.
Ready to implement a data replication solution?
Our hope is that the information provided here has helped you to better understand the topic of data replication – and to clarify scenarios where it may be helpful.
If you’re considering a data replication solution for your organizational needs, get in touch with us.
At StarQuest, we’re experts at data replication. Our powerful SQDR software can be utilized for replication from an extensive range of data sources, and it powers real-time change data capture using a log-based approach.
And, importantly, our customer service team is regarded as some of the best in the business, with clients calling us “The best vendor support I have ever encountered.”
Get in touch with us to discuss your data replication needs. We can set you up with a no-charge trial of our software using the DBMS of your choice, and help you take the first step toward a solution that will benefit your business.