Nothing can be more terrifying than losing important data because your system has suddenly crashed. This is where the process of data replication comes to your rescue. It allows you to continue working by switching to a replica of your data.
Exactly how does data replication do this? Read on to find out more.
This article will explain the concept of data replication, how the data replication process works, the advantages and disadvantages of data replication, and opting for enterprise-level data replication software. We’ll also list down a step-by-step guide to help you simplify copying data from one system to another.
What is Data Replication?
Data replication is the process of copying and storing enterprise data in multiple locations. The replication process can be one-time or ongoing, depending on the organization’s requirements—the latter aims to ensure that the replicated data is regularly updated and consistent with the source.
The main purpose of data replication is to improve data availability and accessibility and system robustness and consistency.
We’ll discuss these benefits in detail in the subsequent headings. But, first, let’s take a look at how the data replication process can be accomplished.
How Does Data Replication Work?
Data replication works by copying data from one location to another, for example, between two on-premise hosts in the same or different locations. For example, data replication in storage is copied from one storage network system to another.
You can replicate data on-demand – in bulk or batches as per a schedule. Besides, replication can also be done in real-time as the data is entered, altered, or erased in the main sourcing system.
Data can be duplicated via various replication procedures; the three common data replication procedures include:
It involves copying entire data from the source to the target system, including new, modified, and present information. However, this data replication technique requires more processing power and increases the load on the network. Plus, the cost usually upsurges as maintaining consistency becomes difficult when copying large data volumes.
In this data replication technique, only some part of the data is replicated, such as the updated data. Thus, it is faster than full table replication because it deals with a comparatively smaller volume, which reduces network load and consistency issues.
This data replication technique is only viable for databases as it is done using binary log files present in the database. It reads data directly from the log files, reducing the load on the production system. This technique falls closest to real-time data replication.
Disadvantages of Data Replication
Maintaining consistent data across disparate locations is often taxing in terms of resources. Therefore, some of the common challenges of data replication:
Maintaining duplicates of the same data in various locations results in greater storage and processor overheads.
Executing and handling the replication process needs committed time from an in-house team to ensure that the copied data is consistent with the original source data.
Preserving consistency across data replicas can increase network traffic.
Latency or Service Interruptions
Latency or service interruptions during data transfer can yield difficulties in data replication. The process
Synchronizing updates between distributed environments is complicated because copying data from various sources at different time intervals can result in some datasets going out of sync with the rest.
This could be temporary, lasting for a few hours, or your data could become entirely out of sync.
To tackle this challenge, database admins should ensure that data is updated consistently. The data replication process should be carefully planned, implemented, appraised, and polished as needed to improve the process.
Benefits of Data Replication
The advantages of data replication are data accessibility to several hosts or data centers and simplification of data sharing between systems on a large scale by dividing the network load between heterogeneous systems.
Your business can expect to experience the following advantages from implementing data replication services:
Data Reliability and Availability
Data replication ensures easy access to data. This is particularly useful for multi-national organizations, spread over different locations. Therefore, in case of a hardware failure or any other issue in one location, data is still available to other sites.
The main benefit of data replication appears in terms of disaster recovery and data protection. It ensures that a consistent backup is maintained in the event of a disaster, hardware catastrophe, or a system breach, which can compromise data.
So, if a system stops working because of any of the above-mentioned reasons, you can access the data from a different location.
Data replication can also enhance and boost server performance. When companies run numerous data copies on different servers, users can access data much quicker. Moreover, when all data read operations are directed to a replica, admins can reduce processing cycles on the primary server for more resource-exhaustive write operations.
Better Network Performance
Keeping copies of the same data in various locations can reduce data access latency as you can retrieve the required data from the location where the transaction is being executed.
For example, users in Asian or European countries may face latency issues when accessing Australian data centers. However, placing a replica of this data somewhere close to the user can enhance access times while balancing the load on the network.
Data Analytics Support
Usually, data-driven businesses duplicate data from numerous sources into their data stores, such as data warehouses or data lakes, to fuel their business intelligence. This makes it easier for the analytics team dispersed across various locations to undertake shared projects.
Enhanced Test System Performance
Replication simplifies the distribution and synchronization of data for test systems that mandate quick accessibility for faster decision-making.
Replicating Data: The Step-by-Step Process
You can reap the advantages of data replication if there is a consistent data copy across the organization. Here’s a breakdown of the steps that helps accomplish the real-time data replication process:
- The first step is to narrow down the data source and target system.
- Next, choose tables and columns that are to be copied from the source.
- Then, identify how frequently updates need to be made.
- Now select a data replication technique (either full, partial, or log-based).
- Next, write custom code or use enterprise data replication software to perform the process.
- Lastly, closely monitor how the data is being extracted, filtered, transformed, and loaded to ensure quality.
Understanding and Selecting Data Replication Tools
Selecting a real-time data replication tool that fulfills your requirements is key to ensuring smooth process execution.
One way to go about it is to write custom codes to replicate data. However, one challenge in following this route is that integrating other internal applications in the network is a major commitment in time and resources. Plus, over time, you’ll see that this method is not scalable and can present unique challenges in error recording, job monitoring, and refactoring code when any element in the process alters.
Another way is to use code-free, enterprise-grade data replication software to minimize manual labor in generating and handling data replication transactions across your organization. Plus, a majority of the data replication software can scale with respect to the volume and velocity of data.
Astera Centerprise is one such enterprise-level tool that enables data replication by integrating, cleansing, and transforming data in a visual, code-free environment. It automates the entire replication process using features like job scheduling, workflow automation, smart mapping, and more. Hence, saving users valuable time in process execution and enabling them to collect insights from data rather than spending time on data management.