In a data-driven economy, having higher availability and better accessibility to data can provide a competitive advantage. Therefore, organizations must understand the importance of data replication strategies to build a robust data distribution environment. With data replication, organizations can streamline the process of managing data for backups and analytics.
An automated data replication process is particularly essential for data-driven organizations as they produce a colossal amount of digital information daily. However, the advantages of data replication are not limited to companies that handle a plethora of data. Different data replication strategies can be embraced based on the requirements to manage data efficiently.
What is Data Replication?
Data replication is the process of copying data and storing it in separate nodes, databases, or locations. This is done to ensure the availability and integrity of data in the event of a system failure or other disruption. Data replication can be performed between two on-premise systems, cloud-based databases, and more. This replication can occur in bulk, in scheduled batches, or real-time across data centers. Depending on your specific needs, you could choose between various data replication strategies, each with its own pros and cons.
What are the Advantages of Data Replication?
Other than disaster recovery, data replication can provide several benefits to organizations and IT end users when implemented efficiently. Some of the advantages of data replication are:
1) Better Application Reliability
Data replication guarantees that multiple copies of data are available 24/7/365. This implies that even if a hardware or machinery failure occurs or there is some form of interruption, the data is still accessible from another location. As a result, it maximizes data availability while decreasing downtime. This, in turn, boosts the reliability of your system.
2) Better Transactional Performance
While working with transactional data, you must ensure that data gets updated simultaneously everywhere. For this, you need to carefully monitor various synchronous processes. Hence, your application must write the commit before the control threads can continue the tasks.
Data Replication can prevent data dependency on the master node, reducing additional disk-based I/O operations and making the entire process more durable.
3) Faster Data Access
By having multiple copies of data stored in different locations, data replication can reduce latency, especially for users accessing data from faraway locations. With data replication in place, users can route data reads across numerous machines that are a part of the network, thereby improving the read performance of your application. Hence, readers working on remote networks can easily fetch and read data.
This application of data replication also helps reduce the cache missings and lower the input/output operations on the replica, as replicas may also need to cache that part of the data.
For example, users in Asia may experience high latency when accessing data from North American data centers. But placing a copy of the data closer to users’ location can improve access times. Also, this minimizes the pressure on single servers and improves overall network speed.
4) Data Durability
With data changes/updation taking place on multiple machines simultaneously instead of a single computer, data replication helps boost and ensure robust data durability. Leveraging numerous CPUs and disks to ensure that the replication, transformation, and loading processes take place correctly improves processing and compute power.
5) Improved Reporting and Analytics
Data replication enables advanced analytics by synchronizing and consolidating data from various sources into data warehouses for business intelligence and machine learning. Real-time insights can be gained for better decision-making by immediately copying data to other locations.
6) Foolproof Security and Data Recovery
Organizations today create, store, and use large amounts of data for their day-to-day operations. Any data breaches or losses can compromise the organization significantly, making data recovery and security a prominent concern.
Data replication creates multiple backup copies of data, which improves data security and enables faster disaster recovery. With backups updated in real or near real-time, organizations can access current and up-to-date data, even during any failures/ data losses. This is important in systems that primarily handle sensitive or critical data. Data replication can also be used for data archiving, which helps keep a historical record and comply with legal, regulatory, or audit requirements.
Types of Data Replication & Their Real-World Use Cases
Today, organizations use several types of data replication methods based on the use cases and advantages of data replication. Some of them are:
Synchronous replication makes real-time copies of data. This replication technique is commonly utilized in mission-critical applications requiring significant data availability and consistency. Also, it is employed to achieve low Recovery Time Objectives (RTOs). Although continually replicating data in real time is relatively expensive, synchronous replication is exceptionally dependable in the case of a disaster.
One real-world instance of synchronous replication is a bank that uses a database management system (DBMS) to establish synchronous replication between a primary database and one or more subsidiary databases. The primary database processes all transactions, including account transfers, deposits, and withdrawals. Concurrently, the secondary databases are instantly modified with the required information when the primary database is updated. By doing this, the consistency of the data across all databases is guaranteed. For financial institutions, this is important since it ensures that all transactions are accurately and promptly recorded and that consumers can access their account information and conduct transactions whenever they want.
When using asynchronous replication, data is first saved in the main source. Then, depending on how it’s configured, the data is copied to another location at specific times. Because users can schedule the replication at any time, the network is less congested during peak hours.
Healthcare organizations use asynchronous replication to ensure the availability of backed-up patient data across different departments. For instance, doctors, technicians, and nurses may need access to patient data from EHRs, lab reports, etc. Using asynchronous replication, patient data can be collected and uploaded to the primary server and replicated as a backup on multiple servers spread across the hospital. This enables data to be viewed by various departments and used to create reports for diagnosis to improve patient care.
Snapshot replication is the process of creating a snapshot of the source database at static points in time and replicating the data in the secondary systems. Unlike most replications, snapshot replication does not keep track of modifications to the data. Consequently, the data gets replicated in a single transaction. Snapshot replication is effective when data doesn’t change regularly or when big changes happen quickly.
Snapshot replication works best when there are infrequent data changes. For example, if an organization occasionally changes its website, it is advised that the complete snapshot of data be replicated once it has changed. Based on the frequency of the changes to the website, the organization can use snapshot replication weekly or monthly.
In merge replication, data from two or more databases are consolidated into a single database. It enables both the publisher (the primary server) and subscriber (the secondary server) to independently make updates to the database. This feature makes merge replication the most complicated type of replication. In this case, the initial synchronization from the publisher is a snapshot replication. However, as soon as the data is altered, the altered data is transmitted to a merging agent (installed on all servers). This agent then updates and distributes the data using conflict resolution strategies.
Merge replication can be used for a multi-store retail chain with a central database at its headquarters. Each store can have a local database for tracking sales, inventory, etc. The retail chain can use it to recurrently synchronize the data between the local and central databases at the corporate headquarters. This enables alterations made at one store to be replicated in the databases of the other stores. It also gives the headquarters a complete view of all locations’ sales, inventory, and customer information.
Also Read: Data Replication Best Practices
Disadvantages of Data Replication
Data replication has several benefits, like improving efficiency and guaranteeing data availability. However, it is possible that users may encounter some data replication issues because of certain drawbacks associated with the process. Some of these disadvantages of data replication are:
1) Data Inconsistency
Contrary to centralized environments, managing simultaneous updates in distributed environments is more challenging. When data is replicated from many sources at various times, specific datasets may go out of sync with others. This could happen momentarily, continue for several hours, or result in the data being unsynchronized.
2) Slow Writing
Although placing secondary data locations closer to consumers makes data access easier, there is a tradeoff. Running data replication at several places simultaneously might strain your network, slow it down, and use up a lot of processing resources. Writing to databases would take longer, even though reading data from dispersed locations is quicker. This can be a problem for organizations with limited bandwidth or high network usage.
3) Version Control
It is not easy to keep track of several replicated data versions, especially when different copies of the same data are being replicated at once. Trying to determine which version of the data is the most recent or reliable may cause inaccuracies. Though version numbering can address this problem to a certain extent, it adds to the workload.
4) Increased Storage Cost
As the name suggests, data replication makes multiple copies of the original data, which might consume a lot of storage space. This is especially true for large-scale systems that produce massive amounts of data. Organizations could incur higher storage expenses due to the extra space needed to keep multiple copies of data. This can be a major concern for businesses with limited storage space or working on a tight budget.
5) Database Coupling
In the context of data replication, data coupling refers to the strict integration of the replication process with the underlying DBMS. This implies that the replication process is closely intertwined with the particular database in use. As a result, it is impossible to modify or replace the database without changing the replication method. This can affect the flexibility and scalability of the entire business process.
Organizations can benefit by offering accessible data to every end-user by implementing data replication in a distributed environment. Though there are multiple types of data replication, each has its own advantages and disadvantages. Before incorporating a data replication strategy, ensure you are familiar with your business needs and the benefits of every data replication type.
These data replication strategies, however, are quite effort-intensive and require in-depth technical expertise. Implementing them can be challenging, especially for a beginner, and this is where Hevo saves the day!
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