Today, business owners rely on data-driven business intelligence solutions to make strategic decisions and stay ahead of the competition. Data security is of primary concern for businesses of all sizes. The process of replicating and storing data in several locations to improve availability and accessibility across a network is known as data replication. As a result, a distributed environment is created that allows local users to access data more quickly and without interfering with other users.
Data replication is an important part of disaster recovery (DR) plans because it ensures that a real-time copy of data is always available in the event of a system failure, cybersecurity breach, or other calamities, natural or caused by human mistakes. Copies of the replicated data can be kept in the same system, on-site or off-site servers, or across various clouds.
Benefits of Data Replication
While data replication is frequently used in disaster recovery (DR) plans, it is far from its only application. Data replication, when done correctly, provides enormous benefits to enterprises, end- users, and IT professionals alike.
Improving data availability: By storing data at several locations across the network, data replication improves system resilience and reliability. This ensures that data can still be accessed from a separate site in the event of a technical failure caused by a virus, software malfunction, hardware failure, or other interruptions. It’s a lifesaver for businesses with data stored in several locations since it ensures data access 24 hours a day, seven days a week, across all geographies.
Faster access to data: When accessing data from one place to another, there may be some lag. Users benefit from faster data access and query execution by storing replicas on local servers. For instance, employees from numerous branches of a firm can easily access data from their home or branch office.
Server performance: Data replication distributes the database across multiple sites in a distributed system, reducing the data load on the central server and improving network performance. For write operations, IT experts reduce the number of processing cycles on the primary server.
Disaster recovery: Due to data breaches or hardware failure, businesses are frequently vulnerable to data loss, deletion, or corruption. Data replication keeps backups of the primary data on a secondary appliance (hot copies) that may be recovered promptly.
How Does Data Replication Work?
Writing or copying data to different locations is referred to as replication. Copies are made on-demand, sent in bulk in batches according to a schedule, or replicated in real-time when data in the master source is written, updated, or deleted. There are various components, types, strategies, and schemes that go into successful data replication. Here are some of the components of the data replication process.
Publisher: It’s where the data comes from and where objects for replication articles are built. To reproduce data as a unit, these articles are grouped and published in single or numerous publications.
Distributor: This is where the publisher’s replicated databases are stored before being provided to the subscriber.
Subscribe: The recipient of duplicated data, which can simultaneously receive data from many publishers.
There are two types of data replication solutions in the business intelligence services market: synchronous replication and asynchronous replication.
Synchronous replication processes write data to both the primary and replica (target) storage systems at the same time. This ensures that the primary copy and duplicate are almost identical in real-time. However, due to its high cost, it will eat into your IT budget and may cause latency, slowing down your primary application (source). Synchronous replication is commonly used for high-end transactional applications that require immediate failover in the event that the primary fails.
When executing asynchronous replication, data is initially written to the primary source, then replicated to the target medium at specified intervals, depending on the parameters and implementation. Because replication can be scheduled at times of low network utilisation, there is more bandwidth available for production. Most network-based replication products support this strategy.
Data Replication Strategies
From the beginning, most database-based solutions maintain track of every modification in the database. It also creates a log file, sometimes known as a changelog, for the same. Each log file is a collection of log messages, each of which contains information such as the time, user, change, cascade effects, and change method. The database then allocates each of them a unique position Id and saves them in a chronological order depending on the Id.
Statement-based replication records and stores all commands, queries, and activities that change the database and cause updates. The replicas are generated by re-running these statements in the sequence in which they occur in procedures that use the statement-based mechanism.
Row-based replication takes track of all new rows in the database and saves them in the log file as a record. Procedures using a row-based replication mechanism do replication by recapitulating over each log message in the sequence in which they were received. The location Id serves as a bookmark, in this case, allowing the database to easily continue the replication operation.
Data replication best practices
It’s critical to follow some good administrative practises after the replication network is set up:
- A strategy for regularly backing up a database should be in place. Regular backup restoration testing should also be performed.
- Since scripts can be easily stored and backed up, it is critical to script all replication components and repetitive operations as part of the disaster recovery strategy. The components can easily be re-scripted if the policies change.
- It is vital to identify the elements that influence replication performance. Hardware, database design, network settings, server configuration, and agent parameters are all part of this. All of these must be implemented and monitored for the application’s workload.
Parameters to be monitored during data replication:
- Replication time needed
- Replication that lasts for a long time
- The amount of replication actions that can happen at the same time is known as concurrency.
- Synchronization timeframe
- Consumption of resources