Replicating data, or an entire database is not something that should be taken lightly. It is important to understand the process and best practices for replication to be as smooth as possible! Make sure you are familiar with the steps required for data replication before taking on this project.
In this article we will cover:
How Database Replication Works
Database replication can either be a single occurrence or an ongoing process. It involves all data sources in an organization’s distributed infrastructure. The organization’s distributed management system is used to replicate and properly distribute the data amongst all the sources.
The classic case of database replication involves one or more applications that connect a primary storage location with a secondary location that is often off-site. Today, those primary and secondary storage locations are most often individual source databases — such as Oracle, MySQL, Microsoft SQL, and MongoDB — as well as data warehouses that amalgamate data from these sources, offering storage and analytics services on larger quantities of data. Data warehouses are often hosted in the cloud.
Database Replication Techniques
There are several ways to replicate a database. Different techniques offer different advantages, as they vary in thoroughness, simplicity, and speed. The ideal choice of technique depends on how companies store data and what purpose the replicated information will serve.
Regarding the timing of data transfer, there are two types of data replication:
- Asynchronous replication is when the data is sent to the model server — the server that the replicas take data from — from the client. Then, the model server pings the client with a confirmation saying the data has been received. From there, it goes about copying data to the replicas at an unspecified or monitored pace.
- Synchronous replication is when data is copied from the client-server to the model server and then replicated to all the replica servers before the client is notified that data has been replicated. This takes longer to verify than the asynchronous method, but it presents the advantage of knowing that all data was copied before proceeding.
Asynchronous database replication offers flexibility and ease of use, as replications happen in the background. However, there is a greater risk that data will be lost without the client’s knowledge because confirmation comes before the main replication process. Synchronous replication is more rigid and time-consuming, but more likely to ensure that data will be successfully replicated. The client will be alerted if it hasn’t since confirmation comes after the entire process has finished.
Data Replication Schemes
Organizations can perform data replication by following a specific method to move the data. These schemes are different from the methods mentioned above. Rather than serving as an operational strategy for continuous data movement, a scheme dictates how data can be replicated to best meet the needs of a business: moved in full or moved in parts.
Full Database Replication
Full database replication involves replicating a database to use it across multiple hosts. This provides the highest level of data redundancy and availability. For international organizations, this helps users in Asia get the same data as their North American counterparts at a similar speed. If the Asia-based server has a problem, users can draw data from their European or North American servers as a backup.
Drawbacks of the scheme include slower update operations and difficulty in keeping each location consistent, particularly if the data is constantly changing.
Federated replication is where the data in the database is divided into sections. Each section is stored in a different location based on its importance. This type of replication is useful for mobilized workforces such as insurance adjusters, financial planners, and salespeople. These workers can carry partial databases on their laptops or other devices and periodically synchronize them with the main server.
It may be most efficient for analysts to store European data in Europe, Australian data in Australia, and so on, keeping the data close to the users. In contrast, the headquarters keeps a complete set of data for high-level analysis.
Data Replication Pitfalls To Avoid
Data replication is a complex technical process. It provides advantages for decision-making, but the benefits may have a price.
Controlling concurrent updates in a distributed environment is more complex than in a centralized environment. Replicating data from various sources at different times can cause some datasets to be out of sync with others. This may be momentary, last for hours, or data could become entirely out of sync.
Database administrators should take care to ensure that all replicas are updated consistently. The replication process should be well-thought-through, reviewed, and revised as necessary to optimize the process.
More Data Means More Storage
Having the same data in more than one place consumes more storage space. It’s important to factor this cost in when planning a data replication project.
More Data Movement May Require More Processing Power and Network Capacity
While reading data from distributed sites may be faster than reading from a more distant central location, writing to databases is a slower process. Replication updates can consume processing power and slow the network down. Efficiency in data and database replication can help manage the increased load.
Streamline Replication Processes
Data replication has both advantages and pitfalls. Choosing a replication process that fits your needs will help smooth out any bumps in the road.
Of course, you can write code internally to handle the replication process — but is this really a good idea? Essentially, you’re adding another in-house application to maintain, which can be a significant commitment of time and energy. Additionally, some complexities come with maintaining a system over time: error logging, alerting, job monitoring, autoscaling, and refactoring code when APIs change.
By accounting for all of these functions, data replication tools streamline the process.
Simplify Data Replication with the Right Solution
Relational Junction lets you spend more time driving insights from data and less time managing the data itself. With a wide variety of data connectors, Relational Junction can replicate data from your SaaS applications and transactional databases to the destination of your choice. With all of your data right where you want it, you can use data analysis tools of your choice to surface business intelligence. Set up a free trial today and start gaining data-driven insights in a matter of minutes.
Rick is the Founder and CEO of Sesame Software. He has developed application systems for 40 years in his roles as a product architect, software developer, systems integrator, and database analyst. Rick developed Relational Junction to meet the market demand for easily managed enterprise application integration and data warehouse products. He has been awarded six patents for technology used in Relational Junction.