Data replication is the process of storing the same data in multiple locations to improve data availability and accessibility, and to improve system resilience and reliability.
One common use of data replication is for disaster recovery, to ensure that an accurate backup exists at all times in case of a catastrophe, hardware failure, or a system breach where data is compromised.
Having a replica can also make data access faster, especially in organizations with a large number of locations. Users in Asia or Europe may experience latency when reading data in North American data centers. Putting a replica of the data closer to the user can improve access times and balance the network load.
Replicated data can also improve and optimize server performance. When businesses run multiple replicas on multiple servers, users can access data faster. Additionally, by directing all read operations to a replica, administrators can save processing cycles on the primary server for more resource-intensive write operations.
When it comes to data analytics, data replication has yet another meaning. Data-driven organizations replicate data from multiple sources into data warehouses, where they use them to power business intelligence (BI) tools.
How data replication works
Replication involves writing or copying the same data to different locations. For example, data can be copied between two on-premises hosts, between hosts in different locations, to multiple storage devices on the same host, or to or from a cloud-based host. Data can be copied on demand or be transferred in bulk or in batches according to a schedule, or be replicated in real time as the data is written, changed, or deleted in the master source.
Benefits of data replication
By making data available on multiple hosts or data centers, data replication facilitates the large-scale sharing of data among systems and distributes the network load among multisite systems. Organizations can expect to see benefits including:
Improved reliability and availability: If one system goes down due to faulty hardware, malware attack, or another problem, the data can be accessed from a different site.
- Improved network performance: Having the same data in multiple locations can lower data access latency, since required data can be retrieved closer to where the transaction is executing.
- Increased data analytics support: Replicating data to a data warehouse empowers distributed analytics teams to work on common projects for business intelligence.
- Improved test system performance: Data replication facilitates the distribution and synchronization of data for test systems that demand fast data accessibility.
Data replication challenges
Though replication provides many benefits, organizations should weigh the benefits against the disadvantages. The challenges to maintaining consistent data across an organization boil down to limited resources:
- Money: Keeping copies of the same data in multiple locations leads to higher storage and processor costs.
- Time: Implementing and managing a data replication system requires dedicated time from an internal team.
- Bandwidth: Maintaining consistency across data copies requires new procedures and adds traffic to the network.
Data replication methods
When it comes to replicating data from databases, there are three basic methods for replicating data:
Full table replication
Full table replication copies everything from the source to the destination, including new, updated, and existing data. This method is useful if records are hard deleted from a source on a regular basis, or if the source doesn’t have a suitable column for key-based replication, a method we’ll get into in a moment.
However, this method has several drawbacks. Full table replication requires more processing power and generates larger network loads than copying only changed data. Depending on what tools you use to copy full tables, the cost typically increases as the number of rows copied goes up.
Key-based incremental replication
Key-based incremental replication — also known as key-based incremental data capture or key-based incremental loading — updates only data changed since the previous update. Since fewer rows of data are copied during each update, key-based replication is more efficient than full table replication. However, one major limitation of key-based replication is its inability to replicate hard-deleted data, since the key value is deleted when the record is deleted.
Log-based incremental replication
Log-based incremental replication is a special case of replication that applies only to database sources. This process replicates data based on information from the database log file, which lists changes to the database. This method is the most efficient of the three, but it must be supported by the source database, as it is by MySQL, PostgreSQL, and Oracle.
This method works best if the source database structure is relatively static. If columns are added or removed or data types change, the configuration of the log-based system must be updated to reflect the changes, and this can be a time- and resource-intensive process. For this reason, if you anticipate your source structure requiring frequent changes, it may be better to use full table or key-based replication.
Data replication schemes
Organizations can perform data replication by following a specific scheme to move the data. These schemes are different than the aforementioned methods above. Rather than serving as an operational strategy for continuous data movement, a scheme dictates the way in which data can be replicated in order to best meet the needs of a business: moved in full or moved in parts.
Full database replication
Full database replication is where an entire database is replicated for use from 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.
Partial replication is where the data in the database is divided into sections, with each stored in different locations based its importance for each location. Partial replication is useful for mobilized workforces such as insurance adjusters, financial planners, and sales people. These workers can carry partial databases on their laptop or other device and periodically synchronize them with a main server.
For analysts, it may be most efficient to store European data in Europe, Australian data in Australia, and so on, keeping the data close to the users, while the headquarters keeps a complete set of data for high-level analysis.
Data replication process
The benefits of data replication are useful only if there’s a consistent copy of the data across all systems. Following a process for replication helps ensure consistency.
- Identify the data source and destination.
- Select tables and columns from the source to be copied.
- Determine the frequency of updates.
- Determine a replication method: full table, key-based, or log-based.
- For key-based replication, identify replication keys, which are columns that, if changed or updated in the source, will trigger the records that they’re part of to be copied in the replication process.
- Write custom code or use a replication tool to run the replication process.
- Monitor the extraction and loading processes for quality control.
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 a variety of 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 completely 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 the replication process with the right tool
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 that you need to maintain, which can be a big commitment of time and energy. Additionally, there are complexities that come along 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 the right way
As a cloud-first, open source platform for rapidly moving data, Stitch lets you spend more time gleaning insights from data and less time on data management.
With more than 90+ connectors, Stitch can replicate data from your SaaS tools and transactional databases to your data warehouse, where you can use data analysis tools to surface business intelligence. With Stitch’s ready-to-go, out-of-the-box solution, you don’t have to write your own data replication process. Set up a free trial today and start gaining data-driven insights tomorrow.