Data replication is the process of copying data from a system of record to a backup system. There are several compelling uses for having a working copy of a source database. Organizations replicate data to support high availability, accessibility, backup, and disaster recovery.
In this white paper, we will be covering:
- Common reasons to use data replication
- How data replication works
- Benefits of replication
- Methods to accomplish your goals
Common Uses of Data Replication
Improve the Availability of Data
Having data distributed across networks improves fault tolerance and accessibility, especially across global organizations. Data replication enhances the resilience and reliability of systems by storing data at multiple nodes across a global network.
Access Data for Reporting and Analytics
Data-driven organizations replicate data from multiple sources into data warehouses, where they use them to power business intelligence (BI) tools. Storing data from the various applications in a common data warehouse, also allows reports to span multiple applications. This gives BI users the proverbial 360-degree view of their corporate data.
Increase Data Access Speed
In organizations where there are multiple branch offices spread across the globe, users may experience some latency while accessing data from one country to another. Placing replicas on local servers provides users with faster data access and query execution times.
Enhance Server Performance
Replicated data can also improve and optimize server performance. When it comes to data analytics and business intelligence, using the original source system’s database puts a drain on system resources. This can lead to performance issues with the original transactional systems. By directing all read operations to a replica, administrators can save processing cycles on the primary server for more resource-intensive write operations.
Database replication effectively reduces the load on the source application server. As a result, the performance of the network improves by dispersing the data among other nodes in the distributed system.
Ensure Disaster Recovery
Businesses are often susceptible to data loss due to a data breach or hardware malfunction. It is possible to compromise the employees’ or clients’ valuable data during a disaster. Data replication facilitates the recovery of lost or corrupted data by maintaining accurate backups at well-monitored locations. A recovery tool is also essential to this end, one that can retain backups for varying lengths of time according to data retention best practices and a patchwork of laws governing data retention.
How Data Replication Works
Replication involves copying data from a variety of source systems to different locations. For example, data can be copied between two on-premises hosts, between hosts in other locations, to multiple storage devices on the same host, or to or from a cloud-based host. Data replications can be done according to a schedule, in real-time, changed, or deleted in the master source.
The challenge is finding a solution that works with all of your data, without needing different solutions for each application. Avoid niche products that only handle one or two applications.
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 Methods
First, let us examine the methods of data replication in the context of latency. Some use cases require real-time replication, such as having a standby database ready in the event of a database server failure.
Standby databases provide redundancy in the event of a database server failure. A corrupted filesystem or a broken network path can cause this to happen. In this case, a hot backup database that can be automatically switched to be the active database gives an extra layer of protection to keep systems running with no downtime.
Several database platforms provide the ability to replicate every new transaction in the database to a Standby Database. If this is done in real-time, the process is called Change Data Capture. CDC uses database logs on the source database instead of polling the data itself.
Oracle was one of the first to implement this twenty years ago with Real Application Clusters (RAC). A database in one datacenter could be configured to send transaction logs in real-time to another Oracle database server anywhere, over the internet. The remote standby database server would receive the SQL statements from the source database’s transaction logs and apply them to the standby database. The DBA would set up this process by exporting the source database and importing it to the standby database, then turning on the transaction replication.
If the primary database went offline, the connection information is configured to use the standby database instead. This is commonly known as fail-over mode. SQL Server was perhaps the second DBMS vendor to accomplish this.
Oracle GoldenGate expands this capability to include databases from non-Oracle sources such as DB2, SQL Server, MySQL, and Sybase. GoldenGate requires a separate process to build the database tables and do the initial population since it only captures change logs. Migrating the baseline data from a non-Oracle database is a major technical challenge that each customer faces.
Near-Real Time Replication
A fairly recent product space is near-real-time replication. This is used to create a data warehouse that is simply a clone of the source database. Instead of spending months designing and data mapping a data warehouse into a structure that simplifies the accessibility of information for reporting users, the source schema is recreated in the target warehouse database.
This approach requires an abstraction layer when the source application’s database schema is too complex for business users to understand. This can be accomplished by creating views on top of the mirrored tables.
Most reporting products allow an administrator to define metadata in the reporting application. This resolves the complexity into topics, which are subject areas such as customer accounts and contacts, financial transactions, support cases, and inventory. Standard database technologies today either have built-in capabilities or use third-party tools to accomplish data replication. While Oracle Database and Microsoft SQL actively support data replication, some traditional technologies may not include this feature out of the box.
When it comes to replicating data from databases, there are several 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 regularly or if the source doesn’t have unique keys or change timestamps.
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.
Change Data Capture
In this method, the data replication software makes full initial copies of data from origin to destination, following which the subscriber database receives updates whenever data is modified. This is a more efficient mode of replication since fewer rows are copied each time data is changed.
Transactional replication is usually found in server-to-server environments the database logs can be monitored, captured, parsed, streamed to the receiving server, and applied to the receiving database. This Change Data Capture technique rarely works for SaaS applications, most of which lack a notification mechanism.
In Snapshot replication, data is replicated exactly as it appears at any given time. Unlike other methods, Snapshot replication does not pay attention to the intervening changes made to data. This replication mode is used when changes made to data tend to be infrequent, for example, performing initial synchronizations between publishers and subscribers.
Timestamp-Based Incremental Replication
Timestamp-based incremental replication updates only data changed since the previous update. In contrast with full table replication, timestamp-based replication copies fewer rows of data during each update – making it more efficient. Limitations of this technique are its inability to replicate or detect hard-deleted data, and not being able to update records that do not have unique keys.
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 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 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 the Right Way
Relational Junction lets you spend more time driving insights from data and less time managing the data itself. Relational Junction can replicate data from your SaaS applications and transactional databases to your data warehouse in minutes. From there, you can use data analysis tools to surface business intelligence.
With Relational Junction’s click-and-go solution, you don’t have to write your own data replication process. There is no coding, data mapping, or modeling required. With patented multithreaded technology, Relational Junction ensures the fastest possible data movement. Set up a free trial today and start gaining data-driven insights in a matter of minutes!