Data replication is the process of creating and maintaining multiple copies of the same data in different locations as a way of ensuring data availability, reliability and resilience across an organization.
By replicating data from a source location to one or more target locations, replicas give an organization’s global users ready access to the data they need without suffering from latency issues.
When multiple copies of the same data exist in different locations, even if one copy becomes inaccessible due to disaster, outage or any other reason, another copy can be used as a backup. This redundancy helps organizations minimize downtime and data loss and improve business continuity.
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Data replication can take place over a storage area network, local area network or local wide area network, as well as to the cloud. Replication can happen either synchronously or asynchronously, which refers to how write operations are managed.
- Synchronous data replication means the data is constantly copied to the main server and all replica servers simultaneously.
- Asynchronous data replication means that data is first copied to the main server and only then copied to replica servers in batches.
Although synchronous replication ensures no data is lost, asynchronous replication requires substantially less bandwidth and is less expensive.
By employing an effective data replication strategy, organizations can benefit in the following ways:
Data replication can be used as part of a scaling strategy to accommodate increased traffic and workload demands. Replication builds scalability by distributing data across multiple nodes, which can allow for more processing power and better server performance.
Maintaining copies of data in different locations helps minimize data loss and downtime in the event of an electrical outage, cybersecurity attack or natural disaster. The ability to restore from a remote replica helps ensure system robustness, organizational reliability and security.
A globally distributed database means it must travel a shorter distance to the end user. This reduces latency and increases speed and server performance, which are especially important for real-time based workloads in gaming or recommendation systems, or resource-heavy systems like design tools.
Replication enhances fault tolerance by providing redundancy. If one copy of the data becomes corrupted or is lost due to a failure, the system can fall back on one of the other replicas. This helps prevent data loss and ensures uninterrupted operations.
By distributing data access requests across multiple servers or locations, data replication can lead to optimized server performance by putting less stress on individual servers. This load balancing can help manage high volumes of requests and ensure a more responsive user experience.
Data replication can be classified into various types based on the method, purpose and characteristics of the replication process. The three main types of data replication are transactional replication, snapshot replication and merge replication.
Transaction replication consists of databases being copied in their entirety from the primary server (the publisher) and sent to secondary servers (subscribers). Any data changes are consistently and continuously updated. Since data is replicated in real time and sent from the primary database to secondary servers in the order of their occurrence, transactional consistency is ensured. This type of database replication is commonly used in server-to-server environments.
With snapshot replication, a snapshot of the database is distributed from the primary server to the secondary servers. Instead of continuous updates, data is sent as it exists at the time of the snapshot. This type of database replication is recommended when there aren’t many data changes or when first initiating synchronization between the publisher and subscriber. Although not useful for data backups because it doesn’t monitor for data changes, snapshot replication can help with recoveries in the event of accidental deletion.
Merge replication consists of two databases being combined into a single database. As a result, any changes to data can be updated from the publisher to the subscribers. This is a complex type of database replication since both parties (the primary server and the secondary servers) can make changes to the data. This type of replication is only recommended for use in a server-to-client environment.
Replication schemes are the operations and tasks required to perform data replication. The three main data replication schemes are full replication, partial replication and no replication.
With full replication, a primary database is copied in its entirety to every site in the distributed system. This global distribution scheme delivers high database redundancy, reduced latency and accelerated query execution. The downsides of full replication are that it’s difficult to achieve concurrency and update processes are slow.
In a partial replication scheme, some sections of the database are replicated across some or all of the sites, typically data that has been recently updated. Partial replication enables prioritizing which data is important and should be replicated, as well as the distributing resources according to what the field needs.
No replication is a scheme where all data is stored on only one site. This enables easily recovering data and achieving concurrency. The disadvantages of no replication are that it negatively impacts availability and also slows down query execution.
Data replication techniques refer to the methods and mechanisms used to replicate data from a primary source to one or more target systems or locations. The most widely used data replication techniques are full-table replication, key-based replication and log-based replication.
With full-table replication, all data is copied from the data source to the destination, including all new and existing data. This technique is recommended if records are regularly deleted or if other techniques are technically impossible. Due to the size of the datasets, full-table replication does require more processing and network resources, as well as being more expensive.
In key-based incremental replications, only new data that has been added since the previous update is replicated. This technique is more efficient because fewer rows are copied. One downside of key-based incremental replication is that it does not enable replication of data from a previous update that was hard-deleted.
Log-based replication captures changes made to data at the data source by monitoring database log records (Log file or ChangeLog). These changes are then replicated to the target systems and only apply to supported database sources. Log-based replication is recommended when the source database structure is static because it could otherwise become a very resource-intensive process.
Data replication is a versatile technique that is useful in various industries and scenarios to improve data availability, fault tolerance and performance. Some of the most common data replication use cases include:
- Improve availability and failover: Data replication is commonly used to maintain redundant copies of critical data. In the event of a hardware or system failure, applications can switch to a replica, minimizing downtime and data loss.
- Strengthen disaster recovery (DR) position: By replicating data to different locations, organizations can ensure that data is preserved during natural disasters, fires or other catastrophic events affecting the primary data center.
- Increasing performance through load balancing: Distributing read requests across multiple database replicas helps balance the load on the primary system, thereby ensuring optimal performance during peak usage.
- Reduce latency for global workforce: Organizations that have multiple branch offices across a number of continents can replicate data to data centers located closer to each user. This reduces latency and improves user experience.
- Improve business intelligence and machine learning: By synchronizing cloud-based business intelligence reporting and enabling data movement from various data sources into data stores, including data warehouses or data lakes, data replication supports advanced analytics.
- Improve access to healthcare data: Replicating electronic health records (EHRs) and patient data provide healthcare professionals with quick data access to critical patient information while maintaining data redundancy.
- Gaming and online multiplayer: Replicating game data and state information across game servers helps support online multiplayer gaming, ensuring synchronization and consistent player experiences.
When implementing a data replication strategy, the growing complexity of data systems and the increased physical distance between servers within a system poses several risks, including:
Data replication tools must ensure that data remains consistent across all replicas. Replication delays, network issues or conflicts in concurrent updates can cause data schema and data profiling anomalies, such as null counts, type changes and skew.
While data replication is often used for data backup and disaster recovery, not all replication strategies provide real-time data protection (link resides outside ibm.com). If there is lag between data changes and their replication during a failure, data loss could result.
Replicating data over a network can introduce latency and consume bandwidth. High network latency or limited bandwidth can lead to replication delays, affecting the timeliness of data updates.
Replicating data to multiple locations can introduce security risks. Organizations must ensure any data replication tools used adequately protect data during replication and at-rest in all target locations.
Organizations operating in regulated industries must ensure that data replication practices comply with industry-specific regulations and data privacy laws, which can add complexity to replication strategies.
By implementing a data management system to oversee and monitor the data replication process, organizations can significantly reduce the risks involved. A software as a service (SaaS)-based data observability platform is one such system that can help ensure:
- Data is successfully replicated to other instances, including cloud instances
- Replication and migration pipelines are performing as expected
- Broken pipelines or irregular data volumes are alerted to immediately
- Data is delivered on time
- Delivered data is reliable and trusted for use in analytics
By monitoring the data pipelines involved in the replication process, DataOps engineers can ensure all data propagated through the pipeline is accurate, complete and reliable. This ensures data replicated to each instance can be reliably used by stakeholders. In terms of monitoring, an effective SaaS observability platform will be:
- Granular—indicates where the issue is with specificity
- Persistent—follows lineage to understand where errors began
- Automated—reduces manual errors and enables the use of thresholds
- Ubiquitous—delivers end-to-end pipeline coverage
- Timely—enables catching errors on time before they have an impact
Tracking pipelines enables systematic troubleshooting, so any errors are identified and can be fixed on time. This ensures users constantly benefit from updated, reliable and healthy data in their analyses. Various types of metadata that can be tracked include task duration, task status, when data was updated and more. In the event of anomalies, tracking (and alerting) helps DataOps engineers ensure data health.
Data pipeline anomaly alerting is an essential step that closes the observability loop. With alerting, DataOps engineers can fix any data health issues before they affect data replication across various instances. Within existing data systems, data engineers can trigger alerts for:
- Missed data deliveries
- Schema changes that are unexpected
- SLA misses
- anomalies in column-level statistics like nulls and distributions
- Irregular data volumes and sizes
- Pipeline failures, inefficiencies and errors
By proactively setting up alerts and monitoring them through dashboards and other preferred tools (Slack, PagerDuty, etc.), organizations can truly maximize the benefits of data replication and ensure business continuity.
IBM® Databand® is observability software for data pipelines and warehouses that automatically collects metadata to build historical baselines, detect anomalies and triage alerts to remediate data quality issues.
Providing real-time data replication between heterogeneous data stores, IBM® Data Replication software is available on-premises, as a SaaS product and within IBM® Cloud Pak® for Data.
Supporting ETL and ELT patterns, IBM® DataStage® delivers flexible and near-real-time data integration both on premises and in the cloud.
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