Data Replication: The Basics, Risks, and Best Practices

Databand
2022-04-27 13:20:30

Data-driven organizations are poised for success. They can make more efficient and accurate decisions and their employees are not impeded by organizational silos or lack of information. Data replication enables leveraging data to its full extent. But how can organizations maximize the potential of data replication and make sure it helps them meet their goals? Read on for all the answers.

What is Data Replication?

Data replication is the process of copying or replicating data from the main organizational server or cloud instance to other cloud or on-premises instances at different locations. Thanks to data replication, organizational users can access the data they need for their work quickly and easily, wherever they are in the world. In addition, data replication ensures organizations have backups of their data, which is essential in case of an outage or disaster. In other words, data replication creates data availability at low latency.

Data replication can take place either synchronously or asynchronously. Synchronously means the data is constantly copied to the main server and all replica servers at the same time. Asynchronous data replication means that data is first copied to the main server and only then copied to replica servers. Often, it occurs in scheduled intervals.

Why Data Replication is Necessary

Data replication ensures that organizational data is always available to all stakeholders. By replicating data across instances, organizations can ensure:

Scalability

Data scalability is the ability to handle changing demands by continuously adapting resources. Replication of data across multiple servers builds scalability and ensures the availability of consistent data to all users at all times.

Disaster Protection

Electrical outages, cybersecurity attacks and natural disasters can cause systems and instances to crash and no longer be available. By replicating data across multiple instances, data is backed up and always accessible to any stakeholder. This ensures system robustness, organizational reliability and security.

Speed / Latency

Data that has to travel across the globe creates latency. This creates a poor user experience, which can be felt especially in real-time based applications like gaming or recommendation systems, or resource-heavy systems like design tools. By distributing the data globally it travels a shorter distance to the end user, which results in increased speed and performance.

Test System Performance

By distributing and synchronizing data across multiple test systems, data becomes more accessible. This availability improves their performance.

An Example of Data Replication

Organizations that have multiple branch offices across a number of continents can benefit from data replication. If organizational data only resides in servers in Europe, users from Asia, North America and South America will experience latency when attempting to read the data. But by replicating data across instances in San Francisco, São Paulo, New York, London, Berlin, Prague, Tel Aviv, Hyderabad, Singapore and Melbourne, for example, all users can improve access times for all users significantly.

Data Replication Variations

Types of Data Replication

Replication systems vary. Therefore, it is important to distinguish which type is a good fit for your organizational infrastructure needs and business goals. There are three main types of data replication systems:

Transactional 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. Transactional consistency is ensured, which means that data is replicated in real-time and sent from the primary server to secondary servers in the order of their occurrence. As a result, transactional replication makes it easy to track changes and any lost data. This type of replication is commonly used in server-to-server environments.

Snapshot Replication

In the snapshot replication type, 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. It is recommended to use this type of replication when there are not many data changes or at the initial synchronization between the publisher and subscriber.

Merge Replication

A 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 replication since both parties (the primary server and the secondary servers) can make changes to the data. It is recommended to use this type of replication in a server-to-client environment.

Comparison Table: Transactional Replication vs. Snapshot replication vs. Merge Replication

Data Replication Table

Schemes of Replication

Replication schemes are the operations and tasks required to perform replication. There are three main replication schemes organizations can choose from:

Full Replication

Full replication occurs when the entire database is copied in its entirety to every site in the distributed system. This scheme improves data availability and accessibility through database redundancy. In addition, performance is improved because global distribution of data reduces latency and accelerates query execution. On the other hand, it is difficult to achieve concurrency and update processes are slow.

Data Replication - Full

Partial Replication

In a partial replication scheme, some sections of the database are replicated across some or all of the sites. The description of these fragments can be found in the replication schema. Partial replication enables prioritizing which data is important and should be replicated as well as distributing resources according to the needs of the field.

Data Replication - Partial

No Replication

In this scheme, data is stored on one site only. This enables easily recovering data and achieving concurrency. On the other hand, it negatively impacts availability and performance.

No Data Replication

Techniques of Replication

Replicating data can take place through different techniques. These include:

Full-table Replication

In a full-table replication, all data is copied from the source to the destination. This includes new data, as well as existing data. It is recommended to use this technique if records are regularly deleted or if other techniques are technically impossible. On the other hand, this technique requires more processing and network resources and the cost is higher.

Key-based Replication

In a key-based replication, only new data that has been added since the previous update, is updated. This technique is more efficient since less rows are copied. On the other hand, it does not enable replication data from a previous update that might have been hard-deleted.

Log-based Replication

A log-based replication replicates any changes to the database, from the DB log file. It applies only to database sources and has to be supported by it. This technique is recommended when the source database structure is static, otherwise it might become a very resource-intensive process.

Cloud Migration + Data Replication

When organizations digitally transform their infrastructure and migrate to the cloud, data can be replicated to cloud instances. By replicating data to the cloud, organizations can enjoy its benefits: scalability, global accessibility, data availability and easier maintenance. This means organizational users benefit from data that is more accessible, usable and reliable, which eliminates internal silos and increases business agility.

Data Risks in the Replication Process

When replicating data to the cloud, it is important to monitor the process. The growing complexity of data systems as well as the increased physical distance between servers within a system could pose some risks.

These risks include:

Inconsistency

Data schema and data profiling anomalies, like null counts, type changes and skew.

Data Loss

Ensuring all data has been migrated from the sources to the instances.

Delays

Data not being successfully migrated on time.

Data Replication Management + Observability

By implementing a management system to oversee and monitor the replications process, organizations can significantly reduce the risks involved in the data replication process. A data observability platform will ensure:

  • Data is successfully replicated to other instances, including cloud instances
  • Replication and migration pipelines are performing as expected
  • Any broken pipelines or irregular data volumes are alerted about so they can be fixed
  • Data is delivered on time 
  • Delivered data is reliable, so organizational stakeholders can use it for analytics

Monitoring

By monitoring the data pipelines that take part in the replication process, organizations and their DataOps engineer can ensure the data propagated through the pipeline is accurate, complete and reliable. This ensures data replicated to all instances can be reliably used by stakeholders. An effective monitoring system will be:

  • Granular – specifically indicating where the issue is
  • Persistent – following lineage to understand where errors began
  • Automated – reducing manual errors and enabling the use of thresholds
  • Ubiquitous – covering the pipeline end-to-end
  • Timely – enabling catching errors on time before they have an impact

Learn more about data monitoring here.

Tracking

Tracking pipelines enables systematic troubleshooting, so that any errors are identified and fixed on time. This ensures users constantly benefit from updated, reliable and healthy data in their analyses. There are various types of metadata that can be tracked, like task duration, task status, when data was updated, and more. By tracking and alerting (see below) in case of anomalies, DataOps engineers ensure data health.

Alerting

Alerting about and data pipeline anomalies is an essential step that closes the observability loop. Alerting DataOps engineers gives them the opportunity to fix any data health issues that might 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 tools of your choice (Slack, Pagerduty, etc.), organizations can truly maximize the potential of data replication for their business.

Conclusion

Data replication holds great promise for organizations. By replicating data to multiple instances, they can ensure data availability and improved performance, as well as internal “insurance” in case of a disaster. This page covers the basics for any business or data engineer getting started with data replication: the variations, schemes and techniques, as well as more advanced content for monitoring the process to gain observability and reduce the potential risk.

Wherever you are on your data replication journey, we recommend auditing your pipelines to ensure data health. If you need help finding and fixing data health issues fast, click here.