Real Time Data Replication: 5 Easy Strategies

With more users trying to access the data more frequently and parallelly, ensuring high data availability is one of the biggest challenges organizations face today. Real time data replication is the key to achieving that.

Irrespective of the data type, be it blogs, linked media, etc., organizations have an ever-growing need to scale up their systems to provide robust and speedier access to data. While fitter data transfer and storage technologies have facilitated this cause, data replication needs an organized mechanism to optimize data availability.

This article aims to provide you with in-depth knowledge of real time data replication, its need, and various replication strategies that you can choose from to start replicating your data and seamlessly meet your unique business needs.

Table of Contents

• Understanding Real Time Data Replication
• Understanding the Need for Real Time Data Replication
• Strategies to Achieve Real Time Data Replication
  • Strategy 1: Using Built-In Replication Mechanisms
  • Strategy 2: Using Continous Polling Methods
  • Strategy 3: Using Trigger-Based Custom Solution
  • Strategy 4: Using Transaction Logs
  • Strategy 5: Using Cloud-Based Mechanisms
• Conclusion

Understanding Real Time Data Replication

Real time data replication, a key aspect of modern data management, facilitates the synchronized transfer of data between a source and destination database in real-time. This process ensures that any changes made in the source database are promptly reflected in the destination database, enabling up-to-date information availability across multiple locations.

By employing real time data replication, organizations can generate and maintain numerous copies of complex datasets, ensuring seamless accessibility and minimizing data latency.

The implementation of real time data replication is particularly vital for ensuring data consistency and high availability in individual systems and across multiple servers. By constantly synchronizing data in real-time, organizations can mitigate the risk of data loss or inconsistencies between different database instances.

This replication mechanism acts as a safeguard, ensuring that critical information remains accessible even in the event of a system failure or network disruption.

The Need for Real Time Data Replication

The following are some of the use cases that require the ability to replicate data in real-time:

• Disaster Recovery & Compliance: Most organizations desire to have numerous copies of their crucial business data across various data nodes and replicas; to ensure high data availability and accessibility in case of an unforeseen disaster or incident. Organizations, thus, try to replicate their data across a similar kind of technology with minimal or no changes in the technology stack.
• Real-Time Online Analytical Processing: Organisations that want to draw crucial actionable insights to make data-driven decisions for their business prefer replicating their data across numerous databases. They thus ensure that their analysis workloads do not affect their primary database. They choose the destination database with robust analytical function support and faster read performance to ensure this.
• High Data Availability: To balance and execute the many concurrent requests and queries and ensure robust performance, organizations leverage either the built-in load balancers to intelligently manage and route the traffic or completely replicate their transactional database for smooth data querying.

Also Read: Advantages of Data Replication

Strategies to Achieve Real Time Data Replication

The choice of mechanism for achieving real time data replication depends on several factors associated with your database architecture design. The following are some of the deciding factors that you must take into consideration:

• Do the source and destination databases belong to the same technology?
• Does the source database have any built-in mechanism for replication?
• Are the source and destination databases deployed on-premise or on the cloud? 
• Does the organization have the infrastructural bandwidth to deploy a replication solution external to the source and target database?
• Does the organization possess the development skills to implement a custom solution?
• Is the organization open to paid third-party tools?

Depending on your answers to such questions, you can consider one of the following replication strategies:

• Strategy 1: Using Built-In Replication Mechanisms
• Strategy 2: Using Continous Polling Methods
• Strategy 3: Using Trigger-Based Custom Solution
• Strategy 4: Using Transaction Logs
• Strategy 5: Using Cloud-Based Mechanisms

Strategy 1: Using Built-In Replication Mechanisms

Most enterprise-grade transaction databases, such as Oracle, MariaDB, PostgreSQL, etc., house robust support for in-built data replication mechanisms that help users back up their data to a similar replica database.

Such built-in mechanisms are highly reliable, easy to configure, and properly documented, allowing database administrators to set up data replication seamlessly. These mechanisms leverage “log reading” techniques to achieve the change data capture functionality. 

You can check out our detailed guide to see a real-life example of real time data replication for the Oracle database.

Limitations of Using Built-In Mechanisms to Carry Out Real time Data Replication:

• The compatibility of the built-in data replication mechanism depends highly on the major and minor versions of the source and destination databases. You may face various technical difficulties while trying to replicate data across databases running on different software versions, however, this is not true for every database.
• Built-in mechanisms require the source and the destination database to be of the same vendor and have the same technology stack. Leveraging such techniques to replicate data across platforms can thus result in a lot of technical difficulties. For example, if your source database is Oracle and your destination database is PostgreSQL, then using the built-in replication methods can result in complications.
• Using built-in mechanisms to replicate data may require you to invest large amounts of money to purchase the software license. For example, Oracle requires you to pay for its in-built data replication mechanism, Oracle Golden Gate, if you want to transfer data from your Oracle database.

Strategy 2: Using Continous Polling Methods

Using the continuous polling mechanism to achieve real time data replication requires you to write custom code snippets to replicate/copy data from your source database to your destination database. You will then need to leverage the polling mechanism to monitor the changes in the data source.

You will have to develop code snippets that identify such changes, format them and update them on the destination database. The continuous polling mechanism leverages a queue technique, such as Kafka, RabbitMQ, etc., to decouple them if the destination database can’t reach the source database or requires isolation.

Limitations of Using Continous Polling Methods to Carry Out Real time Data Replication

• Using the continuous polling mechanism requires having a field in the source database that helps your code snippets monitor and capture changes. Such columns or attributes are usually timestamp-based columns that change when a modification occurs in the database.
• Having a polling script in place results in a much higher source database load, affecting its performance and response speeds.

Strategy 3: Using Trigger-Based Custom Solution

Databases such as Oracle, MariaDB, etc. house an intuitive functionality of built-in triggers that execute under specific scenarios to carry out an operation such as data replication. These operate as callback functions and insert data-based changes into a queuing mechanism such as Kafka, RabbitMQ, etc., and then the consumer helps transfer data to the destination database.

These triggers are robust and reliable and require leveraging a network transfer operation and transport layer. These, further, are not dependent on any timestamp column.

Limitations of using Triggers to Carry Out Real time Data Replication

• Triggers provide support only for a limited number of operations, such as insert operations, calling a function, etc., hence you might still require having mechanisms, such as polling, in place to carry out replication successfully.
• Triggers might be an additional load on your source databases, as most transactions can’t occur until the trigger associated with them happens.

Strategy 4: Using Transaction Logs

Most databases maintain transaction logs to monitor operations that are occurring in the database. Such data logs contain information associated with operations/tasks such as data definition commands, inserts, updates, deletes, etc., and even keep track of the specific points where these tasks occur.

Such databases allow users to access the transaction logs non-synchronously to fetch the changes and then leverage a queuing mechanism like Kafka to replicate changes in the destination database.

Limitations of using Transaction Logs to Carry Out Real time Data Replication

• Databases that support data replication using transaction logs may require you to use special connectors to access them. For example, accessing the transaction logs of your Oracle database can be challenging, as it requires you to use either an open-source or a licensed connector.
• The development effort needed to execute this is very high if you do not manage to find a connector capable of parsing your database’s transactional log. In extreme cases, you may need to write your own parser to accomplish this. 

Strategy 5: Using Cloud-Based Mechanisms

If you leverage a cloud-based database to manage and store your crucial business data, you might already have robust mechanisms for your cloud computing platform. Various cloud service providers house the support for carrying out real time data replication.

For example, Amazon Web Services (AWS), allows users to achieve data replication by combining event streams from their databases with AWS streaming services such as AWS Kinesis, Lambda functions, etc. Such functionalities let users replicate data with no or minimal coding.

Limitations of using Cloud-Based Mechanism to Carry Out Real time Data Replication

• Built-in data replication methods provided by cloud-service providers often face issues while replicating your data when your data source or destination belongs to an alternate cloud service provider or any third-party database service.
• If you want to add transformation-based functionalities, you must develop robust code snippets supporting this operation.

Related: Best Data Replication Softwares

Conclusion

This article teaches you in-depth about real time data replication and various strategies to achieve it. It briefly introduces numerous concepts related to such strategies and helps the users understand them better and use them to perform data replication and recovery in the most efficient way possible. These methods, however, can be challenging, especially for a beginner, and this is where Hevo saves the day.

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