It’s no secret that data fuels the successes of businesses today. In thriving businesses, data helps shape nearly every decision carried out within an organization. No business can succeed without using data as a key component in decision-making and planning. Data is essential for gathering intelligence that can help improve business operations. Organizations have always relied on data and applications to function based on data that was collected daily, weekly, or monthly. Many businesses now aim to have data that is actionable and accessible in real-time. Real-time data access can drastically improve a company’s productivity level and opens up more opportunities for greater efficiency.

With businesses having a large number of users trying to access data frequently and in real time, it is necessary to ensure the high availability of data. Data replication and real-time connectivity are very important tools that can help businesses to connect, integrate, and work with data sets in real time. Access to this data can be managed whether the data is stored on-premise or in the cloud, or even a mix of both. 

In this article, we will offer an in-depth analysis of what real-time data replication is all about while showcasing its benefits and why it is an essential part of modern business. Finally, we will cover five easy methods in which real-time data replication can be achieved. These five methods can help to make the right decision when trying to decide which approach best matches your business and data needs.

What is Real-Time Data Replication?

Real-time data replication can be defined as the process of copying data from a data source to another place of storage, known as a destination or target, such as a data warehouse or data lake, in an instantaneous manner. When a change occurs in the data source, these changes are replicated and applied to the destination platform. Generally, this keeps the data between the source and target consistently in sync. By having data be kept in sync through replication, it can then be used for operational use, analytics, or data science purposes. 

The real time data replication process may include either data ingestion, integration, or synchronization. Data ingestion is the process of collecting data from a source and transferring it to a destination. Data integration is the process of collating data from disparate sources into a unified single view. Lastly, data synchronization is the continuous harmonization of data between the source and destination.

Real-time data replication supports fast-paced business practices, ensures high data availability at all times across multiple locations, and is an important component of data continuity. Real-time data replication allows everyone to access vital and relevant data as they need it. The benefits of using real-time database replication include increased data reliability, improved data consistency across each node where data is stored, lower data redundancy, and increases the overall performance of data infrastructure.

Why Do You Need Real-Time Data Replication?

Real-time data replication has several benefits to data-driven businesses. Most of these benefits aim towards promoting improved performance across the entire business. In other cases, it can be used by data-driven companies or organizations to share, protect, and distribute data more efficiently. With data replication, a massive benefit can be seen by synchronizing data across different locations or platforms in real-time. Below we will look at a few common reasons for implementing real-time data replication.

• Improve data availability. Real-time data replication ensures that you have your data readily available for use when users or systems need it. Traditionally, this is done by making resources accessible from multiple data sources. Data replication also makes data available when there is a breakdown of the system or some type of malicious attack which may defunct the system. High availability is a hallmark of data replication, so whether there is a software issue or hardware malfunction, you can always have access to your data.
• Increase the speed of data access: Real-time replication ensures that companies and users operating around the globe can access the company’s data and services easily and quickly. Replication can help reduce service latency or delays in retrieving data since data is replicated and available in real-time. This helps to improve the customer’s experience and business relationship with an organization.
• Disaster recovery: Real-time replication allows systems to recover from multitudes of disasters more quickly, sometimes even with zero downtime. These types of disasters include fires and floods that occur at a data center, hackers and other malicious entities, or even the deletion or locking of data mistakenly. By replicating the data produced by an organization to other destinations in real-time, no matter the worst-case scenario, you will always have a replicated database to fall back on.
• Better server performance: When only a single source of data is handling all requests for data, it can cause a massive burden on the infrastructure where the data resides. To reduce this negative effect on your business, real-time replication can be used to improve network load by enabling load balancing across data infrastructure. Load balancing can divide, route, and redirect traffic to your replicated data residing on other servers, alleviating a single server from shouldering the entire load. This is possible since each server within your network would have a copy of the needed data.  This improves the server performance of each server and better meets the data needs of your customers in real-time.
• Real-time analytical processing: Real-time data replication can help you draw actionable insights from your data to make decisions in real time. These insights can be a major factor in rapidly increasing business growth. Data replication can help to migrate data from multiple systems into a single platform so enterprise-wide data can be factored into analytics outcomes.

Methods to Achieve Real-Time Data Replication

Before diving into the methods that can be used to achieve real-time data replication, let us review a few helpful tips that are essential to implementing a solid replication strategy. Choosing to use one of the methods listed below will largely depend on several factors associated with your business needs and your data architecture. With this knowledge, you can choose a method that fits your particular situation best. The considerations to factor in when choosing the method to use include:

• Business objectives: It is important to identify your business objectives before embarking on real-time data replication. Any replication strategy you choose will be ineffective if it does not align with your business needs. You can decide on your business objectives starting by asking yourself “why is real-time data replication needed?”. Other business objective questions include asking “what type of applications can access the data?” and “what kind of users or stakeholders will be involved in the process?”. Answering these questions can help put your organization on the right path for choosing an effective real-time data replication method.
• Matching your data source and destination: It is helpful to match your data source and destination databases to ensure that they belong to the same technology. By doing this, you can ease the implementation of real-time data replication before choosing the exact method you’ll use to implement it. If this is not checked, you may experience various technical difficulties while trying to replicate data across the databases. These difficulties could include having data types or platforms not supported by the data replication strategy.
• Source and destination deployment: You should also ascertain if both your data source(s) and destination(s) are deployed on-premise or in the cloud. This will help in your decision-making and mitigate any challenge that may be experienced during the real-time data replication. Depending on where the data infrastructure is deployed, certain methods may be more advantageous than others.
• Timeliness of replication: One of the questions that need answering before choosing any method for real-time replication is how quickly the replication should occur. The definition of real-time may vary from context to context, although most modern replication methods ensure that data is replicated with sub-second latency. 
• Third-party applications: You should also assess if your source database has built-in mechanisms to aid in real-time data replication. In the case it does not, you will also need to decide if your organization will need to use, and possibly pay for, a third-party tool to have seamless data replication in real time. This can be a major consideration for highly-regulated industries which must be careful with verifying that third-party tools and applications can be used,

After going through some factors for choosing the right real-time replication method that fits your business purpose, let's look at the methods in more detail.

Method 1: Arcion Real-time data replication software 

Arcion is a data replication tool that offers real-time data replication in minutes by deploying resilient, production-grade data pipelines with supercharged Change Data Capture (CDC). It offers low-latency CDC that enables enterprises to move petabytes of data with virtually zero impact on the production environment. All of this is done in real-time. 

With Arcion, transactional integrity is guaranteed with a zero data loss architecture combined with built-in data validation and log-based CDC to guarantee data consistency. Arcion has over 100+ deployments across 3 continents, moving massive amounts of data for some of the biggest names in multiple industries every month.

Key features of Arcion's real-time data replication software include:

• Zero-code deployments: Arcion allows you to easily replicate your enterprise data without having to write custom code. It can be easily configured through CLI or an easy-to-use UI. This is made even easier by extensive documentation and support that is available to enable you to get up to speed quickly.
• Automatic schema conversion: Another key feature of Arcion’s real-time replication offering is the ability to automatically convert the schemas of various supported databases. During migration and replication, Arcion can automatically detect tables and column types across source and target systems.  The conversion process, even for SQL to NoSQL conversions, is seamless, efficient, and automatic.

• Schema evolution: Arcion enables automatic schema evolution since it can detect when there are changes to data types, tables, or even a change in the names of columns. It automatically modifies the appropriate schemas to make sure that data pipelines remain functional and do not break when data and table definitions change.
• built-in high availability: Arcion has incredible data availability ratios when compared to other tools in the market as its pipelines are constantly monitored for reliable data flow. High availability is built into the core of Arcion and is included out of the box.

• Enterprise-grade security: Arcion is SOC 2 Type I and Type II compliant. This standard ensures that Arcion is highly secure and has implemented enterprise-grade security throughout its range of solutions.
• Guaranteed data consistency: Arcion’s built-in data validation services and checkpointing system ensure consistency across all your databases. This means that the data is synced appropriately between source and target systems and there are no cases of disparity, missing, incomplete or inaccurate data.
• No resyncs: Eliminate costly resyncs required by other CDC solutions. By preceding the need to resync data, minimize the impacts to production with Arcion’s Read Once, Write Multiple technologies (patent-pending).
• Transactional integrity guaranteed: Leverage Arcion’s zero data loss architecture for guaranteed end-to-end data consistency, built-in checkpointing, and more without any custom code. Arcion guarantees consistency at the transaction level making sure that every update or create operation is properly replicated across target systems.
• Massive scalability: Leave scalability and performance concerns behind with a highly-distributed, highly parallel architecture supporting 10x faster data replication. Arcion can easily scale to meet your data replication needs without burdening your budget.

Method 2: Using Continuous Polling Methods

This method of achieving real time data replication requires you to write custom code that will be used to replicate data.  The custom code is used to identify any changes that occur in the database based on a timestamp. If the timestamp is greater than the last sync, the data changes are collected. These changes are then applied to the destination database or platform. In a case where the events can not immediately be written to the destination for whatever reason, the polling mechanism can use a queueing technique to apply the changes when the destination is available for writing once again.

Limitations of Using Continuous Polling Method

The following are limitations associated with using continuous polling methods:

• In this method, a polling script or application is required to poll for changes in the source and to move data from the source database to the destination. This constant querying can give rise to a much higher workload on the source database. At scale, this approach will eventually affect the performance and responsiveness of the source database.
• The continuous polling method requires that you have a field in the source database to enable the custom code to monitor, capture, and pull out changes that happen in the source database. This is usually done through a timestamp column with the timestamp of the last change to the row. This extra column requires constant modifications which can lead to extra load and performance degradation on the source.

Method 3: Using Trigger-Based Custom Solution

Most popular and industry-friendly databases usually have the built-in ability to create triggers on database tables and columns. These triggers can aid in real-time data replication by executing these triggers when a change to the database meets specific criteria. When a change occurs, the trigger can write the changes to a “change” table which would hold all of the updates that will be applied to data in a destination, similar to an audit log. Trigger-based custom solutions are not dependent on any timestamp column but do require a program or platform that can leverage the change data and apply it to the destination correctly. In some cases, triggers can also operate as callback functions to insert database changes into a queuing mechanism which will then apply the changes to a destination. 

Limitations of using Triggers-Based Custom solution 

The following are limitations associated with using trigger-based custom solutions:

• Triggers can put an additional load on the source databases and may affect overall system performance. Transactions may not be able to complete until a trigger linked to it successfully executes. This can lock up the database and put future changes to the database on standby until the trigger executes. This can potentially add more load to the data source and cause dips in performance.
• Triggers can not be used for every database operation. Triggers commonly can only execute based on a limited number of operations such as calling a stored procedure, inserts or updates, and other scenarios. With this, triggers may be limited for some database operations meaning that you may need to couple trigger-based CDC with another method to cover all an organization's CDC needs.

Method 4: Using Transaction Logs

Transaction logs are maintained on a database so you can monitor every operation that is or has taken place on the database. The transaction logs usually harbor information connected with any operation or task, this information ranges from updates, deletes, inserts, data definition commands, and many others. Transaction logs can also be used to store specific points where data tasks occur allowing users to collect changes to the data and replicate the changes using a queuing mechanism. Transaction logs can be scanned and used to identify changes. These changes can then be applied to the destination database. Of course, this will generally still require some type of custom script or code to load the transaction log data and statements and create the necessary changes in the destination.

Limitations of using Transaction Logs 

The following are limitations associated with using transaction logs:

• Special connectors may be required to access transaction logs on databases that offer support for real-time data replication. The special connectors may be open-sourced or licensed and may not fully function as needed or be quite pricey to purchase for use.
• The development effort needed to use this method is very tedious, time-consuming, and tough to debug. If a suitable connector can’t be found, you will be required to write a parser to extract the changes from the logs and replicate the data in real time to the destination. 

Method 5: Using Cloud-Based Mechanisms

You can use a cloud-based offering to carry out your real-time data replication seamlessly. Most cloud-structured databases used to manage and store your business data already have a robust data replication mechanism in place to enable you to effortlessly achieve the replication of your company’s data in real time. They help you replicate data with little or no coding, allowing you to perform data replication by combining event streams from databases with other streaming services.

Limitations of using Cloud-Based Mechanisms

The following are limitations associated with using Cloud-Based Mechanisms:

• Using or adding transformation-based functions will require you to build some custom code to handle the transformations.
• In a situation where your source and target databases are different or belong to a competitor, the built-in data replication method found on the cloud-based mechanism may face compatibility issues while trying to replicate your data.

Conclusion

This article has looked at real-time data replication to give you an understanding of its concept and offer insights about it. In the post, we discussed five potential methods in which data replication can be carried out in real-time. For each of these, we also glanced at potential limitations that can be associated with each method. Hopefully, you now know which method is potentially best suited to meet your data replication needs.

For many businesses, implementing real-time data replication is a monumental task. This is where Arcion truly shines, allowing teams to create robust, real-time data pipelines to enable data replication at scale. To get started today, chat with one of our real-time data replication specialists and be production-ready in no time.