One of the biggest challenges that most organizations face today is ensuring the high availability and accessibility of data over the complex set of networks they have in place. Having around-the-clock and real-time access to crucial business data can help organizations carry out processes seamlessly and maintain a steady revenue flow. Thus, organizations need to scale their systems and provide support for accessing data seamlessly. Data Replication is one such technique.

Data Replication allows users to access data from numerous sources, such as servers, sites, etc. in real-time, thereby tackling the challenge of maintaining high data availability. This article aims at providing you with in-depth knowledge of what data replication is, the benefits and challenges associated with data replication, types of data replication, and more.

Upon a complete walkthrough of the content, you’ll understand what Data Replication is all about and why you must start replicating and maintaining your crucial business data.

What is Data Replication?

Data Replication refers to the process of storing and maintaining numerous copies of your crucial data across different machines. It helps organizations ensure high data availability and accessibility at all times, thereby allowing organizations to access and recover data even during an unforeseen disaster or data loss.

There are multiple ways of Data Replication, namely, Full Replication, which allows users to maintain a replica of the entire database across numerous sites, or Partial Replication, which lets users replicate a small section/fragment of the database to a destination of their choice.

Data Replication Examples

With data being present in several technologies such as databases, data warehouses, applications, and data lakes, there are multiple data replication examples possible:

  • Database to Database Replication: You can either replicate the database to a similar database or to an entirely new database. For instance, replicating data from a single instance of PostgreSQL to an instance of PostgreSQL in a different time zone allows users to query databases near them. For cases when you want to leverage the map-reduce functionality of MongoDB, you can perform cross-replication between PostgreSQL and MongoDB.
  • Database to Data Lake Replication: Before using data for analysis in a data warehouse, you might want all your unstructured and raw data to be available in a  data lake, saving time for defining data structures, schema, and transformations. For example, AWS offers a data lake solution via Amazon S3. You might want to replicate your sales data present in PostgreSQL into S3, from where you can extract and process the raw data for training your forecasting models.
  • Database to Data Warehouse Replication: Querying your database directly is often harmful, inefficient, and won’t give an effective performance. Hence, firms often replicate data from databases like SQL Server to a data warehouse like Snowflake, BigQuery, or Redshift to enjoy their powerful analytics engine.
  • Database to Search Engine Replication: Doing text-based lookups on a database like MySQL is ineffective, especially for large datasets. A more effective solution is replicating your data from a database to dedicated search engines like ElasticSearch.
  • Application to Data Warehouse Replication: For data stored in your external applications, you can extract data via API calls and load it to a data warehouse for further processing by building an ETL pipeline.
  • Data Warehouse to Application Replication: For operational analytics, you would often need data in your data warehouse to be fed back to an application like Salesforce for real-time decision-making.          
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How does Data Replication work?

Now that you’re familiar with what is Data Replication let’s discuss the replication process in brief. Most in-use enterprise-grade databases such as MongoDB, Oracle, PostgreSQL, etc., house the support for replicating data with ease.

These databases allow users to copy data, either by leveraging the in-built replication functionalities or using third-party tools to achieve replication. In either case, the general process of replicating data remains identical.

The following process represents the general steps a user needs to carry out to replicate data properly:

  • Step 1: The first step of replicating data is identifying your desired data source and the destination system, where you’ll store the replica.
  • Step 2: Once you’ve decided on your data source and destination, you need to copy the desired database tables and records from your source database.
  • Step 3: The next important step is to fix the frequency of updates, that is how frequently you want to refresh your data.
  • Step 4: With the replication frequency now decided, you now need to choose the desired replication mechanism, selecting between Full, Partial or Log-based.
  • Step 5: You can now either develop custom code snippets or use a fully-managed data integration & replication platform such as Hevo Data, to carry out replication.
  • Step 6: With the Data Replication process happening, you need to keep track of how the data extraction, filtering, transformation, and loading is taking place to ensure data quality and seamless process completion.

The common types of Data Replication processes are listed below: 

  • Transactional Replication: In Transactional Replication, first all the existing data is replicated to the destination. Then, as a new row comes into the source system, it replicates it immediately to the destination. This ensures transaction consistency.
  • Snapshot Replication: Snapshot Replication takes a snapshot of the source system at the time of replication and then replicates the same data to all the destinations. It does not consider any changes in data during replication.
  • Merge Replication: Merge Replication merges two or more Databases into a single Database. It is one of the complex types of Data Replication. This Data Replication type is used when the data is distributed across multiple data sources and wants to unify and synchronize data in one place so that all users can use it.

What are Data Replication Schemes?

There are 3 main Data Replication Schemes used for Database Replication. The following Data Replication Schemes are listed below:

1) Full Database Replication

Full Replication involves copying the complete Database to every node of the distributed system. With this, users can achieve high data redundancy, better performance, and high data availability. It takes a long time because data updates need to replicate to all the nodes.

2) Partial Replication

Partial Replication copies only a particular part of the Database that is decided based on the business requirements or priority. The number of copies for each part of the Database can range from one to the number of total nodes in the distributed system.

3) No Replication

Data Replication - No Replication
No Replication

Here, your data is stored on one site only, i.e., only one fragment exists on each node of the distributed system. Offering easy data recovery and better concurrency, this type of data replication scheme is the fastest to perform and helps achieve effortless data synchronization.

Also Read: Real-time Data Replication

What are the Common 3 Data Replication Techniques?

ere’s a list of most widely used data replication techniques:

  • Full Table Replication: Full replication copies all the data from the source system to the destination. It consists of existing and updated rows and replicates the hard deletes. The disadvantage of Full-time replication is that it puts a burden on the network and takes time if the dataset is large.
  • Key-based Incremental Replication: In Key-based Incremental Replication, the first keys are scanned and checked if any index is added, deleted, or updated. Then, only relevant keys or indexes are replicated to the destination making the backup process faster.
  • Log-based Incremental Replication: Log-based incremental replication is a method of replicating data by capturing changes to a source database as log records(Log file or ChangeLog) and then applying those changes to one or more target databases. This approach makes the replication process more efficient and less disruptive to the source database, as only the changes are replicated and not the entire dataset. It is commonly available for MySQL, PostgreSQL, and MongoDB backend databases.

Read More: Data Replication Strategies

What are the Benefits of Data Replication?

Replicating data and maintaining multiple copies of data across various servers provides users with numerous benefits, such as robust performance, data security, data durability, etc. The following are some of the key benefits of Data Replication:

1) Better Application Reliability

Data Replication across various machines helps ensure that you can easily access the data, even when a hardware or machinery failure occurs, thereby boosting the reliability of your system.

2) Better Transactional Commit Performance

When you’re working with transactional data, you need to monitor various synchronous processes to ensure that the data updation occurs everywhere simultaneously. Hence, your application must write the commit before the control threads can continue the tasks.

Data Replication helps avoid such additional disk-based I/O operations by eradicating the data dependency on the master node only, thereby making the entire process more durable.

3) Better Read Performance

With Data Replication in place, users can route data reads across numerous machines that are a part of the network, thereby improving upon the read performance of your application. Hence, readers working on remote networks can easily fetch and read data with minimal latency.

4) Data Durability Guarantee

Data Replication helps boost and ensure robust data durability, as it results in data changes/updation taking place on multiple machines simultaneously instead of a single computer. It thereby provides more processing and computation power by leveraging numerous CPUs and disks to ensure that the replication, transformation, and loading processes take place correctly.

5) Robust Data Recovery

Organizations depend on diverse software and hardware to help them carry out their daily operations and, hence, fear any unforeseen data breaches or losses. Replication allows users to maintain backups of their data that update in real time, thereby allowing them to access current and up-to-date data, even during any failures/ data losses.

Read More: Advantages of Data Replication

4 Burning Challenges of Data Replication

Data Replication provides users with numerous benefits that help boost performance and ensure data availability. However, it also poses some challenges to the users trying to copy their data. The following are some of the challenges of replicating your data:

1) High Cost

Replicating data requires you to invest in numerous hardware and software components such as CPUs, storage disks, etc., along with a complete technical setup to ensure a smooth replication process.

It further requires you to invest in acquiring more “manpower” with a strong technical background. All such requirements make the process of replicating data, challenging, even for big organizations.

2) Time Consuming

Carrying out the tedious task of replication without any bugs, errors, etc., requires you to set up a reaction pipeline. Setting up a reaction pipeline that operates correctly can be a time-consuming task and can even take months, depending upon your replication needs and the task complexities.

Further, ensuring patience and keeping all the stakeholders on the same page for this period can turn out to be a challenge even for big organizations.

3) High Bandwidth Requirement

With replication taking place, a large amount of data flows from your data source to the destination database. To ensure a smooth flow of information and prevent any loss of data, having sufficient bandwidth is necessary.

Maintaining bandwidth, capable of supporting & processing large volumes of complex data while carrying out the replication process can be a challenging task, even for large organizations.

4) Technical Lags

One of the biggest challenges that an organization faces when replicating its data is technical lags. Replication usually involves leveraging master nodes and slave nodes. The master node acts as the data source and represents the point where the data flow starts and reaches the slave nodes.

These slave nodes usually face some lag associated with the data coming from the master node. Such lags can occur depending upon the system configurations and can range from a few records to hundreds of data records.

Since the slave nodes often suffer from some lag, they often face delays and do not update the data in real-time. Lags are a common issue with most systems and applications. However, they can be quite troublesome in cases as follows:

  • In case you’re shopping on an e-commerce website, and you add products to your cart, but upon reaching the checkout stage, the “products” disappear. This happens due to a lag in replication in the slave node.
  • In case you’re working with a transactional data flow, the transactions you might have made are taking time to reflect at the destination. This happens due to a lag in replication in the slave node.

Hevo can abstract all these challenges with its automated No Code Data Pipeline. With Hevo in place, you can perform Data Replication seamlessly.

Data Replication Use Cases

Data replication is used in several industries on a daily basis for multiple use cases, such as:

  • Banking and Financial Services: Financial institutions track customer transactions in real-time and then replicate them into a production database to detect fraudulent transactions.
  • Online Retail: With past customer transactions, social media engagement, and buying behavior available in a single place by near real-time data replication, e-commerce firms can generate relevant offers to customers and boost conversions.
  • Healthcare: Hospitals and healthcare organizations use data replication to keep multiple copies of patient records, test results, and other sensitive information in different locations for disaster recovery and remote access. This also allows researchers to get complete health information in a single place and effectively detect diseases.
  • Manufacturing: By replicating production data, inventory, and supply chain information, manufacturing firms can easily detect problems in their production lines and better optimize their processes for higher efficiency.

What to Consider When it Comes to Data Replication?

While you are replicating data on-premise data to Cloud, across multiple Cloud environments, or bidirectionally. It is essential to keep the point of a few factors listed below:

  • How to reign in network and storage costs 
  • How to minimize the impact on production workloads

First, minimizing the storage requirements and cost of operating the data is essential. One should deduplicate the data before performing a Data Replication process; this can be automated.

The wide area network (WAN) connections deliver fast speed with little impact on production workloads. Also, you should have good knowledge of whether your company needs synchronous or asynchronous Data Replication.

The Synchronous Data Replication process writes data to its primary storage and to its replica server at the same time. This will allow you to replicate the data but it comes with 2 major drawbacks that are time and cost.

To replicate data, it is essential to confirm that all the data is written properly. It decreases the network performance significantly and increases the cost.

The Asynchronous Data Replication process writes data to the replicas after it has been written to the primary storage. Once the storage device receives the data, the write operation is considered complete. It requires less bandwidth for the network and is designed to work for long distances. 

Based on your requirements and priorities, you can select from a variety of data replication software including open-source replication tools.

Also Read: Data Replication Best Practices

How does HEVO help in Data Replication?

Hevo Data, a Fully-managed Data Pipeline platform, can help you automate, simplify & enrich your data replication process in a few clicks. With Hevo’s wide variety of connectors and blazing-fast Data Pipelines, you can extract & load data from 150+ Data Sources, including straight into your Data Warehouses or any Databases. To further streamline and prepare your data for analysis, you can process and enrich raw granular data using Hevo’s robust & built-in Transformation Layer without writing a single line of code.

Hevo is the fastest, easiest, and most reliable data replication platform that will save your engineering bandwidth and time multifold. Try our 14-day full access free trial today to experience an entirely automated hassle-free Data Replication with transparent pricing and world-class customer support.

Data Replication FAQs

1) What is data replication storage?

Storage-based replication, also known as data replication storage, is a method of replicating data available over a network to multiple distinct storage locations/regions.

2) Why does data replication matter?

Any loss of data, whether due to system failure, connectivity failure, or disaster, can result in significant losses. Companies opt for Data Replication to avoid these losses.

Data Replication facilitates large-scale data sharing among systems and distributes network load among multisite systems by making data available on multiple hosts or data centers.

3) What is the difference between data replication and data backup?

The act of copying and then moving data between a company’s sites is known as Data Replication. It is commonly measured in terms of Recovery Time Objective (RTO) and Recovery Point Objective (RPO) (RPO). It focuses on business continuity or the continued operation of mission-critical and customer-facing applications following a disaster.

Data backup continues to be the go-to solution for many industries that require long-term records for compliance and granular recovery.

Final Thoughts

This article teaches you in-depth about data replication, its benefits and use cases, and answers all your queries about it. It provides a brief introduction of numerous concepts related to data replication 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, are quite effort-intensive and require in-depth technical expertise. Implementing them can be challenging, especially for a beginner, and this is where Hevo saves the day!

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Hevo Data provides an Automated No-code Data Pipeline that empowers you to overcome the above-mentioned limitations. Hevo caters to 150+ data sources (including 40+ free sources) and can seamlessly perform Data Replication in real time. Hevo’s fault-tolerant architecture ensures a consistent and secure replication of your data. It will make your life easier and make data replication hassle-free.

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Sarthak Bhardwaj
Customer Experience Engineer, Hevo

Sarthak brings two years of expertise in JDBC, MongoDB, REST API, and AWS, playing a pivotal role in Hevo's triumph through adept problem-solving and superior issue management.

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