In the age of digital products and increasing web traffic, running a laborious Backup procedure every minute may not be the best option. Here’s where Data Replication can help save your day! Data Replication not only lowers the chance of disasters and data loss but also boosts the solution’s performance.

Data Availability is a crucial component in Distributed Systems, and Data Replication can address it. The replicas can be spread globally and brought closer to the end customers, thereby decreasing latency and increasing customer satisfaction. Not only this, read queries can be served more quickly, and Distributed Systems become more fault-tolerant. If one of the system’s components fails to answer, the request is sent to another machine to get the right response back.

This guide will walk you through the Data Transactions and Replication in Distributed Sytems. You will gain a deep understanding of Distributed Systems and why you need them. In addition, you will learn about Distributed Transactions and understand their importance in Distributed Systems. 

In this guide, you will also discover the need for Data Replication in Distributed Systems, explore the different types of Data Replication, and unravel the various benefits offered by Data Replication in Distributed Systems. So, let’s dive deeper into the world of Distributed Systems and various Data Transaction & Replication techniques.

Table of Contents

What is a Distributed System?

A Distributed System, also sometimes referred to as Distributed Computing, is a system having many components scattered over several machines that communicate and coordinate operations that appear to the end-user as a single coherent system.

Distributed Systems are a significant advancement in IT and computer science since an increasing number of connected and related tasks are becoming too complex and hard for a single computer to complete.

Why do You Need Distributed Systems?

There are a variety of reasons why teams choose to deploy Distributed Systems. Here are a few explanations:

  • Horizontal Scalability: Since computation takes place separately on each node, adding more nodes and functionality as needed is simple and cost-effective.
  • Reliability: Since Distributed Systems can be built up of hundreds of nodes that work together, most of them are fault-tolerant. If a single machine fails, the system usually does not suffer any disruptions.
  • Performance: As workload can be divided up and transferred to various machines, distributed systems are tremendously efficient.
  • Heterogeneity: The nodes and components in most distributed systems are generally asynchronous, with various hardware, middleware, software, and operating systems. This makes it possible to expand distributed systems by adding additional components.
  • Replication: Distributed systems allow sharing of information and communications. Data Replication in Distributed Systems enhances fault tolerance, reliability, and accessibility by maintaining consistency amongst redundant resources such as software and hardware components.

Example of a Distributed System

Distributed System architecture can be leveraged in distributed databases, distributed file systems, distributed messaging systems, and others. The most well-known example of a Distributed System is the Internet. The Internet allows many distinct computer systems in many geographical places to connect and exchange information and resources.

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What are Data Transactions in Distributed Systems?

A Distributed Transaction is made up of one or more statements that update data on two or more separate nodes in a distributed database, either individually or collectively. The processing requirements for Distributed Transaction are the same as for conventional database transactions, but they must be handled across various resources, making them more difficult to implement for Database Developers.

As with any other transaction, Distributed Transactions must follow all 4 ACID properties(atomicity, consistency, isolation, and durability). Read more about ACID Transactions here.

The 2 Phase Commit (2PC) is a typical approach for ensuring the accurate completion of a Distributed Transaction. This technique is typically used for modifications that may commit in a short amount of time, such as a few milliseconds to a few minutes.

Why do You Need Data Transactions in Distributed Systems?

When you need to swiftly update related data that is dispersed across different databases, Distributed Transactions are the key. If you have numerous systems that maintain customer information and need to make a universal modification (such as changing the country) across all records, a Distributed Transaction will ensure that all records are updated. 

If there is a failure in the transaction, the data is returned to its previous state, and the originating application is responsible for resubmitting the transaction.

Due to the sheer volume of incoming data, Distributed Transactions are extremely important in data streaming systems today. Even a brief failure of one of the resources might result in a significant volume of data being lost.

Refer to Understanding Transactions in Database: 5 Important Points to read more about Data Transactions.

What is Data Replication in Distributed Systems?

Data Replication is the process of generating numerous copies of data. You then store these copies also called replicas in various locations for backup, fault tolerance, and improved overall network accessibility. The data replicates can be stored on on-site and off-site servers, as well as cloud-based hosts, or all within the same system.

Data Replication in Distributed Systems refers to the distribution of data from a source server to other servers while keeping the data updated and synced with the source so that users can access data relevant to their activities without interfering with the work of others.

Why do You Need Data Replication in Distributed Systems?

Data in a Distributed System is stored among several computers in a network. Some of the  reasons for Data Replication in Distributed Systems include:

  • Higher Availability: In Distributed Systems, Replication is the most important aspect of increasing data availability. Data is replicated over numerous locations so that the user can access it even if some of the copies are unavailable due to site failures. 
  • Reduced Latency: By keeping data geographically closer to a consumer, Replication helps to reduce data query latency. For example, CDNs (Content Delivery Networks) such as Netflix, retain a copy of duplicated data closer to the user.
  • Read Scalability: Read queries can be served from copies of the same data that have been replicated. This increases the overall throughput of queries.
  • Fault-Tolerant: Even when there are network challenges, the system operates. If one replica fails, service can be supplied by another replica.

What are the Types of Data Replication in Distributed System?

There are several types of Data Replication in Distributed System based on certain types of architecture. Let’s look at some of these below:

Asynchronous vs Synchronous Replication

  • Asynchronous Replication: In this replication, the replica gets modified after the commit(save) is fired onto the database.
  • Synchronous Replication: In this replication, the replica gets modified immediately after some changes are made in the relation table.

Active vs Passive Replication

  • Active Replication: Active Replication, is a non-centralized replication mechanism. The central idea is that all replicas receive and process the same set of client requests.
    • Consistency is ensured by assuming that replicas will generate the same output when given the same input in the same sequence. This assumption indicates that servers respond to queries in a deterministic manner.
    • Clients do not address a single server, but rather a group of servers.
    • Client requests can be broadcast to servers via an Atomic Broadcast for them to get the same input in the same sequence.
  • Passive Replication: Client requests are processed by just one server (named primary) in Passive Replication.
    • The primary server changes the status of the other (backup) servers after processing a request and responds to the client.
    • One of the backup servers takes over if the primary server fails. Even non-deterministic processes can benefit from Passive Replication.
    • The drawback of Passive Replication over Active Replication is that the response is delayed in the event of failure.

Based on Server Model

  • Single Leader Architecture: In this architecture, one server accepts client writes and replicas pull data from it. This is the most popular and traditional way. It’s the synchronous technique, but it’s also quite rigid.
  • Multi Leader Architecture: In this architecture, multiple servers can accept writes and serve as a model for replicas. To avoid delay, copies should be spread out and leaders should be near all of them.
  • No Leader Architecture: Every server in this architecture can receive writes and function as a replica model. While it provides maximum flexibility, it makes synchronization difficult.

Replication Schemes

  • Full Data Replication: Full Replication refers to the replication of the whole database across all Distributed System sites.
    • Across a wide area network, this technique maximizes data availability and redundancy.
    • Since the results can be accessed from any local server, Full Replication speeds up the execution of global queries.
    • The drawback of Full Replication is that the updating process is often sluggish. This makes maintaining current data copies in all locations challenging.
  • Partial Data Replication: Here, only selected parts of the database are replicated based on the significance of data at each site.
    • The number of copies, in this case, can be anything from one to the total number of nodes in the Distributed System.
    • This kind of replication can be effective for members of Sales and Marketing teams where a Partial Database is maintained on personal computers and synchronized with the main server regularly.
  • No Replication: In this Replication scheme, each node in a Distributed System receives just a copy of one section of the database.
    • While the No Replication can be ascribed to the simplicity of data recovery, it might slow down query execution since several users access the same server.
    • No Data Replication in DBMS gives low data availability when compared to alternative Replication techniques.

Also Read: Types of Data Replication

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  • Built-in Connectors: Support for 150+ Data Sources, including Databases, SaaS Platforms, Files & More. Native Webhooks & REST API Connector available for Custom Sources.
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  • Live Support: The Hevo team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.

What are the Different Types of Replication Models in Distributed Systems?

There are 3 frequently used Data Replication models in Distributed Systems, each with its own set of features and performance. These are displayed in the image below:

Advantages of Data Replication in Distributed Systems

Some of the benefits offered by Data Replication in Distributed Database or Systems are:

  • Ensures Business Continuity: Data Replication in Distributed Systems as part of your disaster recovery strategy guarantees that there is an off-site replica of the system in the event of hardware failure or a ransomware attack. This allows businesses to recover data while maintaining business continuity.
  • Increased Availability: A Distributed Database allows several users to view and manage data without interfering with one another.
  • Enhanced Performance: Since the same data is stored in various places, users can access information from the server closest to them, lowering network latency and improving speed.
  • Allows Multiple User Access: Data Replication aids query execution, especially when numerous users access the database.
  • Improve Analytics: A team can do Analytics without compromising performance by having a separate, complete copy of a Database.

Disadvantages of Data Replication in Distributed Systems

Data Replication in Distributed Systems can pose several challenges as discussed below:

  • It can take up a lot of storage space, especially when doing full replications. If several copies need to be updated at the same time, this might result in significant financial costs or reduced performance.
  • When employing merge or peer-to-peer replication, maintaining data consistency can be challenging.
  • Different sources might be out of sync with each other due to incorrect or out-of-date Replication. This might result in unnecessary Data Warehouse expenditures spent processing and keeping useless data.
  • There are maintenance and other costs associated with running several servers. These costs must be covered by either the organization or a third party. If a third party handles them, the company risks vendor lock-in or service concerns outside its control.


In a nutshell, in this guide, you read about Distributed Systems, Distributed Transactions, and Data Replication in Distributed Systems. You also explored the need for all these systems and techniques. In addition, you explored the different types of Replication in Distributed Systems. At the end of this post, you explored the various benefits and challenges associated with Data Replication in Distributed Systems.

However, businesses today are confronted with more diverse and sophisticated data sets than they have ever been before. As a result, organizations can no longer manage their data only through simple Data Replication processes. Most businesses now employ a range of automatic processing methods to stay competitive. This is where a simple solution like Hevo might come in handy!

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Share your experience with Data Transactions & Replication in Distributed Systems in the comments section below!

Shubhnoor Gill
Research Analyst, Hevo Data

Shubhnoor is a data analyst with a proven track record of translating data insights into actionable marketing strategies. She leverages her expertise in market research and product development, honed through experience across diverse industries and at Hevo Data. Currently pursuing a Master of Management in Artificial Intelligence, Shubhnoor is a dedicated learner who stays at the forefront of data-driven marketing trends. Her data-backed content empowers readers to make informed decisions and achieve real-world results.

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