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Difference between Parallel and Distributed databases

Last Updated : 18 Sep, 2024
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Parallel and Distributed Databases are significant components within an enormous quantity of big data processing since they help to enhance data handling in terms of efficiency and expandability. Nonetheless, there is sometimes confusion between them because both are aimed at achieving the most effective solution to the organization’s handling of its data. This article lays out the definition of parallel and distributed databases, their functionality, and pros and cons to give you an insight into what system is more desirable for use in certain cases.

What is Parallel Database?

A parallel DBMS is a DBMS that runs across multiple processors and is designed to execute operations in parallel, whenever possible. The parallel DBMS links several smaller machines to achieve the same throughput as expected from a single large machine.

Features

  • There are parallel working of CPUs
  • It improves performance
  • It divides large tasks into various other tasks
  • Completes work very quickly

Advantages of Parallel Databases

  • Increased Speed and Efficiency: That is why the application of parallel databases allows executing several queries at the same time and consequently, decreases the total amount of time needed for processing them.
  • High Throughput: They perform a large number of computations in less time more than the sequential methods hence suitable for large-scale uses.

Disadvantages of Parallel Databases

  • Complexity in Maintenance: Indeed, employment of parallel databases might be slightly problematic as synchronization and some other components of the processes are worthy of special attention.
  • Higher Costs: While parallel databases may be defined with robust physical databases which require intensive hardware the databases may be more costly in setting up and even in operation.

What is Distributed Database?

A Distributed database is defined as a logically related collection of data that is shared which is physically distributed over a computer network on different sites. The Distributed DBMS is defined as, the software that allows for the management of the distributed database and makes the distributed data available for the users.

Features

  1. It is a group of logically related shared data
  2. The data gets split into various fragments
  3. There may be a replication of fragments
  4. The sites are linked by a communication network

The main difference between the parallel and distributed databases is that the former is tightly coupled and then later loosely coupled.

Advantages of Distributed Databases

  • Fault Tolerance: Since the data is distributed to various centers, failure in one center does not result to the total failure of the system.
  • Scalability: Due to the ability of distributed databases, it can easily be scaled up by adding nodes to the network which makes it ideal for businesses that are expanding.
  • Local Autonomy: Every site can administer a separate data base, however they are all in the collective database at the same time.

Disadvantages of Distributed Databases

  • Complex Data Management: Sharing data with multiple facilities presents some problems like latency of data.
  • Security Concerns: The more the places there is data stored the higher the risk of breaches hence the need to enforce very strict security measures.

Difference Between Parallel and Distributed Databases

Parallel Database

Distributed Database
In parallel databases, processes are tightly coupled and constitutes a single database system  i.e., the parallel database is a centralized database and data reside in a single location In distributed databases, the sites are loosely coupled and share no physical components i.e., distributed database is our geographically departed, and data are distributed at several locations.
 In parallel databases, query processing and transaction is complicated. In distributed databases, query processing and transaction is more complicated.
In parallel databases, it’s not applicable. In distributed databases, a local and global transaction can be transformed into distributed database systems
In parallel databases, the data is partitioned among various disks so that it can be retrieved faster. In distributed databases, each site preserve a local database system for faster processing due to the slow interconnection between sites
In parallel databases, there are 3 types of architecture: shared memory, shared disk, and shared shared-nothing. Distributed databases are generally a kind of shared-nothing architecture
In parallel databases, query optimization is more complicated. In distributed databases, query Optimisation techniques may be different at different sites and are easy to maintain
In parallel databases, data is generally not copied. In distributed databases, data is replicated at any number of sites to improve the performance of systems
Parallel databases are generally homogeneous in nature Distributed databases may be homogeneous or heterogeneous in nature.
Skew is the major issue with the increasing degree of parallelism in parallel databases. Blocking due to site failure and transparency are the major issues in distributed databases. 

Conclusion

Parallel and distributed databases are very effective when it comes to large quantities of data however they are used for different aim. Parallel databases are well suited when increasing the response time of queries to be processed in the same system whereas distributed databases offer better reliability and capability of expanding throughout one or more locations.

Depending on what you want to achieve—whether it is faster query or more scalable and fail proof system then you can go for what ever form of database architecture that will suit you best.



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