What are MPP Systems? Benefits, Types and Examples

What are MPP Systems

What are MPP Systems? Benefits, Types and Examples

What are MPP Systems

Written by Imran Abdul Rauf

Content Writer

April 17, 2022

The amount of data gathered, stored, and used reached a staggering more than 64 zettabytes in 2020. And by the end of 2021, industry experts predicted the figure to grow by 12.5%, i.e., reach 72 zettabytes. Companies collect massive amounts of data for business decisions and consumer operations. That’s why massively parallel processing (MPP) systems cater to the demand for ever-growing storage capacity and computing capability to process big data.

What is Massively Parallel Processing (MPP)?

MPP is the collaborative processing of a program using two or more processors, and using different processors allows the system to perform at higher speeds. Computers running the processing nodes are independent and don’t share a memory. Instead, each processor covers a different part of the data program with its operating system.

Organizations often employ a messaging interface that enables MPP systems to serve thread handling. The process facilitates quick analytics for business intelligence and bulks of data volume. The MPP architecture permits processors to communicate and share relevant information. Large datasets in data warehouses associate independent processing nodes together.

What is an MPP Database?

An MPP database is a data warehouse where processing activities are split between different nodes and servers. A leader node carries the communication with each individual node. The computer nodes handle the requested processes by dividing the work into smaller, manageable tasks and unit chunks.

The MPP process can scale horizontally by incorporating additional computer nodes, instead of additional servers for scaling vertically. Teams can sift and sort the data faster and respond to the queries if more processors are attached to the data warehouse and MPP databases. In doing so, this diminished the need for extended time intervals needed for complex searches on large datasets.

Data warehouse appliances, normally used to acquire in-depth insights and big data analyses, merge MPP architecture into the database to achieve a higher performance and platform scalability.

What are MPP Databases Used For?

As we stated earlier, organizations collect huge bulks of data. And using a single server without sufficient computing power to manage processing in an operating system makes the stored data expensive to handle and doesn’t produce the expected results. Although there are different approaches to handling this problem, companies often use MPP as an essential tool in their storage infrastructure.

Components that use independent nodes with their own operating systems to create an efficient model, for instance, many people in the company can run queries in a data warehouse simultaneously without incurring lengthy response times. MPP databases are also extensively used for centralizing large amounts of data in data warehouses. IT personnel and teams use the centralized data at different locations to access the same data assets.

The activities and jobs of IT teams stem down from a single source of truth and not data silos. The purpose is to ensure all entrusted parties can use the most updated data.

Departmental collaboration and usage

At times two or more company departments require alignment. For example, if the accounts and finance departments use the same data sets, finance can develop better synergy to support the accounts team.

Moreover, the finance department can do better forecasting through the pending data provided by the sales team. Similarly, operations, HR, logistics, and technical support business units benefit from a central repository and quick processing.


Symmetric multiprocessor (SMP) systems share memory, software, and I/O resources and usually use one CPU to manage database requests. The SMP databases can run on multiple servers and share resources in cluster configurations. The major difference between SMP and MPP is the system design. An MPP system has its dedicated resources and shares nothing, while the SMP counterpart shares the same resources.

The other key difference is the massively factor. As you can use hundreds of processors in an MPP system and each processor has its own operating system and memory, IT personnel can handle loads of data in parallel. Moreover, SMP systems are known to deliver low returns. Each processor does enable faster synchronization across the system, but it also has its own cache.

This results in cache-based and bandwidth issues that arise when multiple processors to the same operating system and resources are added. These memory and resource limitations give MPP systems an edge and more scalability capability over SMP.

Examples of MPP Systems

There are two types of MPP database architecture: grid computing and computer clustering.

Grid computing

Users can employ multiple computers across a distributed network through grid computing. One significant way grid computing benefits businesses is that resources are used when available. Grid computing reduces hardware spend and server space and restricts bandwidth usage when used for other jobs or multiple requests are processed simultaneously.

Computer clustering

Computer clustering links the nodes which have the ability the handle multiple tasks at the same time by communicating with each other. Therefore, the more nodes are associated with the MPP system, the faster the queries will be answered.

  • Processing nodes: Processing nodes are the fundamental components of MPP. Nodes could be a desktop PC, server, virtual server, etc., or simple processing codes with multiple processing units installed.
  • High speed interconnect: An MPP system normally breaks down queries into parts which are later dispersed to nodes. Each node will perform its task individually in the parallel processing system. The process requires a high bandwidth connection and centralized communication between nodes. Ethernet or any distributed fiber data interface typically handles this high-speed interconnection.
  • Distributed lock manager: Distributed lock manager enables resource sharing when nodes share disk space among them. Nodes will send resource requests to the DLM and associate them when resources are available. The DLM also assists in handling nodes failures regarding data inconsistency and recovery issues.

Popular MPP Systems Used by Businesses

  • BigQuery: Google’s BigQuery is a low-cost, fully managed enterprise data warehouse for analytics. However, the platform is serverless and doesn’t provide an infrastructure to manage. Moreover, you don’t need a database administrator that allows personnel to emphasize analyzing data and extracting valuable insights through familiar SQL.
  • Snowflake: Snowflake provides the Data Cloud, a globally distributed network where thousands of companies deploy data with almost limitless scale, concurrency, and performance. Inside the Data Cloud ecosystem, businesses put together their siloed data, discover and efficiently secure governed data, and implement a variety of analytics workloads. Regardless of the users and data location, Snowflake will provide a quality experience across multiple public clouds.
  • Synapse: Azure Synapse Analytics is a cloud-based enterprise data warehouse that uses MPP systems to run complex queries across petabytes of data quickly. The platform uses an MPP architecture to manage analytical workloads and aggregate vast amounts of data. Unlike transactional databases, which stores rows in a table, MPP databases store each column as an object.
  • Amazon Redshift: Amazon Redshift is a petascale data warehouse that manages analytics workloads, especially for large data sets like BI and OLAP applications. The platform has a massively parallel processing architecture and columnar storage structure to handle complex analytical queries seamlessly.


Massively parallel processing systems can unlock the power of your business data and produce deeper analysis for big data activities. If you like to learn more about cloud and big data analytics services that may work in conjunction with MPP systems, talk to our data analytics experts at Royal Cyber.

Are you looking for an MPP Database?

Recent Blogs

  • Revolutionizing Customer Support with Salesforce Einstein GPT for Service Cloud
    Harness the power of AI with Salesforce Einstein GPT for Service Cloud. Unlock innovative ways …Read More »
  • Salesforce Hyperforce: A Deep Dive into the Future of Cloud Deployment
    Discover Salesforce Hyperforce, the future of cloud deployment. Explore its scalability, security, and global reach, …Read More »
  • LLMs in Retail: Which Operations Can You Transform With AI?
    Artificial Intelligence (AI) has been making significant waves across various industries, revolutionizing business operations.Read More »