Azure Synapse is a cloud-based analytics service that provides businesses with a unified experience for ingesting, preparing, managing and presenting data for their business intelligence and machine learning needs. One of the key features of Azure Synapse is its MPP (Massively Parallel Processing) architecture, which helps organizations process large datasets quickly and efficiently.
What is MPP Architecture?
MPP is a distributed computing architecture that includes multiple processors working together to process large amounts of data. In an MPP system, data is split into smaller parts and processed in parallel across multiple nodes. Each node has its own processor, memory, and storage resources. As a result, MPP systems can process large data sets much faster than traditional single node systems.
How does the MPP Architecture work in Azure Synapse?
Azure Synapse uses an MPP architecture to provide fast and scalable analytical processing. Architecture consists of two basic components:
Azure Synapse’s compute layer consists of multiple compute nodes, each with its own processor, memory, and storage resources. Compute nodes work together to process queries and data in parallel. This means that queries can be executed on multiple nodes simultaneously, resulting in faster processing times.
Azure Synapse’s storage tier is based on Azure Data Lake Storage, which provides highly scalable and durable storage for big data workloads. The storage tier is designed to handle petabyte-scale data volumes, making it ideal for MPP processing.
Advantages of MPP Architecture in Azure Synapse
There are several advantages to using the MPP architecture in Azure Synapse, such as:
Faster processing times
MPP systems can process large data sets much faster than traditional single node systems. This means businesses can gain insights from their data much faster, leading to better decisions and improved business outcomes.
MPP systems are highly scalable, which means they can grow as your business grows. As your data volumes increase, Azure Synapse can add more compute nodes to meet additional processing needs.
MPP systems can be more cost effective than traditional single node systems. This is because MPP systems can process large data sets in less time; this means you need fewer resources to achieve the same level of processing.
MPP vs SMP
We mentioned that MPP is a distributed computing architecture that includes multiple processors working together to process large amounts of data; SMP (Symmetric Multiprocessing) is a shared memory architecture where multiple processors access the same memory.
Advantages of MPP over SMP
- Scalability: MPP systems are highly scalable and can grow as your business grows. On the other hand, SMP systems have a limited number of processors that can be added, which makes them less scalable.
- Faster processing times: MPP systems can process large data sets much faster than SMP systems, as they can divide data into smaller parts and process them in parallel across multiple nodes. In contrast, SMP systems share a single memory which can become a bottleneck during operation.
- Cost-effective: MPP systems can be more cost-effective than SMP systems as they can process large datasets in less time, meaning you need fewer resources to achieve the same level of processing.
Advantages of SMP over MPP
- Simplicity: SMP systems are easier to install and maintain as they have a shared memory and do not require complex network connections between nodes.
- Lower latency: Since SMP systems have a common memory, they can access data faster than MPP systems that need to transfer data between nodes.
In summary, MPP is generally preferred for processing large datasets that require high scalability and fast processing times, while SMP is preferred for simpler workloads that require lower latency and easier maintenance.
The MPP architecture is a key feature of Azure Synapse, providing fast and scalable analytical processing for businesses of all sizes. Businesses can process large data sets quickly and efficiently using the MPP architecture, resulting in better decision making and improved business outcomes.