Data Warehouse — Separation of Cloud and Storage

Data Warehouse — Separation of Cloud and Storage

There have been greater demands for cloud data warehouses to separate computing and storage. Some databases are still in the Cloud Hosting stage, just migrating the original architecture to the cloud host, using cloud resources.

There have been greater demands for cloud data warehouses to separate computing and storage. Some databases are still in the Cloud Hosting stage, just migrating the original architecture to the cloud host, using cloud resources. Some databases have entered the Cloud Native stage, based on the infrastructure of the IAAS layer of the cloud platform, to build flexibility, serverless, data sharing and other capabilities.

The advantage of separating storage and computing resources is that you can independently plan storage and computing resource specifications and capacities. In this way, the expansion, reduction, and release of computing resources can be completed relatively quickly, and will not bring additional data relocation costs. Storage and computing can also better combine their respective characteristics and choose resource specifications and designs that are more suitable for them.

Let’s take a look at Alibaba Cloud’s native data warehouse AnalyticDB MySQL as it enhances its flexibility in the past few years.

AnalyticDB was originally built based on the traditional Massive Parallel Processing (MPP) architecture like AWS RedShift. Recently, AnalyticDB launched a flexible model with a separate architecture for computing and storage. The AnalyticDB elastic model is divided into access layer, computing layer, and storage layer. The access layer is compatible with the MySQL protocol, including access control, optimizer, metadata, query scheduling and other modules, responsible for real-time data writing and query.

data-warehouse cloud storage

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