Break free from on-premise database limitations

Free yourself from the limitations of on-premise databases

When customers consider moving to the cloud, they face a major challenge: what to do with legacy systems. As a result, the best option is to migrate to a more cloud-native architecture. At the same time, you can consider moving from a general-purpose Database Management System (DBMS) to a special-purpose one.

In general, the current DBMS most used by on-premise businesses were designed to be used in two kinds of use cases: online transaction processing and business intelligence statistics.

However, new classes of applications are emerging that require database support. These include customer experience applications focused on “edge data”, deep and recursive data analysis that is not very compatible with SQL, support for artificial intelligence (AI) and machine learning (ML), analysis of complex relationships with data graphics, and streaming data capture and event management in real time. These new workloads require customized databases that have better scalability and performance and that are compatible with a more agile development process.

These models and workloads can lead to a revolution in companies and their processes, with better intelligence and automation, faster market access and smarter decisions within the organization and at the edge.

To take advantage of everything the cloud has to offer, you also need dynamic scalability and agility in the provisioning of processing or storage resources. Such provisioning must also deliver reliable performance and the means to innovate faster through integration with the platform.

Amazon Web Services and Compucloud offer a wide range of database management technologies that, together, address all of the requirements described above. The goal is to provide a transformative movement that AWS calls “refactoring”.

The AWS DBMS that refactor your database workloads

The main technologies in question include the following:

• Amazon Aurora. This managed relational DBMS is compatible with MySQL and PostgreSQL, provides the full capabilities of an enterprise RDBMS, and is optimized for performance, scalability and high availability beyond what is possible with open source relational database technologies.

• Amazon Redshift. This DBMS is designed for statistical workloads and has a columnar architecture that stores data efficiently for high query performance.

• Amazon DynamoDB. This fully managed NoSQL database service offers fast and predictable performance as well as optimal scalability.

In the past, companies had complex data integration solutions that limited their options for new uses of data and new types of data. This must change. AI/ML-based workloads, transmission data (including Internet of Things (IoT) data), time series analysis, and other types of data and analysis must be integrated and managed together. These technologies are necessary to address new business opportunities in areas such as supply chain optimization, a better overall customer experience driven by artificial intelligence, dynamic pricing, and logistics optimization.

Companies are either in the process of migrating to the cloud or in the middle of planning to move to the cloud. The reasons have to do with increased efficiency, simplification of operations and overall savings in the areas of hardware, software and staff time. The biggest challenge has to do with choosing a migration objective and calculating the costs and risks associated with the migration.

Source of information: Carl W. Olofson. (September 2020). How to free yourself from installation restrictions: AWS cloud database services. International Data Corporation (IDC), #US46773920, 10. October 2021, From ©2020 IDC Database.