Containers offer multiple advantages to a reporting server thanks to how they maximize hardware to release new versions of an application. A Docker database container is like a virtual machine, but it runs the software and any associated system files at once. Rather than executing multiple instances of the OS and required files, a container simply executes the application.
The combination of Docker and databases offers a few key benefits for business intelligence (BI) platforms. For one, each can scale independent of another, but containers offer faster data provisioning and superior testing capabilities as well.
Fast Provisioning
Requesting a new report is a time-consuming process, but the data is a necessity as soon as possible. Enter the Docker database, an alternative to traditional data storage and provisioning. Container provisioning relies on stackable image layers and a container layer. Thus, each new layer is a new version built on existing processes.
Another way to think about this is micromanaging a group of processes, where users can make incremental changes to something specific without affecting the entire application. Users can configure based on their needs, and then push the changes live once that work is confirmed.
Deployment time is much shorter with the Docker container database. Changes are applied only to the previous versions affected, with no additional turnaround documenting and waiting for a request.
Testing with Containers
To create a flexible container, the build must go through testing before deployment. One of the Docker and databases advantages in testing is the ability to create and tear down an instance, without affecting the application at large. Containers offer an alternative to traditional testing, which relies on a clean OS within a virtual machine that performs a certain way.
The existing container manages all dependencies, in essence creating a production-like environment. Launch the application and then test.
This flexibility also processes large datasets using the functionality that was already built, and makes adjustments in real-time, without reexamining base configurations. It’s also possible to run multiple tests simultaneously, which allows for more exploratory testing to examine user experience or application performance.
Scaling with Containers
The modular construction of the modern SaaS application allows for additional functionality as needed. Core functionality is preserved, even as new containers are spawned to handle the additional load.
How these containers scale comes down to the kind of services they handle. In a parallel scale, services don’t need to coordinate with one another. When they require other services to function, the service can scale sequentially.
For BI platforms, a modular approach translates to greater functionality over time. Services can build out depending on the needs of the customer base, offering specific functionality as the service and customer base grows, rather than rebuilding the application over time.
Even as certain aspects fall out of use, those modules can be deprecated without affecting the ecosystem at large.
Working with large datasets requires the most efficient possible methods for testing and deploying new approaches to these challenges. Containers allow a modular approach to data management, one that grows and scales as needed with efficiency in mind every step of the way.
