Characteristics

Discoverability: The ability of the system to make operational data available for consumption. This involves discovering multiple disparate types of data available within an application that can be used for various ad hoc explorations.

Connectivity: Operational data can emanate from a variety of data sources in different format with disparate volumes. For this reason, the operational system has to provide the capability to seamlessly integrate all the data with the least overhead for production application.

Scalability: The ability of the system to scale up and out to adapt to changes in the operational analytics workload in terms of storage or compute requirements.

Monitoring: You should be able to continuously monitor the operational system performance and get notified about the resource utilization and the overall health of your system.

Security: The access to the operational system must be secure. With Amazon OpenSearch Service, you can configure the domain to be accessible with an endpoint within your VPC or a public endpoint accessible to the internet. In addition to network-based access control, you must set up user authentication and authorization to secure the access to data based on business requirements. OpenSearch Service supports encryption at rest and in Transit.

Data durability: With operational analytics, the use cases differ as to the retention requirements. You should understand your business requirements in terms of analyzing historical data. With Amazon OpenSearch Service, you can retain more data with less cost using the UltraWarm and cold storage tiers.

Automation: The data lifecycle in your operational system should be automated in order to easily onboard new data pipelines and reduce the overhead of managing the lifecycle of the data. With Index State Management (ISM) in Amazon OpenSearch Service, you can create your own policies to automate the lifecycle management of indices stored in the service.

  Observability: The ability to understand internal state from the various signal outputs in the system. By providing a holistic view of these various signals along with a meaningful inference it becomes easy understand how healthy and well performant the overall system is.

User centricity: Each analytics application should address a well-defined operational scope and solve a particular problem at hand. Users of the system often won’t understand or care about the analytics process but only see the value the result.  

Agility: The system must be flexible enough to accommodate changing needs of an analytics application and offer necessary control to bring in additional data with low overhead.