How do you test data engineering solutions for high availability?

First of all, designing any system to be Fault-Tolerant is the key. 1. Identify the core components which are critical to the business to run and keep redundant copies of them in different global regions where business is operating. 2. Test the Disaster Recovery procedures to be ready for the worst scenarios 3. Test the architecture by bringing down few core components to see whether it impacts the whole application infrastructure. 4. Always design the system for failure

High availability (HA) is the ability of a system to operate continuously without failing for a designated period of time. HA works to ensure a system meets an agreed-upon operational performance level. In information technology (IT), a widely held but difficult-to-achieve standard of availability is known as five-nines availability, which means the system or product is available 99.999% of the time.

Testing Data engineering solutions for high availability involves multiple steps to ensure continuous, fault-tolerant operation. Initially, design and architecture are reviewed to confirm redundancy and failover capabilities. Load testing is performed under varying conditions to assess system performance and bottlenecks. Failover scenarios are simulated to validate automatic recovery and data consistency. Monitoring tools are employed to track real-time metrics like latency, throughput, and error rates. Stress tests push the system to its limits, assessing how it degrades under extreme conditions and recovers afterward. The objective is to identify weak points and implement improvements to ensure robust, uninterrupted service.

Designing high availability architecture also depends on the Recovery time objective (RTO) and Recovery point objective (RPO). Recovery time objective (RTO) refers to the amount of time that an application, system, and process can be down without causing significant damage to the business and the time spent restoring the application and its data to resume normal business operations after an incident. RPO helps determine how much data a company can tolerate losing during an unforeseen event. According to the above requirements and depending on the cost, active-active or active-passive architecture can be designed either to roll over or recover after some point in time.

A high available architecture is when there are a number of different components, modules, or services that work together to maintain optimal performance, irrespective of peak-time loads. In certain real-life scenarios, lives may depend on a database built for high availability. When a patient arrives in the emergency room, medical professionals need instant access to their medical health records to understand what treatment decisions are best. Any delay in accessing this information could have a devastating impact. To design HA we need to keep few of the below in our mind, like: 1. Achieve geographic redundancy 2. Use failover solutions 3. Implement load balancers

As you reach to the higher level of reliability, several things happen to the system. The cost of hardware rise due to increase in the server, network and disk redundancy. Identifying and eliminating the failures become highly difficult. In case of designing the high availability test cases like converting the faults occurred from fault modelling to actionable test cases. Categorising test cases into Component level fault test cases which is within the machine boundary System level fault test cases. Third party test cases. Infrastructure level and operational level fault test cases. It is not enough to conduct high availability testing during pre production environment

Approach testing results in the usual "Six sigma" method: identify bottlenecks, adapt solution, retest, repeat. It's important to also document the limits of high availability: what scenario might break your system? What are the chances of it happening? How much does it cost to remedy? Does remediation make sense based on criticality and probability?