Prioritize real-time processing for time-sensitive data that requires immediate action. This includes user operations data, like low stock levels, stock price, which requires instant feedback or dynamic adjustments. Then, focus on security-related data, as identifying potential threats or breaches promptly is essential for maintaining system integrity. Monitoring critical system metrics in real-time ensures you can address performance issues swiftly, minimizing downtime and optimizing operations. For static or low-impact data, batch processing suffices. This approach allows resource allocation toward high-priority tasks, ensures essential data is processed efficiently while conserving resources on less time-sensitive data.

Deciding which data to process in real-time depends on the business requirements, the nature of the data, and their criticality. Considerations are: 1 Prioritise data that requires immediate action or decision making eg Fraud detection. 2 Any time sensitive analysis where delay in processing impacts the value of the data 3 Dynamic Data streams, data that needs to be processes as it arrives eg. Customer support chats Things that do not need Realtime: - 1 Historical Analysis 2 Low Frequency updates 3 Cost Sensitive Scenarios, what if data 4 Static data In reality we need to implement a combination. Use real-time for critical, time-sensitive data and batch for aggregated or historical analysis.

Data processing in real-time or near real-time is of paramount importance to enhance responsiveness and reliability. By way of illustration, operational metrics are monitored in real-time to detect system issues early, enabling swift interventions to maintain uptime. User interaction data, such as clicks and transactions, is processed in almost real-time, allowing personalized experiences on e-commerce or streaming platforms to be delivered. Security and fraud detection relies on real-time monitoring to address threats immediately. Additionally, almost real-time processing is crucial for customer support systems, ensuring rapid responses that improve user satisfaction and engagement.

Data that is used as an input to core business transactions in the applications need to be processed in Real Time. A simple example is of a Fraud Detection System at an banking organisation. All transactions happening on an account need to be processed and run through Fraud Detection algorithms in real time to be able to alert the Customer of a potential Fraud immediately and also system can take automated actions to remediate the situation.

As a Data Engineer, when handling large datasets, I prioritise processing data in real-time if it requires immediate action. This includes live user interactions, system alerts, or transactions that need instant responses. For example, in a project involving financial data, I set up real-time processing for transactions to detect and prevent fraud as it happens. Data that isn’t time-sensitive, like historical logs or reports, is processed in batches. By focusing on real-time processing for critical data, we ensure the system responds promptly when it matters most, hence using resources efficiently.