Static
Currently, alerts are set with static thresholds or conditions. Keeping these thresholds or conditions up to date requires regular input from business stakeholders and involvement of data teams. That’s an additional maintenance activity. On top of it, the threshold needs to be temporarily tweaked for some business use cases. For instance, a retail company can expect traffic spikes during the holiday season. This static nature of alerts can lead to false alarms that waste the support team’s time or to missed issues that impact the business.
Scattered
The current system notifies about failures, but without any context. The on-call person then has to check logs, understand the issue, and inform the respective team. With complex workflows, logs are scattered across servers, cloud platforms, log management systems, and scheduling tools. Manually browsing through each takes time. Take Sourcegraph, for example. Their support team was spending from 45 to 90 minutes just to check logs for each issue before they started using AI. The scattered nature of pipeline logs and telemetry data increases the on-call person’s work and delays the resolution of issues.
How AI will shift data pipeline monitoring from reactive to proactive
AI models bring together your scattered historical logs and metrics across platforms and summarize only key information, such as:
