Situation
Our Fortune 500 client’s travel platform is used daily by millions of customers and travel partners. The health and reliability of this data is crucial to providing a seamless experience to travelers when booking flights, checking hotel availability, and more. However, a complex data ecosystem with thousands of data pipelines across multiple business units had visibility gaps. Frequent undetected pipeline failures and delays affected downstream reporting and decision-making.
Our client’s data observability initiative is an anchor for enterprise-wide data reliability at the company.
In addition, the absence of a centralized observability layer made it difficult to proactively identify and resolve issues, impacting data governance and SLA adherence.
Action
We implemented a comprehensive data observability framework using Datadog to monitor data pipeline health, job statuses, and SLA compliance across environments. Key features included setting up customized AI-ML based monitors, Service Level Objectives (SLOs), and health dashboards. Datadog Job Monitor and Data Stream Monitoring were implemented for real-time insights, which empowered our client’s enterprise data management team with centralized dashboards and health metrics.
To improve incident response, alerting workflows were enabled and integrated with Slack and Jira to reduce Mean Time to Repair (MTTR) and improve incident response. As a result of this approach, data-related incident escalations have decreased by 40%.
Our client’s data observability initiative has become a foundational pillar for enterprise-wide data reliability at the company. Our engagement supports multiple compliance frameworks and is aligned with broader enterprise data governance goals. Future expansion includes AI-driven anomaly detection and integration with broader observability platforms.
Results
- Improved real-time visibility into data pipeline health and job performance.
- Reduced data downtime through proactive alerts and resolution.
- Enhanced data governance and SLA tracking.
- Streamlined incident management via automated workflows.
The data observability framework monitors data pipeline health across environments.
Data-related incident escalations have decreased by 40%, due to our approach.
The health and reliability of data is key to providing a seamless travel experience.
The data observability framework monitors data pipeline health across environments.
Data-related incident escalations have decreased by 40%, due to our approach.
The health and reliability of data is key to providing a seamless travel experience.
The data observability framework monitors data pipeline health across environments.
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80%
Automation of monitoring and alerting setup -
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70%
Reduction in time to identify data pipeline failures -
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60%
Improvement in SLA compliance monitoring