Monitoring transformation errors is key to maintaining healthy pipelines. Using a data observability platform allows you to implement real-time alerts, root cause analysis, and data validation rules. These features help catch issues early, reduce error propagation, and ensure that your analytics and business decisions are based on trustworthy data.

Forge doesn’t take action on its own, but it does provide smart, contextual guidance based on past fixes. It helps teams resolve issues faster while keeping you in full control of the resolution process, which is key for maintaining SLA compliance and data quality monitoring.

At Etam, every data project begins with the question, 'How does this help us reach our OKRs?' This alignment ensures that data initiatives are directly tied to business impact, improving sponsorship and fostering collaboration across departments. It's a great example of business-aligned data strategy in action.

The right technology can make a big difference. Adopting observability tools that offer features like data lineage tracking, data freshness checks, and pipeline health dashboards can reduce manual firefighting and help your team work more autonomously. This not only improves productivity but also makes day-to-day work more enjoyable.

No worries at all! With Sifflet’s Universal Connector API, you can integrate data from virtually any source. This flexibility means you can monitor your entire data ecosystem and maintain full visibility into your data pipeline monitoring, no matter what tools you're using.

Great question! Data freshness is a key part of data reliability because decisions are only as good as the data they're based on. If your data is outdated or delayed, it can lead to flawed insights and missed opportunities. That's why data freshness checks are a foundational element of any strong data observability strategy.

Data lineage tracking is essential for understanding how data flows from storage to dashboards. When something breaks, Sifflet helps you trace it back to the storage layer, whether it's a corrupted file in S3 or a schema drift in MongoDB. This visibility is critical for root cause analysis and ensuring data reliability across your pipelines.

Yes, Sifflet is designed to be accessible for both business stakeholders and data engineers. It offers intuitive interfaces for no-code monitor creation, context-rich alerts, and field-level data lineage tracking. This democratizes data quality monitoring and helps teams across the organization stay aligned on data health and pipeline performance.