Carrefour’s approach combines no-code self-service tools with as-code automation, making it easy for both technical and non-technical users to adopt. This balance, along with incremental implementation and cultural emphasis on data quality, supports scalable observability across the organization.

The five technical pillars are freshness, volume, schema, distribution, and lineage. These cover everything from whether your data is arriving on time to whether it still follows expected patterns. A strong observability tool like Sifflet monitors all five, providing real-time metrics and context so you can quickly detect and resolve issues before they cause downstream chaos.

A strong data quality monitoring framework should be scalable, rule-based and powered by AI for anomaly detection. It should support multiple data sources and provide actionable insights, not just alerts. Tools that enable data drift detection, schema validation and real-time alerts can make a huge difference in maintaining data integrity across your pipelines.

Data observability helps organizations ensure data quality, monitor pipelines in real time, and build trust in their data. At Big Data LDN, we’ll share how companies like Penguin Random House use observability tools to improve data governance and drive better decisions.

Great question! Infrastructure tools like Datadog are excellent at monitoring system uptime, server health, and network performance, but they lack visibility into the actual content of your data. That means they can’t catch silent data issues like null values or schema changes that break downstream dashboards. That’s where a data observability platform like Sifflet comes in—it ensures your data is accurate, complete, and trustworthy, not just delivered on time.

Data lineage adds context to data observability by linking alerts to their root cause. For example, if a metric suddenly drops, lineage helps trace it back to a delayed ingestion or schema change. This speeds up incident resolution and strengthens anomaly detection. Platforms like Sifflet combine lineage with real-time metrics and data freshness checks to provide a complete view of pipeline health.

Subdomains are perfect for fast-growing teams, multi-regional operations, and enterprises with 200+ users. They’re especially valuable for organizations with customer-facing data products or strict compliance needs, where clear access rules and audit-ready controls are essential for effective data quality monitoring.

Data lineage tracking gives visibility into how data flows from source to destination, making it easier to pinpoint where issues originate. This is essential for root cause analysis, especially when dealing with complex integrations across multiple systems. At Sifflet, we see data lineage as a cornerstone of any observability platform.