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.

If you're just starting out, look for someone with 'Full Data Stack' capabilities, like a Data Analyst with strong SQL and business acumen or a Data Engineer with analytics skills. This person can work closely with other teams to build initial pipelines and help shape your data platform. As your needs evolve, you can grow your team with more specialized roles.

Sifflet provides advanced root cause analysis through complete data lineage and AI-powered anomaly detection. This means teams can quickly trace issues across pipelines and transformations, assess business impact, and resolve incidents faster with smart, context-aware alerts.

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.

Data freshness refers to how current your data is relative to the real-world events it's meant to represent. In data observability, it's one of the most critical metrics because even accurate data can lead to poor decisions if it's outdated. Whether you're monitoring financial trades or patient records, stale data can have serious business consequences.

After evaluating several data catalog vendors, jobvalley selected Sifflet because of its comprehensive features that addressed both data discovery and data quality monitoring. The platform’s ability to streamline onboarding and support real-time metrics made it the ideal choice for their growing data team.

A data platform is a unified system that helps companies collect, store, process, and analyze data across their organization. It acts as the central nervous system for all data operations, powering dashboards, AI models, and decision-making. When paired with strong data observability, it ensures teams can trust their data and move faster with confidence.

Absolutely! With Sifflet’s data lineage tracking, users can view the full column-level lineage from ingestion to consumption. This transparency helps users understand how each metric is computed and how it relates to other data or metrics in the pipeline.