Ideally, data quality monitoring should begin as early as possible in your data journey. As Dan Power shared during Entropy, fixing issues at the source is far more efficient than tracking down errors later. Early adoption of observability tools helps you proactively catch problems, reduce manual fixes, and improve overall data reliability from day one.

Absolutely! With Sifflet’s data quality monitoring integrated into Airflow DAGs, you can detect and isolate bad-quality data before it impacts downstream processes. This helps maintain high data reliability and supports SLA compliance.

Great question! Data observability plays a key role in GDPR compliance by giving teams real-time visibility into where personal data lives, how it's being used, and whether it's being processed according to user consent. With an observability platform in place, you can track data lineage, monitor data quality, and quickly respond to deletion or access requests in a compliant way.

Data observability provides visibility into your pipelines by tracking key metrics like row counts, duplicates, and ingestion patterns. It acts as an early warning system, helping teams catch volume anomalies before they affect dashboards or ML models. By using a robust observability platform, you can ensure that your data is consistently complete and trustworthy.

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.

Absolutely! Sifflet’s data lineage tracking gives you a clear view of how data flows and transforms across your systems. This level of transparency is crucial for root cause analysis and ensuring data governance standards are met.

Shippeo uses Sifflet’s data observability platform to monitor every stage of their data pipelines. By implementing raw data monitoring, intermediate layer checks, and front-facing metric validation, they catch issues early and maintain trust in their real-time supply chain visibility tools.

Great question! Data volume is about how much data you're receiving, while data variety refers to the different types and formats of data sources. For example, a sudden drop in appointment data is a volume issue, while a new file format causing schema mismatches is a variety issue. Observability tools help you monitor both dimensions to maintain healthy pipelines.