Our Head of Product, Martin Zerbib, will walk you through how Sifflet enables data lineage tracking, real-time metrics, and data profiling at scale. You’ll get a sneak peek at our roadmap and see how we’re making data more accessible and reliable for teams of all sizes.

Sifflet shines in data reliability by offering real-time metrics and intelligent anomaly detection. During the webinar, we saw how even non-technical users can set up custom monitors, making it easy for teams to catch issues early and maintain SLA compliance with confidence.

Data lineage tracking is a key part of any robust data observability framework. It helps you understand where your data comes from, how it’s transformed, and where it flows. This visibility is essential for debugging issues, ensuring compliance, and building trust in your data pipelines. It's especially useful when paired with real-time data pipeline monitoring tools.

Great question! Sifflet makes data accessibility a breeze by offering intuitive search features and AI-generated metadata, so both technical and non-technical users can easily find and understand the data they need. This helps break down silos and supports better collaboration, which is a key component of effective data observability.

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.

When evaluating an observability platform, look for strong data lineage tracking, real-time metrics collection, anomaly detection capabilities, and broad integrations across your data stack. Features like field-level lineage, ease of setup, and user-friendly dashboards can make a big difference too. At Sifflet, we believe observability should empower both technical and business users with the context they need to trust and act on data.

Great question! Sifflet's Data Sharing provides access to rich metadata about your data assets, including tags, owners, and monitor configurations. By making this available in your own data warehouse, you can set up automated checks to ensure compliance with your governance standards. It's a powerful way to implement scalable data governance and reduce manual audits using our observability platform.

Great question! Data observability is essential for transformation pipelines because it gives teams visibility into data quality, pipeline performance, and transformation accuracy. Without it, errors can go unnoticed and create downstream issues in analytics and reporting. With a solid observability platform, you can detect anomalies, track data freshness, and ensure your transformations are aligned with business goals.