For sure! Our CEO, Salma Bakouk, will be speaking about the top data trends to watch in 2025, including how GenAI and advanced anomaly detection are shaping the future of observability platforms. You’ll walk away with actionable insights for your data strategy.

Sifflet provides real-time metadata observability that detects when different engines, like Spark and Trino, interpret table schemas differently. By tracking atomic commits and metadata snapshots, Sifflet flags compatibility issues early, preventing downstream failures in dashboards or analytics tools.

Sifflet uses AI-driven context and dynamic thresholding to prioritize alerts based on impact and relevance. Its intelligent alerting system ensures users only get notified when it truly matters, helping reduce alert fatigue and enabling faster, more focused incident response.

Sifflet filters alerts based on business criticality, so teams aren’t overwhelmed by noise. By aligning alerts with business context, it ensures only the most impactful issues get escalated. This smarter approach to pipeline error alerting helps teams focus on what truly matters and reduces unnecessary interruptions.

Metrics observability helps business users trust and understand the KPIs they rely on by making it easy to trace how metrics are defined, calculated, and connected to other data assets. With Sifflet’s observability platform, teams can ensure their business metrics are accurate, reliable, and aligned across departments.

Absolutely! Sifflet integrates data quality monitoring into its Data Catalog, allowing users to define and view data quality checks right alongside asset metadata. This gives teams real-time insights into data reliability and helps build trust in the assets they’re using for decision-making.

Adaptavist built a platform health dashboard that scores each asset based on data freshness, quality, and reliability. This kind of data profiling helps them prioritize fixes, improve root cause analysis, and ensure continued trust in their analytics pipeline observability.

Data observability is crucial no matter which integration method you use. With ETL or ELT, you're moving and transforming data across multiple systems, which can introduce errors or delays. An observability platform like Sifflet helps you track data freshness, detect anomalies, and ensure SLA compliance across your pipelines. This means fewer surprises, faster root cause analysis, and more reliable data for your business teams.