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.

Absolutely! Flow Stopper supports integration with popular tools like Airflow for orchestration and Snowflake for storage. It can run anomaly detection and data validation rules mid-pipeline, helping ensure data quality as it moves through your stack.

Retailers are adopting data observability to gain real-time visibility into inventory across all channels, reduce stock inaccuracies, and avoid costly misalignments between supply and demand. With data observability tools, they can proactively detect issues, monitor data quality, and improve operational efficiency across their data pipelines.

Sifflet gives you end-to-end visibility into your data pipelines, including those built with dbt. With features like pipeline health dashboards, data freshness checks, and telemetry instrumentation, your team can monitor pipeline performance and ensure SLA compliance with confidence.

Data observability plays a key role in SLA compliance by detecting issues like ingestion latency, schema changes, or data drift before they impact downstream users. With proper data quality monitoring and real-time metrics, you can catch problems early and keep your dashboards and reports reliable.

A user-friendly interface boosts adoption across teams and makes it easier to navigate complex datasets. For observability tools, especially those focused on data cataloging and data discovery, a clean UI enables faster insights and more efficient collaboration.

Absolutely! By tagging fields like 'Low Cardinality', data teams can quickly identify which fields are best suited for specific monitors. This enables more targeted data pipeline monitoring, making it easier to detect anomalies and maintain SLA compliance across your analytics pipeline.

Sifflet is designed for both technical and non-technical users, offering no-code monitors, natural-language setup, and cross-persona alerts. This means analysts, data scientists, and executives can all engage with data quality monitoring without needing engineering support, making it a truly inclusive observability platform.