We're excited to use this funding to accelerate product innovation, expand our North American presence, and grow our team. Our focus will be on enhancing AI-powered capabilities, improving data pipeline monitoring, and helping customers maintain data reliability at scale.

The main trade-offs of using Datadog for data pipeline monitoring include high costs, especially in high-cardinality environments, and limited visibility into the actual data content. While Datadog is great for real-time metrics and infrastructure observability, it doesn't provide deep data validation rules or business-aware anomaly detection. Teams needing those capabilities may want to pair it with a more focused data observability solution.

At Sifflet, we believe data quality isn't one-size-fits-all. Our observability platform blends technical robustness with business context, offering customized data quality monitoring that adapts to your specific use cases. This means you get both reliable pipelines and meaningful metrics that align with your business goals.

Setting clear priorities and using a centralized pipeline orchestration visibility tool can help manage expectations across the organization. When stakeholders understand what the team can deliver and when, it builds trust and reduces pressure on your team, leading to a healthier and happier work environment.

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.

Great question! With our new ServiceNow integration, Sifflet can automatically create incidents from any data alert, helping your team respond faster and stay on top of critical issues. It's a big win for incident response automation and keeps your data observability workflows smooth and efficient.

Great question! Data quality is one of the foundational pillars of a strong data governance strategy because it directly impacts decision-making, compliance, and trust in your data. Poor data quality can lead to biased AI models, flawed analytics, and even regulatory risk. That's why integrating data quality monitoring early in your data lifecycle is key to building a reliable and responsible data foundation.

Absolutely! One of Sifflet’s strengths is its seamless integration across your existing data stack. Whether you're working with tools like Airflow, Snowflake, or Kafka, Sifflet helps you monitor your data pipelines without needing to overhaul your infrastructure.