No, Sifflet does not store your data. We designed our platform to discard any data previews immediately after display, and we only retain metadata like table and column names. This approach supports GDPR compliance and strengthens your overall data governance strategy.

Not all data is created equal, so it's important to focus data quality monitoring efforts on the assets that drive business outcomes. That means identifying key dashboards, critical metrics, and high-impact models, then using tools like pipeline health dashboards and SLA monitoring to keep them reliable and fresh.

Great question! MCP allows large language models to access structured external context like pipeline metadata, logs, and diagnostics tools. At Sifflet, we use MCP to enhance data observability by enabling intelligent agents to monitor, diagnose, and act on issues across complex data pipelines in real time.

Data ingestion is the process of collecting and loading data from various sources into a central system like a data lake or warehouse. It's the first step in your data pipeline and is critical for enabling real-time metrics, analytics, and operational decision-making. Without reliable ingestion, your downstream analytics and data observability efforts can quickly fall apart.

Yes, it is! Sifflet currently supports Data Sharing to Snowflake, BigQuery, and S3, with more destinations on the way. This makes it easy to integrate Sifflet into your cloud data observability strategy and leverage your existing infrastructure for deeper insights and proactive monitoring.

In 2025, expect to see more focus on AI-driven anomaly detection, dynamic thresholding, and predictive analytics monitoring. Staying ahead means experimenting with new observability tools, engaging with peers, and continuously aligning your data strategy with evolving business needs.

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.

Absolutely. AI systems need more than just schemas—they need context. Observability platforms like Sifflet provide machine-readable trust signals, data freshness checks, and reliability scores through APIs. This allows autonomous agents to assess data quality in real time and make smarter decisions without relying on outdated documentation.