Great question! With the launch of Data-Quality-as-Code v2, Sifflet has made it much easier to create and manage monitors at scale. Whether you prefer working programmatically or through the UI, our platform now offers smoother workflows and standardized threshold settings for more intuitive data quality monitoring.

Reverse ETL is the process of moving data from your data warehouse into external systems like CRMs or marketing platforms. It plays a crucial role in the modern data stack by enabling operational analytics, allowing business teams to act on real-time metrics and make data-driven decisions directly within their everyday tools.

Our observability tools are designed to support a wide range of industries, from retail and finance to tech and logistics. Whether you're monitoring streaming data in real time or ensuring data freshness in batch pipelines, Sifflet helps teams maintain high data quality and meet SLA compliance goals.

Data observability is key during data integration because it helps detect issues like schema changes or broken APIs early on. Without it, bad data can flow downstream, impacting analytics and decision-making. At Sifflet, we believe observability should start at the source to ensure data reliability across the whole pipeline.

Great question! Software observability took off in the 2010s with the rise of cloud-native apps, but data observability is catching up fast. As businesses start treating data as a mission-critical asset—especially with the growth of AI and cloud data platforms like Snowflake—the need for real-time visibility, data reliability, and governance has become urgent. We're in the early innings, but the pace is accelerating quickly.

Data quality refers to how accurate, complete, consistent, and timely your data is. It's essential because poor data quality can lead to unreliable analytics, missed business opportunities, and even financial losses. Investing in data quality monitoring helps teams regain trust in their data and make confident, data-driven decisions.

Sifflet’s data catalog acts as the central hub for your data ecosystem, enriched with metadata and classification tags. This foundation supports cloud data observability by giving teams full visibility into their assets, enabling better data lineage tracking, telemetry instrumentation, and overall observability platform performance.

Data lineage plays a key role in compliance monitoring by providing transparency into where data comes from, how it's processed, and where it ends up. This is crucial for meeting regulations like GDPR and HIPAA, and for maintaining strong data governance practices across the organization.