Sifflet significantly cut down Meero's troubleshooting time by enabling faster root cause analysis. With real-time alerts and automated anomaly detection, the data team was able to identify and resolve issues in minutes instead of hours, saving up to 50% of their 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.

Sentinel intelligently analyzes metadata like data lineage and schema changes to recommend what really needs monitoring. By focusing on high-impact areas, it cuts down on noise and helps teams manage alert fatigue while optimizing monitoring costs.

At Sifflet, we're integrating advanced AI models into our observability platform to enhance data quality monitoring and anomaly detection. Marie, our Machine Learning Engineer, has been instrumental in building intelligent systems that automatically detect issues across data pipelines, making it easier to maintain data reliability in real time.

Combining dbt Core with a data observability platform like Sifflet helps data teams go beyond transformation and into full-stack monitoring. It enables better root cause analysis, reduces time to resolution, and ensures your data products are trustworthy and resilient.

Absolutely! While dbt tests are a great starting point, Sifflet takes things further with advanced observability tools. By ingesting dbt tests into Sifflet, you can apply powerful features like dynamic thresholding, real-time alerts, and incident response automation. It’s a big step up in data reliability and SLA compliance.

Silent failures are tricky, but with the right data observability tools, you can catch them early. Look for platforms that support real-time alerts, schema registry integration, and dynamic thresholding. These features help you monitor for unexpected changes, missing data, or drift in your pipelines. Sifflet, for example, offers anomaly detection and root cause analysis that help you uncover and fix issues before they impact your business.

Great question! The old playbook for data observability focused heavily on technical infrastructure and treated data like servers — if the pipeline ran and the schema looked fine, the data was assumed to be trustworthy. But today, data is a strategic asset that powers business decisions, AI models, and customer experiences. At Sifflet, we believe modern observability platforms must go beyond uptime and freshness checks to provide context-aware insights that reflect real business impact.