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Frequently asked questions

What is dbt Impact Analysis and how does it help with data observability?
dbt Impact Analysis is a new feature from Sifflet that automatically comments on GitHub or GitLab pull requests with a list of impacted assets when a dbt model is changed. This helps teams enhance their data observability by understanding downstream effects before changes go live.
How can data teams prioritize what to monitor in complex environments?
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.
What does 'observability culture' mean at Adaptavist?
For Adaptavist, observability culture means going beyond tools. It's about clear ownership of alerts, integrating data quality monitoring into sprints, and giving stakeholders ways to provide feedback directly in dashboards. They even track observability metrics to continuously improve their own observability practices.
What does Sifflet plan to do with the new $18M in funding?
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.
How has the shift from ETL to ELT improved performance?
The move from ETL to ELT has been all about speed and flexibility. By loading raw data directly into cloud data warehouses before transforming it, teams can take advantage of powerful in-warehouse compute. This not only reduces ingestion latency but also supports more scalable and cost-effective analytics workflows. It’s a big win for modern data teams focused on performance and throughput metrics.
Why does query formatting matter in modern data operations?
Well-formatted queries are easier to debug, share, and maintain. This aligns with DataOps best practices and supports transparency in data pipelines, which is essential for consistent SLA compliance and proactive monitoring.
How do JOIN strategies affect query execution and data observability?
JOINs can be very resource-intensive if not used correctly. Choosing the right JOIN type and placing conditions in the ON clause helps reduce unnecessary data processing, which is key for effective data observability and real-time metrics tracking.
What impact did Sifflet have on fostering a data-driven culture at Meero?
Sifflet’s intuitive UI and real-time data observability dashboards empowered even non-technical users at Meero to understand data health. This transparency helped build trust in data and promoted a stronger data-driven culture across the organization.
Still have questions?