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Frequently asked questions
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.
Does Sifflet support AI-driven use cases?
Yes, Sifflet leverages AI to enhance data observability with features like anomaly detection and predictive insights. This ensures your data systems remain resilient and can support advanced analytics and AI-driven initiatives. Have a look at how Sifflet is leveraging AI for better data observability here
How can organizations create a culture that supports data observability?
Fostering a data-driven culture starts with education and collaboration. Salma recommends training programs that boost data literacy and initiatives that involve all data stakeholders. This shared responsibility approach ensures better data governance and more effective data quality monitoring.
How does a data catalog improve data reliability and governance?
A well-managed data catalog enhances data reliability by capturing metadata like data lineage, ownership, and quality indicators. It supports data governance by enforcing access controls and documenting compliance requirements, making it easier to meet regulatory standards and ensure trustworthy analytics across the organization.
What kind of monitoring capabilities does Sifflet offer out of the box?
Sifflet comes with a powerful library of pre-built monitors for data profiling, data freshness checks, metrics health, and more. These templates are easily customizable, supporting both batch data observability and streaming data monitoring, so you can tailor them to your specific data pipelines.
How does Sifflet Insights help improve data quality in my BI dashboards?
Sifflet Insights integrates directly into your BI tools like Looker and Tableau, providing real-time alerts about upstream data quality issues. This ensures you always have accurate and reliable data for your reports, which is essential for maintaining data trust and improving data governance.
How does Sifflet support data quality monitoring at scale?
Sifflet uses AI-powered dynamic monitors and data validation rules to automate data quality monitoring across your pipelines. It also integrates with tools like Snowflake and dbt to ensure data freshness checks and schema validations are embedded into your workflows without manual overhead.
Is Sifflet suitable for business users as well as engineers?
Absolutely! Sifflet’s user-friendly interface and clear data asset indicators make it easy for business users to find and trust the right data. With features like visual data discovery and real-time metrics, it bridges the gap between technical teams and business stakeholders.













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