Solution by Use Case

Cost Observability

Cost-efficient data pipelines

Pinpoint cost inefficiencies and anomalies thanks to full-stack data observability.

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Data asset optimization

Leverage lineage and Data Catalog to pinpoint underutilized assets
Get alerted on unexpected behaviors in data consumption patterns

Proactive data pipeline management

Proactively prevent pipelines from running in case a data quality anomaly is detected

Sifflet’s AI Helps Us Focus on What Moves the Business

What impressed us most about Sifflet’s AI-native approach is how seamlessly it adapts to our data landscape — without needing constant tuning. The system learns patterns across our workflows and flags what matters, not just what’s noisy. It’s made our team faster and more focused, especially as we scale analytics across the business.

Simoh-Mohamed Labdoui

Head of Data

"Enabler of Cross Platform Data Storytelling"

"Sifflet has been a game-changer for our organization, providing full visibility of data lineage across multiple repositories and platforms. The ability to connect to various data sources ensures observability regardless of the platform, and the clean, intuitive UI makes setup effortless, even when uploading dbt manifest files via the API. Their documentation is concise and easy to follow, and their team's communication has been outstanding—quickly addressing issues, keeping us informed, and incorporating feedback. "

Callum O'Connor

Senior Analytics Engineer, The Adaptavist

"Building Harmony Between Data and Business With Sifflet"

"Sifflet serves as our key enabler in fostering a harmonious relationship with business teams. By proactively identifying and addressing potential issues before they escalate, we can shift the focus of our interactions from troubleshooting to driving meaningful value. This approach not only enhances collaboration but also ensures that our efforts are aligned with creating impactful outcomes for the organization."

Sophie Gallay

Data & Analytics Director, Etam

" Sifflet empowers our teams through Centralized Data Visibility"

"Having the visibility of our DBT transformations combined with full end-to-end data lineage in one central place in Sifflet is so powerful for giving our data teams confidence in our data, helping to diagnose data quality issues and unlocking an effective data mesh for us at BBC Studios"

Ross Gaskell

Software engineering manager, BBC Studios

"Sifflet allows us to find and trust our data"

"Sifflet has transformed our data observability management at Carrefour Links. Thanks to Sifflet's proactive monitoring, we can identify and resolve potential issues before they impact our operations. Additionally, the simplified access to data enables our teams to collaborate more effectively."

Mehdi Labassi

CTO, Carrefour Links

"A core component of our data strategy and transformation"

"Using Sifflet has helped us move much more quickly because we no longer experience the pain of constantly going back and fixing issues two, three, or four times."

Sami Rahman

Director of Data, Hypebeast

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Frequently asked questions

What role does real-time monitoring play in Sifflet’s platform?

Real-time metrics are essential for proactive data pipeline monitoring. Sifflet’s observability tools provide real-time alerts and anomaly detection, helping teams quickly identify and resolve issues before they impact downstream systems or violate SLA compliance.

Can Sifflet’s dbt Impact Analysis help with root cause analysis?

Absolutely! By identifying all downstream assets affected by a dbt model change, Sifflet’s Impact Report makes it easier to trace issues back to their source, significantly speeding up root cause analysis and reducing incident resolution time.

Why is data categorization important for data governance and compliance?

Effective data categorization is essential for data governance and compliance because it helps identify sensitive data like PII, ensuring the correct protection policies are applied. With Sifflet’s classification tags, governance teams can easily locate and safeguard sensitive information, supporting GDPR data monitoring and overall data security compliance.

Can Sifflet integrate with our existing data tools and platforms?

Absolutely! Sifflet is designed to integrate seamlessly with your current stack. We support a wide range of tools including Airflow, Snowflake, AWS Glue, and more. Our goal is to provide complete pipeline orchestration visibility and data freshness checks, all from one intuitive interface.

How can data lineage tracking improve root cause analysis during incidents?

Data lineage tracking lets you see how data flows across your pipelines, from source to dashboard. This visibility is crucial for root cause analysis because it helps pinpoint exactly where issues originate and which downstream assets are affected. With Sifflet, lineage is mapped automatically, so you can resolve issues faster and with full context.

What makes business-aware data observability so important?

Business-aware observability bridges the gap between technical issues and real-world outcomes. It’s not just about detecting schema changes or data drift — it’s about understanding how those issues affect KPIs, dashboards, and decisions. At Sifflet, we bring together telemetry instrumentation, data profiling, and business context so teams can prioritize incidents based on impact, not just severity. This empowers everyone, from data engineers to product managers, to trust and act on data with confidence.

Who should use the data observability checklist?

This checklist is for anyone who relies on trustworthy data—from CDOs and analysts to DataOps teams and engineers. Whether you're focused on data governance, anomaly detection, or building resilient pipelines, the checklist gives you a clear path to choosing the right observability tools.

How does Sifflet help with root cause analysis in data pipelines?

Sifflet uses intelligent agents to perform root cause analysis across your data lineage. Instead of just alerting you to an issue, it highlights the upstream source, impacted KPIs, and suggests remediation steps. This drastically cuts down investigation time and improves incident response in your data pipeline monitoring workflows.

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