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

Can I use the Fivetran integration to monitor data pipeline health?

Absolutely! By surfacing connector statuses and metadata directly in the lineage graph and catalog, Sifflet helps you stay on top of pipeline health and detect issues early. It's a powerful step forward in proactive data pipeline monitoring.

How does data observability complement a data catalog?

While a data catalog helps you find and understand your data, data observability ensures that the data you find is actually reliable. Observability tools like Sifflet monitor the health of your data pipelines in real time, using features like data freshness checks, anomaly detection, and data quality monitoring. Together, they give you both visibility and trust in your data.

How can inefficient SQL queries impact my data pipeline performance?

Great question! Inefficient SQL queries can lead to slow dashboards, increased ingestion latency, and even failed workloads. By optimizing your queries using best practices like proper filtering and avoiding SELECT *, you help improve data pipeline monitoring and maintain overall data reliability.

How does Sifflet help with SLA compliance and incident response?

Sifflet supports SLA compliance by offering intelligent alerting, dynamic thresholding, and real-time dashboards that track incident metrics and resolution times. Its data reliability dashboard gives teams visibility into SLA adherence and helps prioritize issues based on business impact, streamlining incident management workflows and reducing mean time to resolution.

How does Sifflet help with data observability during the CI process?

Sifflet integrates directly with your CI pipelines on platforms like GitHub and GitLab to proactively surface issues before code is merged. By analyzing the impact of dbt model changes and running data quality monitors in testing environments, Sifflet ensures data reliability and minimizes production disruptions.

Can I see how a business metric is calculated in Sifflet?

Absolutely! With Sifflet’s data lineage tracking, users can view the full column-level lineage from ingestion to consumption. This transparency helps users understand how each metric is computed and how it relates to other data or metrics in the pipeline.

How does data observability help improve data reliability?

Data observability gives you end-to-end visibility into your data pipelines, helping you catch issues like schema changes, data drift, or ingestion failures before they impact downstream systems. By continuously monitoring real-time metrics and enabling root cause analysis, observability platforms like Sifflet ensure your data stays accurate, complete, and up-to-date, which directly supports stronger data reliability.

How does Sifflet help with data freshness monitoring?

At Sifflet, we offer a powerful Freshness Monitor that tracks when your data arrives and alerts you if it's missing or delayed. Whether you're working with batch or streaming pipelines, our observability platform makes it easy to stay on top of data freshness and ensure your analytics stay accurate and timely.

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