Solution by Persona

Technical Teams

Data Engineer

You’ll be the boss. Sifflet gives you the capabilities and oversight to manage your data stack like never before, faster than you ever thought possible.

Launch Product Tour

Troubleshoot and Debug

Sifflet makes troubleshooting and debugging faster, more efficient and more effective thanks to pipeline failure or data anomaly alerts and rich contextual information.

Pipeline Performance Optimization

Pipelines power your data stack. Sifflet helps you monitor pipeline performance and get insight into bottlenecks and inefficient transformations.

Quality Assurance

Uplevel your data quality thanks to automated quality checks and validations and custom rules to ensure data integrity.

More Productive. More Powerful.

Sifflet augments your productivity by giving you end-to-end visibility into your architecture, assets, and pipelines. AI-powered monitoring sends you the right alerts, at the right time, so you can triage efficiently and effectively. And advanced lineage capabilities enable you to get to resolution faster.

Built for Business.

Sifflet helps you collaborate better with users on the business end. Give your data consumers self-serve tools, such as smart monitoring setup that leverages large language models and embed monitoring alerts into their data products.

See Value From Day One.

Sifflet connects to hundreds of tools already in your stack and offers out of the box monitors and tooling so you can start seeing value from day one.

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

How can I measure the ROI of a data observability platform?

You can measure the ROI of a data observability platform by tracking key metrics like the number of data incidents per year, time to detection, and time to resolution. These real-time metrics give you insight into how often issues occur and how quickly your team can resolve them. Don’t forget to factor in qualitative benefits too, like improved team satisfaction and stronger data governance.

Why is using WHERE instead of HAVING so important for performance?

Using WHERE instead of HAVING when not working with GROUP BY clauses is crucial because WHERE filters data earlier in the query execution. This reduces the amount of data processed, which improves query speed and supports better metrics collection in your observability platform.

Can Datadog help with root cause analysis during incidents?

Yes, Datadog is excellent for root cause analysis, especially with its Bits AI SRE feature. This AI-powered assistant automatically investigates incidents by analyzing telemetry data like logs, metrics, and traces, then suggests likely causes and next steps. It’s a major boost for incident response automation and helps reduce mean time to resolution (MTTR).

How can Sifflet help ensure SLA compliance and prevent bad data from affecting business decisions?

Sifflet helps teams stay on top of SLA compliance with proactive data freshness checks, anomaly detection, and incident tracking. Business users can rely on health indicators and lineage views to verify data quality before making decisions, reducing the risk of costly errors due to unreliable data.

Why did Adaptavist choose Sifflet over other observability tools?

Callum and his team were impressed by how quickly Sifflet’s cross-repo data lineage tracking gave them visibility into their pipelines. Within days, they had a working proof of concept and were debugging in minutes instead of days. The unified view across their stack made Sifflet the right fit for scaling data observability across teams.

Is Sifflet suitable for non-technical users who want to contribute to data quality?

Yes, and that’s one of the things we’re most excited about! Sifflet empowers non-technical users to define custom monitoring rules and participate in data quality efforts without needing to write dbt code. It’s all part of building a culture of shared responsibility around data governance and observability.

How does Sifflet support real-time data lineage and observability?

Sifflet provides automated, field-level data lineage integrated with real-time alerts and anomaly detection. It maps how data flows across your stack, enabling quick root cause analysis and impact assessments. With features like data drift detection, schema change tracking, and pipeline error alerting, Sifflet helps teams stay ahead of issues and maintain data reliability.

How does the updated lineage graph help with root cause analysis?

By merging dbt model nodes with dataset nodes, our streamlined lineage graph removes clutter and highlights what really matters. This cleaner view enhances root cause analysis by letting you quickly trace issues back to their source with fewer distractions and more context.

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