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’s on the horizon for data observability as AI and regulations evolve?

The future of data observability is all about scale and responsibility. With AI adoption growing and regulations tightening, businesses need observability tools that can handle unstructured data, ensure SLA compliance, and support security observability. At Sifflet, we're already helping customers monitor ML models and enforce data contracts, and we're excited about building self-healing pipelines and extending observability to new data types.

What is SQL Table Tracer and how does it help with data observability?

SQL Table Tracer (STT) is a lightweight library that extracts table-level lineage from SQL queries. It plays a key role in data observability by identifying upstream and downstream tables, making it easier to understand data dependencies and track changes across your data pipelines.

Can container-based environments improve incident response for data teams?

Absolutely. Containerized environments paired with observability tools like Kubernetes and Prometheus for data enable faster incident detection and response. Features like real-time alerts, dynamic thresholding, and on-call management workflows make it easier to maintain healthy pipelines and reduce downtime.

Why are data consumers becoming more involved in observability decisions?

We’re seeing a big shift where data consumers—like analysts and business users—are finally getting a seat at the table. That’s because data observability impacts everyone, not just engineers. When trust in data is operationalized, it boosts confidence across the business and turns data teams into value creators.

How does Sifflet make data observability more accessible to BI users?

Great question! At Sifflet, we're committed to making data observability insights available right where you work. That’s why we’ve expanded beyond our Chrome extension to integrate directly with popular Data Catalogs like Atlan, Alation, Castor, and Data Galaxy. This means BI users can access real-time metrics and data quality insights without ever leaving their workflow.

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.

What made data observability such a hot topic in 2021?

Great question! Data observability really took off in 2021 because it became clear that reliable data is critical for driving business decisions. As data pipelines became more complex, teams needed better ways to monitor data quality, freshness, and lineage. That’s where data observability platforms came in, helping companies ensure trust in their data by making it fully observable end-to-end.

What’s the difference between a data schema and a database schema?

Great question! A data schema defines structure across your entire data ecosystem, including pipelines, APIs, and ingestion tools. A database schema, on the other hand, is specific to one system, like PostgreSQL or BigQuery, and focuses on tables, columns, and relationships. Both are essential for effective data governance and observability.

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