Product

Data Security

%%Simply%% Secure

Sifflet makes no compromises when it comes to ensuring your security, risk mitigation, and compliance.

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Least Privilege Policies

Sifflet helps you manage who can access what data. It features RBAC principles to grant or restrict access based on a user’s role in the organization, rather than managing individual permissions.

A No Data Storage Approach

Our approach eliminates persistent data storage, with transient data handling and ephemeral computing principles.

State of the Art Isolation

Sifflet uses single tenancy architecture, so each and every client benefits from a dedicated and isolated instance of our tooling, infrastructure, and associated resources.

ENSURE

Product Security

Sifflet gives you peace of mind with tools to keep access to your environment protected.

SaaS or self-hosted deployment
Cross-IdP SSO support
RBAC

ISOLATE

Platform Security

Security at the platform level to keep each and every client safe.

Single tenancy architecture for state of the art isolation
No data alteration and least privilege policy
No data storage approach

PROTECT

Compliance Certifications

Your data’s sensitive. Sifflet is certified compliant across a range of standards.

SOC 2
ISO 27001
GDPR
HIPAA

Reinforced %%Reliability%%

Sifflet’s monitoring features reinforce data reliability for all users, so business can deliver.

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Data Users

Enhance customer trust with tools that secure data and help your business align with regulatory and compliance requirements.

Data Engineers

Keep specific data pipelines and storage systems confidential and protect critical data assets from manipulation.

Data Leaders

Avoid legal issues, financial penalties and reputational damage associated with data mishandling, or unauthorized data access and breaches.

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sifflet a guide to assessing your need for a data observability

Insights

A Guide to Assessing your Need for Data Observability

Product

How Sifflet keeps your data safe

Product

Data quality for all - Un-siloed data quality

Need %%strong%% data protection?

Benefit from peace of mind that your sensitive data is safe and your business mitigates risk by remaining compliant.

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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

Still have a question in mind ?

Frequently asked questions

Why is semantic quality monitoring important for AI applications?

Semantic quality monitoring ensures that the data feeding into your AI models is contextually accurate and production-ready. At Sifflet, we're making this process seamless with tools that check for data drift, validate schema, and maintain high data quality without manual intervention.

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.

What types of data lineage should I know about?

There are four main types: technical lineage, business lineage, cross-system lineage, and governance lineage. Each serves a different purpose, from debugging pipelines to supporting compliance. Tools like Sifflet offer field-level lineage for deeper insights, helping teams across engineering, analytics, and compliance understand and trust their data.

Which ingestion tools work best with cloud data observability platforms?

Popular ingestion tools like Fivetran, Stitch, and Apache Kafka integrate well with cloud data observability platforms. They offer strong support for telemetry instrumentation, real-time ingestion, and schema registry integration. Pairing them with observability tools ensures your data stays reliable and actionable across your entire stack.

How is data volume different from data variety?

Great question! Data volume is about how much data you're receiving, while data variety refers to the different types and formats of data sources. For example, a sudden drop in appointment data is a volume issue, while a new file format causing schema mismatches is a variety issue. Observability tools help you monitor both dimensions to maintain healthy pipelines.

Can Sifflet help me monitor data drift and anomalies beyond what dbt offers?

Absolutely! While dbt is fantastic for defining tests, Sifflet takes it further with advanced data drift detection and anomaly detection. Our platform uses intelligent monitoring templates that adapt to your data’s behavior, so you can spot unexpected changes like missing rows or unusual values without setting manual thresholds.

How does Sifflet's integration with dbt Core improve data observability?

Great question! By integrating with dbt Core, Sifflet enhances data observability across your entire data stack. It helps you monitor dbt test coverage, map tests to downstream dependencies using data lineage tracking, and consolidate metadata like tags and descriptions, all in one place.

How does SQL Table Tracer support different SQL dialects for data lineage tracking?

SQL Table Tracer uses Antlr4 and a unified grammar with semantic predicates to support multiple SQL dialects like Snowflake, Redshift, and PostgreSQL. This ensures accurate data lineage tracking across diverse systems without needing separate parsers for each dialect.

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