IntEgration

Integrates with your modern data stack

Sifflet seamlessly integrates into your data sources and preferred tools, and can run on AWS, Google Cloud Platform, and Microsoft Azure.

Results tag

Showing 0 results

More integration coming soon !

The Sifflet team is always working hard on incorporating more integrations into our product. Get in touch if you want us to keep you updated!

Thanks for your message !

Oops! Something went wrong while submitting the form.

Want Sifflet to integrate your stack?

We'd be such a good fit together

Talk to an expert

"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

Frequently asked questions

What role does data observability play in modern data governance?

AI enhances data observability with advanced anomaly detection, predictive analytics, and automated root cause analysis. This helps teams identify and resolve issues faster while reducing manual effort. Have a look at how Sifflet is leveraging AI for better data observability here

What’s next for data observability at Sifflet?

We’re focused on solving the next generation of challenges, like hybrid environments, end-to-end data lineage tracking, and scaling data trust. Whether it's batch data observability or real-time pipeline monitoring, our mission is to help organizations build resilient, transparent, and future-proof data stacks.

Why is field-level lineage important in data observability?

Field-level lineage gives you a detailed view into how individual data fields move and transform through your pipelines. This level of granularity is super helpful for root cause analysis and understanding the impact of changes. A platform with strong data lineage tracking helps teams troubleshoot faster and maintain high data quality.

What is data distribution deviation and why should I care about it?

Data distribution deviation happens when the distribution of your data changes over time, either gradually or suddenly. This can lead to serious issues like data drift, broken queries, and misleading business metrics. With Sifflet's data observability platform, you can automatically monitor for these deviations and catch problems before they impact your decisions.

Can I deploy Sifflet in my own environment for better control?

Absolutely! Sifflet offers both SaaS and self-managed deployment models. With the self-managed option, you can run the platform entirely within your own infrastructure, giving you full control and helping meet strict compliance and security requirements.

What is reverse ETL and why is it important in the modern data stack?

Reverse ETL is the process of moving data from your data warehouse into external systems like CRMs or marketing platforms. It plays a crucial role in the modern data stack by enabling operational analytics, allowing business teams to act on real-time metrics and make data-driven decisions directly within their everyday tools.

What role does MCP play in improving incident response automation?

MCP is a game-changer for incident response automation. By allowing LLMs to interact with telemetry data, call remediation tools, and maintain context over time, MCP enables proactive monitoring and faster resolution. This aligns perfectly with Sifflet’s mission to reduce downtime and improve pipeline resilience.

What non-quantifiable benefits can data observability bring to my organization?

Besides measurable improvements, data observability also boosts trust in data, enhances decision-making, and improves the overall satisfaction of your data team. When your team spends less time debugging and more time driving value, it fosters a healthier data culture and supports long-term business growth.

Read all FAQs

Still have questions?