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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

Where can I find Sifflet at Big Data LDN 2024?

You can find the Sifflet team at Booth Y640 during Big Data LDN on September 18-19. Stop by to learn more about our data observability platform and how we’re helping organizations like the BBC and Penguin Random House improve their data reliability.

Why is smart alerting important in data observability?

Smart alerting helps your team focus on what really matters. Instead of flooding your Slack with every minor issue, a good observability tool prioritizes alerts based on business impact and data asset importance. This reduces alert fatigue and ensures the right people get notified at the right time. Look for platforms that offer customizable severity levels, real-time alerts, and integrations with your incident management tools like PagerDuty or email alerts.

What’s the difference between data distribution and data lineage tracking?

Great distinction! Data distribution shows you how values are spread across a dataset, while data lineage tracking helps you trace where that data came from and how it’s moved through your pipeline. Both are essential for root cause analysis, but they solve different parts of the puzzle in a robust observability platform.

Why is data lineage tracking important in a data observability tool?

Data lineage tracking helps you understand how data flows from source to dashboard. When something breaks, lineage reveals which downstream assets are affected and who needs to be notified. It’s a key feature for root cause analysis and faster incident resolution in any observability platform.

What should I look for in a modern ETL or ELT tool?

When choosing an ETL or ELT tool, look for features like built-in integrations, ease of use, automation capabilities, and scalability. It's also important to ensure the tool supports observability tools for data quality monitoring, data drift detection, and schema validation. These features help you maintain trust in your data and align with DataOps best practices.

How does Dailymotion foster a strong data culture beyond just using observability tools?

They’ve implemented a full enablement program with starter kits, trainings, and office hours to build data literacy and trust. Observability tools are just one part of the equation; the real focus is on enabling confident, autonomous decision-making across the organization.

Why is data quality monitoring crucial for AI-readiness, according to Dailymotion’s journey?

Dailymotion emphasized that high-quality, well-documented, and observable data is essential for AI readiness. Data quality monitoring ensures that AI systems are trained on accurate and reliable inputs, which is critical for producing trustworthy outcomes.

What are the main trade-offs of using Datadog for data pipeline monitoring?

The main trade-offs of using Datadog for data pipeline monitoring include high costs, especially in high-cardinality environments, and limited visibility into the actual data content. While Datadog is great for real-time metrics and infrastructure observability, it doesn't provide deep data validation rules or business-aware anomaly detection. Teams needing those capabilities may want to pair it with a more focused data observability solution.

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