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

Title of looking for more content goes here

Still have a question in mind ?

Frequently asked questions

What makes Sifflet’s AI agents different from traditional observability tools?

Great question! Traditional observability platforms focus mostly on detection and alerting, but Sifflet’s AI agents go beyond that. They’re designed to understand business impact, automate root cause analysis, and even take action when appropriate. This shift means data reliability becomes proactive and business-aware, not just reactive and technical. It’s a whole new level of data observability.

What is data ingestion and why is it so important for modern businesses?

Data ingestion is the process of collecting and loading data from various sources into a central system like a data lake or warehouse. It's the first step in your data pipeline and is critical for enabling real-time metrics, analytics, and operational decision-making. Without reliable ingestion, your downstream analytics and data observability efforts can quickly fall apart.

How does data observability support effective AI governance?

Great question! Data observability plays a crucial role in AI governance by helping teams continuously monitor model behavior, detect data drift or concept drift, and ensure outputs remain fair and explainable. With tools like data lineage tracking and real-time metrics, observability helps verify that AI systems operate within approved policies, making governance not just a policy but a practice.

How does Sifflet help ensure SLA compliance and data reliability?

Sifflet supports SLA compliance by continuously monitoring key data quality metrics and surfacing issues before they impact business decisions. With automated anomaly detection, real-time alerts, and root cause analysis, our observability platform helps teams maintain data reliability and stay ahead of potential SLA breaches.

How does automated data lineage improve data reliability?

Automated data lineage boosts data reliability by giving teams a clear, real-time view of data flows and dependencies. This visibility supports faster troubleshooting, better data governance, and improved SLA compliance, especially when combined with other observability tools in your stack.

What makes Sifflet different from other observability tools like Datadog or IBM Databand?

Unlike Datadog, which focuses on infrastructure and application telemetry, and IBM Databand, which specializes in pipeline health, Sifflet offers true end-to-end data observability. It combines data quality monitoring, data lineage tracking, and anomaly detection into one platform, all powered by AI agents designed to reduce manual effort and boost trust in your data.

How does Datadog handle data observability after acquiring Metaplane?

After acquiring Metaplane, Datadog integrated basic data observability features like data freshness checks, schema change detection, and column-level lineage into its platform. This allows DevOps and data teams to monitor pipeline health within the same interface. However, it still falls short in offering business-aware observability, which means it might not catch content-level issues that impact downstream analytics or decision-making.

Who should be the first hire on a new data team?

If you're just starting out, look for someone with 'Full Data Stack' capabilities, like a Data Analyst with strong SQL and business acumen or a Data Engineer with analytics skills. This person can work closely with other teams to build initial pipelines and help shape your data platform. As your needs evolve, you can grow your team with more specialized roles.

Read all FAQs