Sifflet

My main use case is that we deployed Sifflet  to solve a critical lack of visibility into the data health of a retail client's AWS-based data lake: S3 , Glue, Redshift. The implementation focused on Sifflet 's ML-driven anomaly detection to monitor over 1,500 tables and 10 million hourly records. By integrating via AWS Marketplace , we moved from manual SQL validation to automated monitoring of metadata and query logs. This allowed us to detect silent failures, such as partial loading or subtle schema drift, that were previously invisible to the engineering team.

The end-to-end data lineage had the greatest impact for us. It provided an automated map correlating upstream AWS Glue  job to downstream Redshift table and Tableau reports. This was vital for instant root cause analysis because we could trace a dashboard error back to its exact point of failure in the pipeline in seconds, rather than hours.

The standout feature that Sifflet offers is definitely the full-stack data lineage. In a complex AWS  environment like ours, it is not enough to know that a table is broken, but you need to know where it broke and what it affects. Sifflet automatically maps the data flow from the ingestion layer in S3  and Glue, through the transformation in Redshift, all the way to the final BI dashboards. This allowed us to perform instant root cause analysis. If a report is wrong, we can trace it back to the exact source or transformation step in seconds. It completely eliminated the hours spent on manual SQL debugging and gives the team total control over the data lifecycle.

Sifflet impacted positively my organization because it established a certified data standard for business stakeholders and also avoided a lot of incidents and improved the governance of the data. Incident prevention is significant, as 80% of anomalies are now resolved before they impact executive reporting. Additionally, we achieved real-time visibility into data freshness and schema evolution across the entire lake. It is all automated now.

Sifflet can be improved in terms of premium investment. High entry cost requires a clear ROI based on cost of bad data. Additionally, alert tuning is an area for improvement because initial ML sensitivity requires expert calibration to prevent alert fatigue.

I have been using Sifflet since 2023.

Sifflet transformed our workflow from reactive to proactive. It eliminated the delay between data failure and its detection, catching schema drift and volume anomalies at the ingestion layer. By surfacing these issues before they reached the business dashboard, we effectively eliminated the data surprises and reduced manual forensic auditing by 50-60%.

My main recommendation for anyone adopting Sifflet is to treat it as a strategic data trust investment, rather than just a technical tool. To succeed, you should leverage the AWS Marketplace  to bypass procurement delay and, most importantly, dedicate the first few weeks to fine-tuning alerts on your most critical data sets to prevent alert fatigue and allow the machine learning models to stabilize before scaling the monitoring across your entire enterprise infrastructure. I would rate this product a 9 overall.

Public Cloud

Amazon Web Services  (AWS)

What do you like best about the product?

Sifflet provides an extremely clear view of our data health across all pipelines. The Monitor Coverage panel makes it easy to understand which assets are fully tested and which ones still need attention. I also appreciate the breakdown of monitor types field profiling, metrics validation, custom tests everything is neatly organized. The alerting system is fast and accurate, helping our team detect issues before they impact downstream dashboards.

What do you dislike about the product?

Sometimes the lineage view takes an extra moment to load when working with very large datasets. It’s not a major issue, just something that could be optimized further as we scale.

What problems is the product solving and how is that benefiting you?

Before Sifflet, visibility into data problems was scattered across different tools. Now, everything is centralized: monitoring, incidents, profiling, and reporting. This has improved our SLA response time and significantly reduced the number of unnoticed data failures. Our analysts now trust the data more, and audit processes have become smoother since we can verify data quality instantly.

What do you like best about the product?

Sifflet makes it incredibly easy to detect anomalies early, thanks to its automated monitoring and clean visual dashboards. The platform feels modern, fast, and intuitive even for team members who aren’t deeply technical.

What do you dislike about the product?

Some of the advanced alert configuration options have a learning curve, especially when handling complex pipelines, but once set up, they work flawlessly.

What problems is the product solving and how is that benefiting you?

Sifflet has significantly reduced data downtime and boosted trust in our analytics. Our teams now catch issues in minutes instead of hours, improving the accuracy of reporting across the entire organization.

What do you like best about the product?

Sifflet provides an end-to-end view of data health. The lineage feature is particularly powerful it clearly shows where an issue originates and how it impacts downstream systems.

What do you dislike about the product?

Pricing can feel high for smaller teams, though the overall value justifies the cost for enterprises handling large-scale data.

What problems is the product solving and how is that benefiting you?

It’s significantly reduced reporting errors and improved our audit readiness. We now trust our financial dashboards fully.

What do you like best about the product?

Sifflet gives us deep visibility into our data pipelines and proactively detects anomalies before they affect reports. The dashboard is intuitive, and the integrations with Snowflake and dbt are seamless.

What do you dislike about the product?

The alert settings can be slightly overwhelming for new users a guided setup wizard for different data teams would make it even easier to adopt.

What problems is the product solving and how is that benefiting you?

It saves hours of manual validation and helps us maintain trust in analytics. Our data team can now focus on improvement rather than firefighting errors.