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.
Sifflet
SiffletExternal reviews
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Automated data monitoring has transformed visibility and now prevents silent failures in our lake
What is our primary use case?
What is most valuable?
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.
What needs improvement?
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.
For how long have I used the solution?
I have been using Sifflet since 2023.
What other advice do I have?
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.
Which deployment model are you using for this solution?
Public Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Amazon Web Services (AWS)
Reliable Data Quality Platform With Strong Monitoring Coverage
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.
A Reliable Data Observability Tool That Gives Us Full Visibility
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.
Reliable Tool for Data Quality and Governance
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.
Comprehensive Data Observability with Smart Alerts
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.
Streamlined Our Data Workflows and Boosted Confidence
What do you like best about the product?
Sifflet has made managing data quality incredibly simple. The automated checks and alerting system help us detect inconsistencies instantly, without relying on manual audits. I also appreciate how well it integrates with our data warehouse setup took less than a day, and we’ve been running smoothly ever since. The lineage visualization is extremely useful for explaining data flow to business users.
What do you dislike about the product?
Honestly, not much to complain about. Sometimes, the refresh rate for larger dashboards could be a bit faster, but that’s expected with detailed monitoring tools.
What problems is the product solving and how is that benefiting you?
Before Sifflet, verifying data freshness across different sources used to take hours. Now, it’s automatic the tool notifies us if a data pipeline breaks or an update is delayed. It’s improved both speed and reliability of our reporting, which has been a major win for the analytics team.
A Smart Platform That Catches Data Issues Before We Do
What do you like best about the product?
Sifflet stands out for its proactive monitoring. It alerts us about potential data quality issues before they cause any disruption. I love the way it visualizes anomalies you can instantly see when and where something went wrong. The interface feels very natural, and setting up monitors for new data assets takes just a few minutes.
What do you dislike about the product?
The only downside is that the alert configuration panel could use a bit more flexibility for complex workflows. Still, it’s a small issue considering how much value we get overall.
What problems is the product solving and how is that benefiting you?
Before using Sifflet, identifying data drift or broken transformations was mostly reactive. Now, the system automatically flags irregularities and provides detailed insights so our engineers can respond faster. It’s improved trust in our analytics and reduced incident investigation time by nearly half.
Gaining Confidence in Our Data Pipeline with Sifflet
What do you like best about the product?
Sifflet gives us a unified and transparent view into our entire data pipeline. The anomaly detection, freshness checks, and lineage visualization work seamlessly together, so we no longer have to jump between multiple tools. The real-time alerts and clean dashboard have helped our team catch issues before they affect downstream reports.
What do you dislike about the product?
The initial setup in our complex environment took longer than we expected. Some integrations and custom monitor configurations required trial and error, and a few parts of the UI/dashboard customization could be improved.
What problems is the product solving and how is that benefiting you?
Earlier, our data quality issues often surfaced too late impacting business decisions and requiring emergency fixes. With Sifflet, we are now proactively monitoring data health, tracing root causes through lineage, and collaborating better across business and engineering teams. This has improved stakeholder trust, reduced incident response time, and freed up engineers for higher-value work.
Reliable Data Quality Control for Our Entire Team
What do you like best about the product?
Sifflet makes it incredibly easy to monitor and maintain data quality across multiple data pipelines. I really like the way it centralizes all data incidents, coverage, and tests into one unified dashboard. The automated alerting feature is a huge plus our team gets notified instantly when an issue arises, which saves us hours of manual checking each week.
What do you dislike about the product?
Some configuration options for custom monitors could be a bit more flexible. It’s not a big problem, but adding more templates for specific data types would make setup even faster for large teams.
What problems is the product solving and how is that benefiting you?
We use Sifflet to ensure data consistency across our analytics workflows. Before using it, we struggled with undetected schema changes and missing records. Now, everything is tracked and verified automatically. It’s given our engineers and analysts more confidence in the data and significantly reduced time spent debugging issues.
Reliable Data Observability with Excellent Alerting and Automation
What do you like best about the product?
Sifflet gives us full visibility into our data pipelines with minimal setup. The anomaly detection is smart and actually useful we catch issues before they affect reports. Integrations with Snowflake, dbt, and Looker were smooth, and the alert system via Slack keeps everyone updated in real time.
What do you dislike about the product?
The UI could use a few more customization options for dashboard layouts, but overall, it’s clean and easy to use.
What problems is the product solving and how is that benefiting you?
Before Sifflet, data issues would go unnoticed until someone reported them. Now, our team detects and resolves anomalies automatically. This has improved data trust, reduced downtime, and saved hours every week that used to be spent troubleshooting broken pipelines.
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