Sifflet
SiffletReviews from AWS customer
0 AWS reviews
-
5 star0
-
4 star0
-
3 star0
-
2 star0
-
1 star0
External reviews
44 reviews
from
External reviews are not included in the AWS star rating for the product.
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.
Powerful Data Observability for Modern Data Teams
What do you like best about the product?
- Intuitive and user-friendly interface, accessible for both technical and non-technical users.
- Comprehensive end-to-end data lineage and impact analysis, making root cause identification fast and clear.
- Flexible integration with a wide range of data sources, warehouses, and BI tools.
- Automated metadata management and cataloging, streamlining data discovery
- Comprehensive end-to-end data lineage and impact analysis, making root cause identification fast and clear.
- Flexible integration with a wide range of data sources, warehouses, and BI tools.
- Automated metadata management and cataloging, streamlining data discovery
What do you dislike about the product?
- Initial setup and configuration can be time-consuming, especially for complex data environments
- Limited customization of certain dashboard visualizations and data lineage
- Limited customization of certain dashboard visualizations and data lineage
What problems is the product solving and how is that benefiting you?
Data Quality Issues Go Undetected: monitoring automatically detects anomalies, schema changes, and quality issues before they impact downstream users
Lack of End-to-End Data Lineage: Sifflet provides comprehensive data lineage (in some ways better than dbt), making it easy to trace data flows, dependencies, and impacts across the stack
Siloed Data Discovery and Poor Collaboration: the data catalog and discovery features centralize metadata, enabling better discovery and collaboration
Lack of End-to-End Data Lineage: Sifflet provides comprehensive data lineage (in some ways better than dbt), making it easy to trace data flows, dependencies, and impacts across the stack
Siloed Data Discovery and Poor Collaboration: the data catalog and discovery features centralize metadata, enabling better discovery and collaboration
showing 1 - 10