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
45 reviews
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Practical Addon, but Slow Support
What do you like best about the product?
I like the fact that the Sifflet web addon allows easy access to information from Power BI. It is directly available, which means I don't need to access a new platform, as it opens directly with Power BI.
What do you dislike about the product?
Things that sometimes seem basic do not work very well. Support is very slow, sometimes taking several months to get a fix. Easy to initialize, but clearly buggy in certain areas.
What problems is the product solving and how is that benefiting you?
I use Sifflet for the centralization and access to knowledge in data governance. The web addon is convenient because it is available directly in PowerBI without requiring a new platform.
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)
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
From traditional data quality to agile data oservability
What do you like best about the product?
Rely on machine learning to discover data and catch data outliers, anomalies and trends.
Ease of use + ease of Integration + ease of monitor implementation.
Ease of use + ease of Integration + ease of monitor implementation.
What do you dislike about the product?
a point to improve is to accelerate the training of the embeded machine learning module. Maybe sifflet team can be more reactive with this point and assist the client to reach quick result.
What problems is the product solving and how is that benefiting you?
monitoring data quality issues.
raise alerts when data pipelines fail to execute with success.
track data freshness and implement data quality rules.
raise alerts when data pipelines fail to execute with success.
track data freshness and implement data quality rules.
A useful tool for the modern data stack
What do you like best about the product?
Provides an easy way to get an understanding of your data landscape by seeing lineage and relationships between data sources, dbt data models and looker explores
What do you dislike about the product?
Some integrations could be stronger - for example, some dbt lineages are not always fully accurate, many api calls to Looker
What problems is the product solving and how is that benefiting you?
- Data lineage
- Monitoring data sources
- Monitoring data sources
A friendy user interface that could become more friendly with some improvements
What do you like best about the product?
Capacity to create&deploy DQ monitor rules easily from UI or using deploy as code module
Capacity to add multiple tag values on any DQ monitor rules to facilitate filtering criteria based on those tags values, asset, severity values..
Capacity to use both search bar criteria (status of last DQ moniror runs combined with some predefined attributes such as severity, last run date..and free text to type to search for Monitor names).
Capacity to pin any DQ monitor or Asset to get a bookmark access from Dashboard pane
Capacity to get for each incidents the detailed list of compromised Dashboards (Power BI reports in our case)
Capacity to add multiple tag values on any DQ monitor rules to facilitate filtering criteria based on those tags values, asset, severity values..
Capacity to use both search bar criteria (status of last DQ moniror runs combined with some predefined attributes such as severity, last run date..and free text to type to search for Monitor names).
Capacity to pin any DQ monitor or Asset to get a bookmark access from Dashboard pane
Capacity to get for each incidents the detailed list of compromised Dashboards (Power BI reports in our case)
What do you dislike about the product?
Data lineage module should be enriched by adding to filter pane :
- Capacity to expand in one click all assets linked to initial targeted asset in order to get a full picture of upstream and downstream linked assets.
- Capacity to view for each existing DQ monitor type (ReferentialIntegrity, DuplicatePercentage..) corresponding consolidated number of incidents present on targeted asset and ideally from filter pane possibility to refine incident number per type of monitor run we want to highlight on targeted asset and also possibility to refine each consolidated DQ monitor incident type number per severity level.
- On Incident module possibility to group into one incident multiple distinct DQ monitor alerts that are concerning same asset but on distinct columns for instance but applying to one common dimension value (country for instance) in order to mutualize all of these incidents into one unique ticketing creation process and root cause analysis to address to asset owner.
- Possibility to put on hold or snooze mode recurring DQ monitor alert on same asset and same grouping dimension value that is repeating over and over again on a daily basis if error threshold value is quite identical from one day to another.
- Capacity to expand in one click all assets linked to initial targeted asset in order to get a full picture of upstream and downstream linked assets.
- Capacity to view for each existing DQ monitor type (ReferentialIntegrity, DuplicatePercentage..) corresponding consolidated number of incidents present on targeted asset and ideally from filter pane possibility to refine incident number per type of monitor run we want to highlight on targeted asset and also possibility to refine each consolidated DQ monitor incident type number per severity level.
- On Incident module possibility to group into one incident multiple distinct DQ monitor alerts that are concerning same asset but on distinct columns for instance but applying to one common dimension value (country for instance) in order to mutualize all of these incidents into one unique ticketing creation process and root cause analysis to address to asset owner.
- Possibility to put on hold or snooze mode recurring DQ monitor alert on same asset and same grouping dimension value that is repeating over and over again on a daily basis if error threshold value is quite identical from one day to another.
What problems is the product solving and how is that benefiting you?
SIFFLET provides an unified platform to collect assets from distinct environment and technology (database, dashboarding solution) in order to check impact of any DQ monitor breach on all of our kind of assets and this analysis can be segregated per specific dimension such as country or solution.
It provides also some data cataloging module to provide some semantic and business logic to our existing data asset.
It provides also some data cataloging module to provide some semantic and business logic to our existing data asset.
Sifflet features
What do you like best about the product?
The features that Sifflet offered was really good.
What do you dislike about the product?
No support to integration with Informatica cloud
What problems is the product solving and how is that benefiting you?
Data Quality issues
My Sifflet Story
What do you like best about the product?
User interface
Monitoring section
Rules for consistency, completenes, accurecy
Incident assign and resoultions
Monitoring section
Rules for consistency, completenes, accurecy
Incident assign and resoultions
What do you dislike about the product?
Not much but recently when we were trying sifflet we didnot see the query or code which broke that rules or the rows which failed to comply with that rules
What problems is the product solving and how is that benefiting you?
Basically, Common data quality issues include missing values, duplicate records, incorrect data formats, inconsistent data values, outdated information, and data entry errors.
Helpful in Identify the root causes of data quality issues by analyzing data sources, processes, and systems.
Today we got immediate alert for one of the table went empty then we resolved the issue ASAP
Helpful in Identify the root causes of data quality issues by analyzing data sources, processes, and systems.
Today we got immediate alert for one of the table went empty then we resolved the issue ASAP
Useful in spotting problems and setting multiple monitors
What do you like best about the product?
I am a data engineer in charge of data quality in my company and, with Sifflet, I am able to perform multiple quality checks (nulls, seasonality patterns, invalid values...) very easily and quickly.
So far, after a few days of usage, I have spotted a few problems (for instance, invalid regex) that were under the radar.
So far, after a few days of usage, I have spotted a few problems (for instance, invalid regex) that were under the radar.
What do you dislike about the product?
The main problem with Sifflet for me, is the number of available monitor templates, which can be overwhelming for new users. I would say the learning curve is rather steep for Sifflet.
What problems is the product solving and how is that benefiting you?
Problems:
- Data quality (assuring data conformity and compliance with business rules)
- Data observability (make sure we process consistent volume of data daily for our import/export flows)
Benefits (so far):
- Spotting data problems (high number of null values, low volume of processed/ingested data)
- Data quality (assuring data conformity and compliance with business rules)
- Data observability (make sure we process consistent volume of data daily for our import/export flows)
Benefits (so far):
- Spotting data problems (high number of null values, low volume of processed/ingested data)
Sifflet
What do you like best about the product?
The way that you can easily visualise the whole data pipeline and explain where metrics come from easily
What do you dislike about the product?
I've only been using Sifflet for a short time and haven't found any downsides yet
What problems is the product solving and how is that benefiting you?
De-mystifying the data pipeline. I am the only analyst in my team, so being able to show the pipeline in a manner that is simple to understand really helps me communicate issues/projects more easily
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