Sifflet
SiffletReviews from AWS customer
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Very Useful but Not Plug and Play
What do you like best about the product?
Sifflet’s strength is in how deep it goes. You get insight into every part of your pipeline, and it scales well across regions and departments. We use it to align our governance framework with actual data behavior, which is a huge benefit. Lineage, anomaly detection, rules — it’s all there.
What do you dislike about the product?
Setup was not easy for us. Took weeks to get all integrations running. There’s a learning curve, especially if your environment is complex. More tutorials or a sandbox mode would help new admins practice.
What problems is the product solving and how is that benefiting you?
Whistle helps us bridge the gap between our governance policies and how data actually flows in real-time. Before using Whistle, identifying anomalies or broken pipelines across departments was time-consuming and mostly reactive. Now we can detect and address issues proactively using monitoring and rules-based alerts. It has helped us standardize data behavior across teams, enforce governance automatically, and improve incident response time significantly.
Solid Tool, Still Growing
What do you like best about the product?
The data freshness checks and automated anomaly rules help me make sure our reports are compliant with internal policies. I don’t need to chase people down anymore when something breaks. The alerts just pop into Slack, and it’s clear who owns what. I also appreciate the audit trail.
What do you dislike about the product?
Some of the UI feels a bit too advanced for casual users. I’ve had to walk some teammates through it. Also, I'd like to see tighter integration with Teams since not all of us use Slack.
What problems is the product solving and how is that benefiting you?
Helps enforce data freshness policies automatically, saving time and reducing manual follow-ups.
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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