Anomalo Data Quality
AnomaloReviews from AWS customer
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anomalo is our go to tool for data observability
What do you like best about the product?
ease of infrastructure setup, no vendor lock-in, simplicity of use, great feature coverage, outstanding support
What do you dislike about the product?
nothing to dislike, rather eager for even more helpful features
What problems is the product solving and how is that benefiting you?
anomalo is our one-stop-shop for data observability which increased the trust of our consumers in our data a lot. On top, debugging data quality issues got way faster and more convenient.
Use anomalo regularly for airflow dags.
What do you like best about the product?
configurable alerting and data visualization
What do you dislike about the product?
Some charts are a bit hard to interpret for new users
What problems is the product solving and how is that benefiting you?
It make monitoring and debugging much easier.
Convenient fire and forget solution that's saved our butts a few times already
What do you like best about the product?
It's very quick to set up checks. Find table, couple of clicks, punt to Slack channel - all set for many use cases. Good docs too, and we've been fortunate to have a direct comm channel with their product manager and CEO for more specific questions, and they've been super helpful in responding.
What do you dislike about the product?
Some more advanced "power user" techniques are hidden in the Django admin console, and are undocumented.
What problems is the product solving and how is that benefiting you?
Data validation ("functional testing" for data pipelines) without having to set up the infrastructure. Saves a lot of time on this front, and we've found creative use-cases for our business using even the basic checks.
Anomalo has dramatically scaled up our ability to monitor and maintain data quality.
What do you like best about the product?
Anamalo has a strong user experience, offers high quality timeseries models, and translates quickly into impact on data quality. The ability to configure robust data quality checks in just a minute or two makes it feasible to enable monitoring across the entire data ecosystem rather than just a few of the highest importance data assets. Integrations with Slack and PagerDuty also make it easy to use Anomalo directly inside of existing workflows.
What do you dislike about the product?
There are a few minor corner cases that could work more smoothly, and user intro documentation could expand, but in general the product has really high perfomance.
What problems is the product solving and how is that benefiting you?
Anomalo lowers the work necessary to implement ongoing data quality monitoring. It has allowed our data teams to quickly scale up our breadth of data quality checks, which have already picked up a lot of bugs and led to increased confidence in the metrics we surface.
Powerful Anomaly Detection Tool with Simple UI
What do you like best about the product?
Anomalo is a lightweight solution to identify issues with data pipelines as well as "anamolous" events in our data. Setting up appropriate alerts allows for "hands-off" monitoring and for increased visibility into our data with the same resources.
What do you dislike about the product?
Anomaly detection right now seems to be based around a single time series algorithm, in which a trend is assumed with some constant noise. That anomaly detection algorithm doesn't work well now when adding in too much sampling error (i.e., observations based on small n).
What problems is the product solving and how is that benefiting you?
* Anomalo has successfully detected suspicious activity in our platform, giving us the confidence to use it for this purpose in the future.
* Anomalo alerts us to failures in data pipelines.
* Anomalo is allowing us greater coverage in our analytics, not requiring that a user manual view key metrics on our platform.
* Anomalo alerts us to failures in data pipelines.
* Anomalo is allowing us greater coverage in our analytics, not requiring that a user manual view key metrics on our platform.
Anomalo is easy to use, very customizable, and extremely powerful
What do you like best about the product?
Anomalo makes it so that setting up alerts is easy. Its built-in automatic checks cover a wide range of data quality checks. The alerting system does a great job of reducing noise.
What do you dislike about the product?
There is not much to dislike. In the early days, there were some missing features such as resolution workflow and metric dashboard, but the Anomalo team is extremely responsive to feedback and built these features very quickly.
What problems is the product solving and how is that benefiting you?
Our company has a data quality problem and Anomalo is helping the data team tackle it head-on. There are three main categories Anomalo is helping with: North start metric tracking, validating business assumptions, and monitoring software data bugs. The data team now has much better observability within our data warehouse, and we can implement custom alerts for many teams at scale.
Anomaly detection with Anomalo
What do you like best about the product?
Anomalo helps us to, with very low effort, increase the confidence in the data products provided by our data teams. Within a minute Anomalo can be set up to run its checks on a new table. The out-of-the-box checks provided by Anomalo, focused on freshness, completeness and table anomalies, improve the observability of our data. Running Anomalo independent of our data pipelines, provides an additional layer of checks that helps us to increase the trust in our datasets. Additional checks on key metrics and validation rules can be configured through the UI, with no coding required. This enables business users themselves to set up checks according to their needs, without the data team becoming a blocker. Additionally, having an external tool for anomaly detection helps us to apply industry best practices as Anomalo emphasizes nudging users to leverage the tool according to these best practices.
What do you dislike about the product?
Not much, Anomalo is a fairly young company able to quickly adapt to user requests and feedback. Any problems with the tool are usually quickly resolved and we're in good contact with their product team.
What problems is the product solving and how is that benefiting you?
We leverage Anomalo as our second layer of defense against data quality issues. The first layer is implemented within our data pipelines and is focused on the accurateness of our data (is a key unique etc) and has essentially no false-positive alerts. Anomalo takes care of all the checks that can have false positives. From completeness of datasets according to our SLAs, to distributions and anomalies within the datasets provided.
Great Data Anomaly Tool
What do you like best about the product?
I like how consistent Anomalo is at being able to monitor and alert us about any discrepancies that stand out. You're also able to adjust the threshold making it more or less noisy depending on how important the check is. Anomalo has definitely helped my team be more proactive in catching issues.
What do you dislike about the product?
It can be a bit annoying configuring all the tables since you need to configure each one manually. But at the moment I'm not experiencing any pain points that could not be resolved by the Anomalo team
What problems is the product solving and how is that benefiting you?
We're solving the issue of data anomalies and data discrepancies with Anomalo. Our team was always reactive but since utilizing Anomalo, we have been more proactive in catching issues before the issues reach out stakeholders
Recommendations to others considering the product:
If you would like to automate data discrepancy/anomaly checks and work with a responsive support team, I would definitely reccomend Anomalo
Great tool that works well with a great team behind!
What do you like best about the product?
We were able to detect data issues quickly using Anomalo and get the alerts quickly so that we could fix them quickly and move on.
We are using this tool to detect infra issues in data pipelines, data quality issues and even monitoring business KPIs - this is how much we trust this tool!
We are using this tool to detect infra issues in data pipelines, data quality issues and even monitoring business KPIs - this is how much we trust this tool!
What do you dislike about the product?
- Some aspects are not part of the original GUI, so you need to access the Django app to manage them.
- The rollout of new features that impact the env without being able to mark it as a feature flag before it impacts the environment (although the Anomalos team was really there to help and discuss this issue and to find solutions).
- The rollout of new features that impact the env without being able to mark it as a feature flag before it impacts the environment (although the Anomalos team was really there to help and discuss this issue and to find solutions).
What problems is the product solving and how is that benefiting you?
Ability to know that there is a great tool that will detect data issues quickly!
You will spend far less time locating an issue and solving it - no more messages from end-users who noticed a drop in a dashboard graph.
You will spend far less time locating an issue and solving it - no more messages from end-users who noticed a drop in a dashboard graph.
Recommendations to others considering the product:
Just try it!
End user feedback
What do you like best about the product?
Anomalo is a great data visualization tool when it comes to simplifying the process for data pattern exploration and data drift identification.
What do you dislike about the product?
Anomalo could have provided more backend support in terms of easier access for API services, application integration, and providing interactive feedback capabilities for false-positive suppression. I would also like to see Anomalo enhance its capabilities for potential root cause analysis. As an analyst/data scientist, it will be very helpful to provide detailed drill-down capability after a certain anomaly is detected. For example, if a customer SQL detects a pattern drift, it will be extremely helpful if Anomalo could suggest detailed segments that primarily contribute to the pattern drift. Similar to the decision tree concept, without users having to come up with additional diagnostic SQL to understand the root cause, Anomalo can provide potential segmentation nodes to separate the normal population and biased population.
What problems is the product solving and how is that benefiting you?
Anomalo is a great data visualization tool when it comes to simplifying the process for data pattern exploration and data drift identification. As an end-user, I go to Anomalo when I need a quick and easy data visualization to identify a major trend that is currently not very straightforward using other toolkits like Tabluea, python packages, airflow, etc. Anomalo combines the features of trend analysis, data segmentation, scheduling and data visualization capabilities, which come in handy for a data scientist/analyst to leverage in their daily role.
Recommendations to others considering the product:
Anomalo is a great data visualization tool when it comes to simplifying the process for data pattern exploration and data drift identification.
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