Anomalo Data Quality
AnomaloReviews from AWS customer
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A simple data-observability tool.
What do you like best about the product?
The major factor I loved about the tool is the simplicity of using it. The interface is super easy to use. The tool could be customised as per the usability of it and as per a user.
What do you dislike about the product?
The tool's option to connect to different teams is a significant drawback. The device cannot control the scanned data, helping decide the table's credibility. Hope it gets resolved.
What problems is the product solving and how is that benefiting you?
It helped majorly to detect the gaps between large data volumes.Clarity over different anamolies was a pleasure using the tool. And to add again the simplicity of the tool helped when it came to understanding the entire use of the tool.
Anomalo is an easy-to-use and powerful data observability tool.
What do you like best about the product?
Clear and intuitive interface
Low entry threshold. Using Anomalo does not require many hours of study and training.
Beautiful data graphs, very helpful when analyzing data and detecting any anomalies
Setting up a new table takes 1 minute and there are 8 built-in checks right from the start
There is a large variety of custom checks
Low entry threshold. Using Anomalo does not require many hours of study and training.
Beautiful data graphs, very helpful when analyzing data and detecting any anomalies
Setting up a new table takes 1 minute and there are 8 built-in checks right from the start
There is a large variety of custom checks
What do you dislike about the product?
In its simplicity, Anomalo sometimes seems to be limited and lacks certain functionalities.
For example:
- Tools and functionality to support multiple teams are missing, such as sending notifications for the same table to different destinations. Possibility of adding comments by different teams or tagging tables.
- There are no tools to control the amount of scanned data (to be able to control costs) to decide whether a given table is worth keeping in Anomalo at all
For example:
- Tools and functionality to support multiple teams are missing, such as sending notifications for the same table to different destinations. Possibility of adding comments by different teams or tagging tables.
- There are no tools to control the amount of scanned data (to be able to control costs) to decide whether a given table is worth keeping in Anomalo at all
What problems is the product solving and how is that benefiting you?
Thanks to Anomalo and transparent charts from the Data Freshness and Data Volume sections, we were able to repeatedly detect data gaps for various days, including historical ones.
In addition, we can clearly see any anomalies related to the amount of processed data for the table for a specific day.
Anomalo gives us, the Data Engineers team, a sense of control and security.
In addition, we can clearly see any anomalies related to the amount of processed data for the table for a specific day.
Anomalo gives us, the Data Engineers team, a sense of control and security.
Pulse Dashboard in Anomalo platform is pivotal for health checks & improving our data quality drives
What do you like best about the product?
With Anomalo, we could easily focus on improving coverage for our important tables & passing metric checks. We can obtain a deep dive into any statistics so that we have clarity on what should be our priority. This also gives visibility to tables that impact our SLA by overviewing their data arrival time in our dashboard.
What do you dislike about the product?
We have recently started using Anomalo to identify data impasses & determine its RCA quickly. We would like to have proper documentation for its features so that it would be easy for us to get familiar with its platform. We integrate Anomalo with our customer data warehouse to perform seamless data quality checks and regulate alerts in case of anomalies.
What problems is the product solving and how is that benefiting you?
Anomalo's Pulse dashboard is brilliant in regard to unsupervised detection, where even slight change anomalies are determined in our customer datasets. We can conveniently devise our dynamic testing approaches irrespective of the data quality conditions in its platform. Investigation in case of production issues is also accurate as we can expand on any data structure in its Pulse dashboard. It also effectively prevents abnormal schema changes and dataset missouts which usually cause unwanted turmoils.
Quick insights on data quality
What do you like best about the product?
Easy to configure: checks automated & custom
What do you dislike about the product?
Not easy to do bulk configurations for entire database
What problems is the product solving and how is that benefiting you?
Nightly checks on data allow scientists to fix data issues before decisions are made, gives a sanity check on model output
Recommendations to others considering the product:
It is a great platform
Anomalo Data Quality Tool
What do you like best about the product?
Administration. Previously worked on another vendor's data quality tool which required constant attention. Anomalo's architecture freed me up to do other things.
What do you dislike about the product?
I didn't encounter any dislikes with Anomalo.
What problems is the product solving and how is that benefiting you?
It is helping to expose some gaps we have in storing data, specifically around audit columns (i.e create_date and last_update_date).
Improving data quality through Anomalo
What do you like best about the product?
- Out-of-the-box data quality checks such as freshness, anomalous changes to record counts and nulls, and more.
- Integration with PagerDuty
- Auto-visualizations
- Integration with PagerDuty
- Auto-visualizations
What do you dislike about the product?
- Adding more validation checks and duplicating across tables is more manual than I prefer
- Some visualizations are not always self-explanatory
- Organization of content, such as tables and views
- Some visualizations are not always self-explanatory
- Organization of content, such as tables and views
What problems is the product solving and how is that benefiting you?
- Data quality dashboard for our tables/views
- Centralized visual location for previously manual or coded checks
- Quick configurations and access for team members
- Centralized visual location for previously manual or coded checks
- Quick configurations and access for team members
Anomalo provides a great solution for data teams to monitor data quality
What do you like best about the product?
- Anomalo provides a solution that we can easily integrate with our data stores (Snowflake, Databricks Delta) and immediately start to configure data quality checks for all the tables that we care about.
- Anomalo in general is very easy to use, yet it is also comprehensive. It allows us to configure almost any key metrics and validation rules in SQL based on what we need to monitor for data quality. It provides integration with Okta, Slack, PagerDuty which are the tech stacks that we use in Opendoor, therefore onboarding Anomalo is quite a smooth experience.
- In addition to the UI, Anomalo also provides a Python SDK which enables a ton of flexibility to integrate Anomalo into building data pipelines and triggering data quality checks on the fly.
- Anomalo's team has been quite responsive to our questions and requests. We have regular office hours to meet with Anomalo's team to discuss questions and so far it has been very helpful every time.
- Anomalo in general is very easy to use, yet it is also comprehensive. It allows us to configure almost any key metrics and validation rules in SQL based on what we need to monitor for data quality. It provides integration with Okta, Slack, PagerDuty which are the tech stacks that we use in Opendoor, therefore onboarding Anomalo is quite a smooth experience.
- In addition to the UI, Anomalo also provides a Python SDK which enables a ton of flexibility to integrate Anomalo into building data pipelines and triggering data quality checks on the fly.
- Anomalo's team has been quite responsive to our questions and requests. We have regular office hours to meet with Anomalo's team to discuss questions and so far it has been very helpful every time.
What do you dislike about the product?
- The way that Anomalo presents the metric results can potentially be improved. One significant inconvenient issue that we observed, is that if there is a table has a lot of "segments", then any metrics graph based on segment is pretty hard to digest because the segments and their metric values are not sorted based on segment names. The segment names are also not searchable because the metric graph is an image.
- Anomalo UI user experience can be further improved. Sometimes these use cases can be hard to find out unless Anomalo's team really understands how the customer is using the solution.
For example, there is currently not a great way to view the history of a single check. The best thing to do is to go to the URL /dashboard/check_runs//runs?sort=-started_at but notice that there is a "" in the URL instead of a "". This means that the history is showing all the runs for a particular check up to the run_id specified in the URL, and therefore there is not a stable URL to see the history.
- Anomalo doesn't have a solution for data lineage visualization yet. While this is not super critical in my company (other than sometimes we need to know if deleting a table could cause any downstream impact), it can be an important functionality that is needed for other companies. Some companies in the data observability area provides lineage such as Monte Carlo.
- Anomalo UI user experience can be further improved. Sometimes these use cases can be hard to find out unless Anomalo's team really understands how the customer is using the solution.
For example, there is currently not a great way to view the history of a single check. The best thing to do is to go to the URL /dashboard/check_runs/
- Anomalo doesn't have a solution for data lineage visualization yet. While this is not super critical in my company (other than sometimes we need to know if deleting a table could cause any downstream impact), it can be an important functionality that is needed for other companies. Some companies in the data observability area provides lineage such as Monte Carlo.
What problems is the product solving and how is that benefiting you?
Anomalo is solving our team's growing needs to monitor data quality. Our team previously tried solutions such as Great Expectations, but it is not super flexible (e.g. the threshold of whether there is a data quality issue or not has to be hardcoded) and it requires writing code to configure data quality checks. Our team has just started configuring Anomalo for all our production tables and we are confident that it will bring very positive benefits.
Still in its early stages but a great product considering where it's at
What do you like best about the product?
Automated anomaly detection makes my life as a data engineering professional way easier, especially managing high-visibility datasets where important failures are not just a matter of the ETL failing, but of the contents of the table changing meaningfully. It's super easy to set up good alerts that detect all kinds of undesired changes or issues in the data. The UI is very intuitive and clear. The reporting/analysis tools are excellent. The triage system is simple but effective.
What do you dislike about the product?
A lot of features outside of the core anomaly detection piece are under-developed and make the tool less useful than it could/will be. Ideally you could send alerts to more than one slack channel or destinations outside of Slack using just the UI. The API is clumsy (ex. it's very difficult to update just one parameter in the config for a table with an existing config) and not fully documented. Datadog integration is a dealbreaker for some teams.
What problems is the product solving and how is that benefiting you?
With the Great Resignation and hypergrowth in a until-recently-start-up environment, there is more data at our company than can be easily monitored for quality without good tooling. Before Anomalo the tooling we had only covered failures in ETLs and very basic issues like if there was an unexpectedly small number of records in a table. Anomalo has made our whole team more efficient by identifying more kinds of issues automatically.
Recommendations to others considering the product:
Save google sheets with table config IDs when building configs!
A flexible, powerful tool for data teams
What do you like best about the product?
Anomalo has helped Faire's data organization maintain high-quality data models and pipelines that are key to effective operation. Anomalo diagnoses data quality issues efficiently, providing well-designed automatic alerts integrated into Slack with data visualizations that highlight critical trends and potential errors that require attention. Catching errors that may have otherwise gone unnoticed for days if not weeks has been a major benefit of setting up alerts on tables in our data warehouse. Our cross-functional analytics, product, and engineering partners appreciate and engage with Anomalo's intuitive alerts, which have sparked discussions that have led to better outcomes and resolutions for the data team, our customers, and the larger business.
What do you dislike about the product?
There are some customization options we would like to have in Anomalo to fine-tune their product to our organization's use cases that are not available just yet, including integration into our engineering DB and additional team-based functionalities. However, the Anomalo team has been supportive and engaged in actioning many of our previous feature requests into fully fledged features, or suggested adaptations that have worked quite well for our organization.
What problems is the product solving and how is that benefiting you?
Anomalo validates the necessary properties of ETL inputs and outputs reducing the time data and analytics team members need to spend hunting down and correcting issues.
Anomalo has improved the trust and reliability of data products for downstream consumers – and even empowered consumers to set up their own checks on aspects of the data model relevant to their work.
Anomalo tracks and alerts any metric or segment level metrics in an intelligent manner on a daily interval, alerting on conditions we specify when setting up checks and reducing the need to manually check as many dashboards.
Anomalo has improved the trust and reliability of data products for downstream consumers – and even empowered consumers to set up their own checks on aspects of the data model relevant to their work.
Anomalo tracks and alerts any metric or segment level metrics in an intelligent manner on a daily interval, alerting on conditions we specify when setting up checks and reducing the need to manually check as many dashboards.
Simple yet powerful data quality monitoring for your data warehouse
What do you like best about the product?
The Anomalo platform makes it very easy to create comprehensive monitoring for your data warehouse. The product provides a simple way to set up powerful data quality tests that would be difficult or impossible to implement in other products.
What do you dislike about the product?
The product is lacking maturity in terms of integrations with other systems, e.g., for logging and aggregating alerts. The documentation is also occasionally lagging behind the feature set.
What problems is the product solving and how is that benefiting you?
The advanced data quality tests in Anomalo help us find issues in our data that would otherwise go unnoticed or be detected at a later time, when damage may already be done.
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