I have been using Datafold for one year. I use Datafold in a consulting context to validate changes in the data model and the DI pipeline. I used Datafold in one project where our team safely refactored key revenue reports built on top of our customer data warehouse. Before merging any DBT or SQL changes, Datafold automatically compared the old and new version of the main fact table. I used Datafold on the customer side, and they are using Datafold on cloud data.
Datafold
DatafoldExternal reviews
24 reviews
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Automated data quality checks have reduced errors and saved significant validation time
What is our primary use case?
What is most valuable?
Datafold offers many features, but the best one for me is that it extracts you from data difficulties, data quality, and observability challenges. It is a data quality and observability platform, and we use Datafold for that purpose.
The data quality feature is helpful in understanding missing values or identifying issues in the data, which stands out to me about those capabilities. A lot of positive impact has come from Datafold in my organization because we needed a data observability tool to observe our data flows and ensure good data quality in our data warehouse and customer data.
It reduced many errors and also reduced manual impact by automating a lot of work.
What needs improvement?
Datafold can be improved mainly on usability, scale, broader integration, and pricing flexibility.
For how long have I used the solution?
I have been working in my current field since 2014.
What do I think about the stability of the solution?
Datafold is stable in my experience.
What do I think about the scalability of the solution?
I think Datafold has good scalability, though I am not completely certain about its full scalability capabilities.
How are customer service and support?
I have not yet interacted with their support team, but may do so in the future.
How would you rate customer service and support?
Which solution did I use previously and why did I switch?
We previously used a custom solution, but it was not suitable for the customer, which is why we switched to Datafold.
What was our ROI?
We have seen a return on investment from using Datafold with a 30% time saved.
What other advice do I have?
I advise using Datafold to ensure good data quality and observability, and to integrate Datafold into the CI/CD pipeline. We use AWS, for example, as our cloud provider. I rate this product a 9 out of 10.
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)
Right one for testing
What do you like best about the product?
Awesome workflow, which is really a great feature of Datafold. Traditional way of doing data transfers is laborious. But this one helps to the best of capabilities. My fellow team was impressed.
What do you dislike about the product?
Nothing as such to say about negative here. But breadth of usage can be enhanced. This is not a drawback for sure. May be in coming days, will explore more and revert. For now all good
What problems is the product solving and how is that benefiting you?
Integration of data and managing data quality are two things that pose a big challenge in front of us. But Datafold made it easier to look and saved our time and effort
Review for Datafold
What do you like best about the product?
Makes life easier with SQL code reviews helping find the hidden changes we made we dint know to our data.It gives ability to quality check the things in our own way with very much lesser errors compared to manual testing.
What do you dislike about the product?
Nothing in particular, a brief guide with documentation would have been justified.
Helps in data managing of huge chunks of table, rows and records of data in day to day usage.
Helps in data managing of huge chunks of table, rows and records of data in day to day usage.
What problems is the product solving and how is that benefiting you?
Makes life way easier for automated testing
Data accuracy and quality kpi achievement.easily integrates with modern data stacks such as Amazon redshift, snowflake,gitlab and GitHub
Data accuracy and quality kpi achievement.easily integrates with modern data stacks such as Amazon redshift, snowflake,gitlab and GitHub
Used to get the real data.
What do you like best about the product?
Datafold helps data teams to see how every code change impacts data and data applications.
What do you dislike about the product?
There is nothing to say negativity about Datafold because its very useful
What problems is the product solving and how is that benefiting you?
It solves common data pipeline problems and provides a comprehensive suite of data testing and monitoring tools.
Automate data testing and improve data quality with DATAFOLD
What do you like best about the product?
Datafold offers the best solution to solve data quality issues. It automates data testing, can easily integrate Datafold into Github and every change to SQL code can be validated by the API automatically. Not only Github can also be integrated with many other data stacks.
What do you dislike about the product?
If there was an option for Data analytics along with this it could have been better, Must add support for NoSQL data.
What problems is the product solving and how is that benefiting you?
Integrated Datafold API with GitHub which resulted in an improvement in the quality of data and also automated data testing saved a lot of time.
A cheap Data Observing tool
What do you like best about the product?
It helped to fix issues which gave insight to correct practices.
What do you dislike about the product?
It's a relatively new tool and doesn't have a vast community. Still a WIP.
What problems is the product solving and how is that benefiting you?
It helped to clearly see data under one roof, which helped production hours.
A Powerful Tool for Ensuring Data Quality and Consistency
What do you like best about the product?
User-friendly interface: Datafold's drag-and-drop interface is easy to use and requires minimal training, allowing teams to quickly get started with data testing and monitoring.
Comprehensive data lineage tracking: Datafold provides a complete view of the data pipeline, making it easy to track the source of issues that arise.
Real-time alerts and visualizations: Datafold alerts users in real-time of any anomalies or issues that occur, allowing them to quickly address and resolve problems.
Pre-built tests and checks: Datafold includes a range of pre-built tests and checks that can be used to ensure data quality and consistency, saving time and effort for data teams.
Comprehensive data lineage tracking: Datafold provides a complete view of the data pipeline, making it easy to track the source of issues that arise.
Real-time alerts and visualizations: Datafold alerts users in real-time of any anomalies or issues that occur, allowing them to quickly address and resolve problems.
Pre-built tests and checks: Datafold includes a range of pre-built tests and checks that can be used to ensure data quality and consistency, saving time and effort for data teams.
What do you dislike about the product?
Limited integration options: Datafold currently has limited integration options with third-party tools, which may be a drawback for teams that rely on a range of data tools and platforms.
Limited functionality for non-SQL data: Datafold is primarily designed for SQL-based data pipelines, which means that teams working with non-SQL data may need to find alternative tools or workarounds.
Limited control over testing and monitoring: Some users have reported that Datafold's pre-built tests and checks can be too rigid and inflexible, which may limit teams' ability to customize testing and monitoring to their specific needs.
Limited functionality for non-SQL data: Datafold is primarily designed for SQL-based data pipelines, which means that teams working with non-SQL data may need to find alternative tools or workarounds.
Limited control over testing and monitoring: Some users have reported that Datafold's pre-built tests and checks can be too rigid and inflexible, which may limit teams' ability to customize testing and monitoring to their specific needs.
What problems is the product solving and how is that benefiting you?
The benefits of using Datafold can include improved data quality, increased efficiency, and reduced risk of errors and data inconsistencies. By solving common data pipeline problems and providing a comprehensive suite of data testing and monitoring tools, Datafold can help teams to more effectively manage their data and achieve their business objectives.
Datafold experience
What do you like best about the product?
Easy to use, some task is easily doable.
What do you dislike about the product?
some time it takes more time, need to resolve
What problems is the product solving and how is that benefiting you?
validate dbt model changes during development, deployment & migrations. Compare data differences during development
Great data catalog and lineage tool for medium-size teams
What do you like best about the product?
It's easy to start with the tool. Datafold team provides excellent support - always reachable and ready to help. They offer a reasonable pricing model.
What do you dislike about the product?
While generally, UI is quite good, it has a few minor bugs/glitches and sometimes feels quite raw. Though as it's mentioned above, the support team is always there to collect feedback and follow up on issues.
What problems is the product solving and how is that benefiting you?
While the tool is famous for its "Data Diff" feature, we mainly use Data Catalog and Lineage features. It helps us move our "tribal knowledge" about the data to a centralized place and use Datafold as a single source of truth about the data meaning at our company.
Recommendations to others considering the product:
Works well with Redshift, easy to set up
A key DataOps tool
What do you like best about the product?
Although Datafold provides several features, their "data diff" and CI integration (Github) is the one that stands out the most. I don't know of any other commercial or open-source dataOps tool which provides this feature or the integration as nicely as Datafold does.
What do you dislike about the product?
Nothing stands out as a "dislike". It provides products and functionality which does what they claim.
What problems is the product solving and how is that benefiting you?
Datafold helps solve several problems with benefits such as 1) Datafold Datadiff CI with Github surfaces data value and schema downstream issues before it hits production, 2) Datafold Alerts with Slack monitors for known potential issues, data volume anomalies, and business rules and 3) Datafold Column Lineage provides excellent ad hoc query insights into where and how of individual column usage throughout our dbt models.
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