Datagaps DataOps Suite Reviews & Product Details

What I appreciate most is its user-friendly interface combined with powerful capabilities. Even for someone who is not deeply technical, it becomes fairly intuitive after initial use. The ability to perform end-to-end data validation across different sources has helped us catch discrepancies early and maintain data integrity. Review collected by and hosted on G2.com.

While DataGaps has been quite helpful overall, there are a few areas where it could improve. Debugging can be challenging at times, especially when you’re trying to trace issues across complex workflows. The process isn’t always as intuitive as I’d expect, and it would benefit from better visibility into what’s happening, along with stronger error-handling mechanisms.

In addition, the flexibility around using parameters in dataflows could be improved. More dynamic and customizable parameter handling would make it easier to support varied use cases and cut down on manual effort.

Another key area is authentication for integrations, particularly for notifications. I’d like to see more modern and secure authentication options—such as Entra ID support—to better align with current enterprise security standards. Review collected by and hosted on G2.com.

What I like most about the Datagaps DataOps suite is the flexibility it offers when working with heterogeneous data sources, and how easily it does so. It provides ease of implementation along with seamless integration with other platforms, such as Databricks, ADO, and GIT. The way it handles large volumes of data comparison is impressive. It is a tool with a great package of features and is capable of performing any sort of automation. Another important aspect is the customer support, which is very prompt and quick in resolving blockers. Review collected by and hosted on G2.com.

I don’t have any major dislikes about the tool. Currently, we manually clean up long-running jobs that are very old but still shown as busy. It would be beneficial if there were an option to automatically clean these jobs. Additionally, it would be helpful to have the ability to create dependencies between dataflows, such that the output of one dataflow can be used as input for another. Review collected by and hosted on G2.com.

Improves confidence in analytics and reporting

Enables faster migrations and upgrades with lower risk

Standardizes data processes across teams and platforms

Supports data-driven decision making with trusted data

Reduces manual checks and custom scripts.

Achieved 98.5% data accuracy across migrated tables.

Measurable improvements in data quality and decision-making capabilities.

DataOps Suite enables reliable, automated, and scalable data operations by embedding quality, observability, and governance directly into the data pipeline—reducing risk,

accelerating delivery, and increasing trust in data. Review collected by and hosted on G2.com.

Change management needed to shift from manual checks to automation. Learning curve for teams new to DataOps practices Review collected by and hosted on G2.com.

Datagaps DataOps Suite integrates seamlessly with ScalaDB, automating data quality checks and validation without extra Scala code. It accelerates ETL testing, ensures accurate data pipelines, and reduces manual effort, helping us maintain trusted, high-quality data efficiently across environments. integration is good with ease in use and implementation Review collected by and hosted on G2.com.

Sometimes, initial setup and configuration can take time, However, once it’s up and running, Datagaps DataOps Suite makes data quality checks effortless and reliable, helping us maintain trusted data with minimal ongoing effort. we use very frequently as it is good in customer support. Review collected by and hosted on G2.com.

I particularly like the recent enhancements in Datagaps DataOps Suite that strengthen mapping manager , data lineage, leverage AI more broadly, and simplify complex data migration validation. Review collected by and hosted on G2.com.

self-paced training modules, video walkthroughs, and certification tracks Review collected by and hosted on G2.com.

DataGaps as a tool has vast features & very comprehensive .

Extreme understanding of Data Quality Testing with tool adds tangible benefit to overall e2e data checks.

Excellent technical support which is very necessary for any tool .

Ease of use & implementation by any junior resource is an added advantage. Review collected by and hosted on G2.com.

I don't have any major dislike as of now only suggestion have more use cases for multiple features would give more confidence to end user Review collected by and hosted on G2.com.

While we've been customers of standalone Datagaps' ETL Validator (ETL V) product for a little over seven years, we recently migrated from ETL V to the DataOps suite (with ETL V & Data Quality) - to take advantage of the more advanced Apache Spark-based platform, inbuilt Python scripting ability as well as extensibility. While there were certainly challenges during migration (as you'd expect with underlying architectural changes), we have had superlative support all through this process.

The web-based interface is fairly easy to use - even for business users to define data rules within the application. Scheduling (of Pipelines, DQ Rules, Data Sources, etc.,) and Email alerts are other things we make use of daily.

The Data Quality (DQ) module is helping us challenge/address the age-old moniker of garbage in/garbage out in the Data Engineering space with its Data Observability, DQ Score, and other metrics. Look forward to sharing these KPIs and metrics more broadly in our organization. Review collected by and hosted on G2.com.

Coming from a one-time purchase (of ETL V) to a subscription-based cost model (of DataOps) certainly needed convincing from our upper management.

The ability to add logic to the type of data differences more granularly and alert only in certain scenarios would be great. Review collected by and hosted on G2.com.

BI Validator for Tableau has enabled us to effectively perform: automated regression testing to speed our DB migrations from Netezza to Teradata, automated regression texting for newly developed tableau content, and performance/stress testing of Tableau content against various DB platforms. This client/server toolset is easy to install, configure, and upgrade as we have performed multiple upgrades since originally purchasing it. UI is clean and easy to navigate. Review collected by and hosted on G2.com.

We have requested and Datagaps engineering are working on the 2 major enhancements that we have requested: (1) build client capabilities into the Web platform to end the need for installation/usage of the client app, (2) create ability to use/select existing Functional Tests as Performance/Stress tests since there is more functionality in defining Functional Tests as opposed to Performance/Stress tests. Review collected by and hosted on G2.com.

I have used it for Tableau report data validation against database. It is doing job efficiently. It is very easy to learn and implement. You name the datasource, Datagaps has support to it. It gives flexibility to install app onprem as well as cloud. Customer support is also good. Though I have not done any integration, but it says it can be integrated to your pipelines easily. I would recommend to use it atleast once for BiTest Automation. Review collected by and hosted on G2.com.

Initially it looks confusing but once you do one POC, you will find it very easy for usage. Review collected by and hosted on G2.com.