Data testing is often assumed to be a solved problem, but standard capabilities — data access, quality rules, and comparison methods — only cover about 75% of what enterprises actually encounter in production. The remaining gap shows up in complex APIs, billion-row datasets, and anomalies no one thought to write a rule for. This blog breaks down the 6 critical components — extensibility, advanced API handling, AI-based observability, large volume handling, DevOps integration, and RPA integration — that close that gap and make data testing enterprise-ready.
Key Takeaways
- Extensibility matters — Python-based plugins let teams solve unexpected data issues without workarounds.
- APIs are essential — Complex sources (e.g., hierarchical JSON via multiple APIs) need advanced API handling.
- AI + rules beat rules alone — Combining Data Quality rules with AI-driven Observability catches both known and unknown issues.
- Scalability and integration close the gaps — Handling billion-row volumes (DB engine or Spark) plus tight DevOps/RPA integration rounds out enterprise-grade data testing.
Importance of Data and Data Testing
Data is a precious asset that has to be validated at various stages of use. One stage is at the point of ingestion, and another as it moves through your enterprise and lands in your data warehouse or data lake. Finally, when it is consumed in your data analytics platform. This is from the point of view of analyzing data.
What about all of the production data that you have in the enterprise?
How is that going to be monitored?
So, table stakes for data testing start with access to all the data in your environment, whether in your analytics platform or stored within your production applications. Along with the data access, data quality rules have to be available, as well as a method of comparing data sources of like or mixed data structures and varying volumes, often in the billions.
- With these core capabilities, you can develop good testing workflows that take care of 75% of your testing needs.
- But what about the other 25%?
- What if your data is in complex hierarchical JSON structures?
- What if the data testing needs are not anticipated and solved?
The last 25% brings about the 6 critical components where you can solve those unexpected needs.
Here are the 6 critical components
| Component | Why It Matters |
|---|---|
| Extensibility | Lets teams resolve unanticipated data problems via Python or other extensible methods, without complex workarounds |
| Advanced API Components | Handles data access via APIs, including complex hierarchical JSON structures requiring multiple calls |
| AI-Based Observability | Combines Data Quality rules with Data Observability to catch both known and previously unanticipated data issues |
| Ability to Handle Large Volumes | Scales from database-engine comparisons (up to 40 million rows) to Apache Spark in-memory comparisons for higher volumes |
| Integration with DevOps Platforms | Keeps DataOps and DevOps process execution and management consistent |
| Integration with RPA Platforms | Extends testing to business users and scenarios that mimic human interaction, beyond what Python, Scala, or SQL alone can cover |
Extensibility
In data testing, there are often times when you need to be able to extend your solution to other areas that weren’t anticipated. A unique data problem is encountered that is outside the norm and could not be thought of beforehand. For example, If your solution is extensible through Python or some other method, the issue can be resolved quickly. With Datagaps, we provide a Plugin component that can be selected from a library of components that is extensible by using Python. This eliminates the need for complex workarounds that you have to shoehorn into other solutions.
Advanced API Components
In today’s world, data comes to us in a variety of ways. Often as simple as CSV files, feeds from production applications or data that is FTP’d to a location. Quite often, there are requirements to use an Advanced API to get access to the data. In one recent example, our client had 8 APIs that we needed to invoke as part of ETL testing to gain access to their Hierarchical JSON data. We needed to create multiple files from each of the APIs, which meant that we needed advanced capabilities.
AI Based Observability
Writing Data Quality rules is effective in most situations, but often it may not be needed if your solution can learn from the data being ingested. A combination of Data Quality rules and Data Observability is the best approach. Data Quality rules can surface likely data issues efficiently while Data Observability will find outliers that haven’t been anticipated before. You may try Datagaps Data Quality Monitor for this.
Ability To Handle Large Volumes in the Billions
As data volumes continue to grow, at some point your normal processing requirements will outgrow your data testing capabilities. Recommended approach: start with a database engine for comparisons up to 40 million rows, since it’s easier to set up and less costly; once volumes exceed that, switch to Apache Spark-based in-memory comparisons for higher-volume workloads.This method takes advantage of native cloud capabilities such as clusters and auto scaling. So if you volumes are low currently the DB Engine will take care of the volumes but as your data scales you have an option to swap out the DB Engine for the Apache spark implementation that can meet your current of future needs. Learn more about automating your Big Data.
Integration with your DevOps Platform
Your DevOps organization has spent an enormous amount of time and cost to implement a DevOps platform. As you introduce your DataOps platform it is important to be able to integrate with the DevOps platform such as x,y,z. This ensures consistency between how your DevOps ad DataOps process execution and management.
Integration with an RPA Platform
Python, Scala and SQL use cases can be extended to handle a limitless number of variations in your data test plans. However, these languages, while easy to use for developers aren’t meant for the business user. Additionally, they aren’t designed to mimic human behavior. There is a Billion dollar industry that caters to Robotic Process Automation. In other words, RPA mimics the human interaction
Conclusion
Data testing needs have risen in importance as organizations monetize the use of the data or make critical decisions based on the data flowing through their enterprise. Volumes are increasing, sources take on different access methods, and often, data needs to be accessed through alternative means via API or other methods. Your processing needs have certainly grown substantially in the past few years. Methods of testing are changing rapidly. That is why we believe extensibility is so important. As all of these dynamics impact your business and future needs, a platform like DataOps Suite that will scale and extend capabilities will be critical for current and future needs.
Frequently Asked Questions: Data Testing Limits, AI Observability, and Extensibility
1) Why isn’t standard data testing enough for most organizations?
Standard testing (data access, quality rules, comparisons) typically only covers about 75% of real-world scenarios — edge cases like complex APIs, massive data volumes, and unknown anomalies require additional capabilities.
2) What’s the difference between Data Quality rules and AI-based Observability?
Data Quality rules catch known, predefined issues you can anticipate and codify. AI-based Observability uses machine learning to detect unexpected anomalies and patterns you wouldn’t think to write a rule for — the two work best together.
3) How does extensibility help with data testing?
Extensibility (like Python-based plugins) lets teams build custom logic for unique data issues on the fly, instead of being limited to a tool’s out-of-the-box features or waiting on vendor updates.
4) Why is DevOps and RPA integration important for data testing?
Integrating testing into DevOps pipelines and RPA workflows allows validation to run automatically as part of continuous delivery, rather than as a separate manual step — critical for enterprise-scale, high-velocity environments.
Rajesh Kumar A
Digital Marketing Manager, Datagaps
Digital Marketing Manager at Datagaps. Drives data-driven growth through content, performance campaigns, and marketing technology.
Subrahmanya Narayana Chirravuri
Senior Director, Technology, Datagaps
Senior Director of Technology at Datagaps. Leads engineering for the ETL, BI, and data-quality validation platforms.
