Over the past few years working as a data engineer, I’ve seen how quickly companies are moving their platforms to Databricks and AWS. The flexibility and scale these platforms provide are amazing, but one challenge always comes up again and again: how do we maintain trust in the data when the volume and complexity keep growing?
In my data engineering journey, one of the most challenging but important topics has been data quality at scale. When you are dealing with smaller datasets, you can usually catch errors with quick scripts or manual checks. But when you’re working with terabytes of data in a cloud environment, especially on Databricks and AWS, the rules are very different. A small error can easily flow into dashboards, AI models, or compliance reports, and the impact can be huge.
I’ve seen cases where a schema change in the source system broke a pipeline, but the issue was noticed only after business leaders questioned the numbers in a report. I’ve also seen late-arriving data shift KPIs overnight, which created confusion and loss of trust. These experiences taught me that data quality cannot be treated as an afterthought. It has to be embedded directly into the pipeline.
On Databricks, I’ve found it effective to build validation checks inside ETL workflows. For example, before writing data to Delta tables, I run checks for null values, duplicate keys, or mismatched record counts. The beauty of Databricks is that you can scale these checks easily using PySpark or SQL, so the validations run even on very large datasets.
When working in AWS, I’ve also used Glue Data Quality. It’s a handy way to define rules like thresholds for nulls, schema conformity, or data type consistency, and run them as part of Glue jobs. Since results can be tracked centrally, it becomes easier to report on overall data health.
Another approach that has worked well for me is using dbt tests alongside Databricks SQL transformations. dbt makes it easy to add tests for assumptions such as “no nulls in important columns” or “foreign key relationships are valid.” These tests run automatically with deployments, which means you don’t have to wait until production to catch issues.
Of course, testing once is not enough. I always suggest setting up monitoring and alerting for trends like volume drops, data freshness, or sudden spikes. For example, with Databricks workflows integrated with AWS CloudWatch, alerts can notify the team if daily loads deviate significantly from expected counts. This kind of visibility helps catch issues early before they spread downstream.
Another trend I see growing is data contracts and ownership. Having clear agreements between data producers and consumers on schema, timeliness, and quality expectations reduces confusion. In my experience, this works especially well when combined with Unity Catalog on Databricks for governance and AWS services for centralized policies. It creates accountability and helps resolve issues faster.
My simple suggestions from experience are:
Automate validation as much as possible using Databricks and Glue.
Test early at ingestion, not just at reporting.
Use dbt or SQL tests to keep transformations in check.
Monitor quality continuously and integrate alerts with AWS services.
Ensure every dataset has an owner responsible for quality.
At the end of the day, data quality is about trust. Without trust, even the most advanced data platform loses its value. With Databricks and AWS, we now have the tools to not just scale pipelines but also scale trust. In my view, that’s where the industry is heading—and the companies that succeed will be the ones that treat data quality as part of their culture, not just a checkbox.
I’d love to hear how others in the community are handling data quality at scale on Databricks and AWS — what practices or tools have worked best for you?
