Leverage DQLabs’ Smart Native Connectors for any type of data sources with built-in support for discovery and governance of data assets along with data quality metrics.
No matter how or where your data is stored, we deliver. Cloud Datawarehouse, Lakehouse or NOSQL, relational databases, API, or legacy – our native connectors are easy to configure, even by non-technical users, and they allow you to connect to all forms (structured, unstructured, or streaming) in a matter of seconds, all out of the box.
You can select critical elements of your choice, entire data sets, or view as you see fit with simple and easy selections. The platform is 100% adaptive and configurable based on your needs wether that means full or sample set based data quality analysis.
You can auto discover metadata properties for any dataset even with no metadata. With added integration, you can perform augmented data cataloging with adaptive thresholds for all types of metadata properties for understanding relationships, monitoring alerts and business context.
Understanding the business context of data before applying any adaptive threshold or auto data quality rules is key. You can perform out of the box semantic identification by simply connecting to your data source of choice.
With self-learning capabilities, you can easily identify, change, and augment your organization’s semantic knowledge with quality metrics across your datasets in a matter of minutes.
Performance is key when handling large datasets which is why we provide a variety of configurations including massive parallel processing capabilities for your specific needs with easy-to-deploy optimization capabilities.
DQLabs provides multiple levels of privacy, compliance and sensitivity classification at the attribute, dataset, and data source level for ease of appropriate sharing and governance as well as helping you understand your sensitivity footprint.
Anything you can do in UI can be done and extended using a normalized API with a centralized permissioning layer for ease of integration with other data catalog, data governance, modern data management or DataOps initiatives.
Leverage a self-learning platform for out of the box Data Quality analysis with integrated catalog and governance.
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