Smart Native Connectors

Using DQLabs smart connectors that come out of the box, you can connect to an unlimited amount of data sources in any form, shape and any location. No need to download or install any drivers. With no code, you can immediately connect to any source necessary with multiple options such as scan, pull, discover metadata, lineage, query based or cross join views or even partitioning to process datasets at high performance and throughput for data quality analysis. With this ability, now you can connect to any data across multi-cloud, on-premises, and legacy systems and semantically enrich with integrated metrics – all in one place.

Check out our Smart Connector Features

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.

Connect to Any Type of Data

Connect to Any Type of Data

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.

Select Your Own Elements

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.

Ability to select elements of your choice
Auto discover metadata for any type of data

Autodiscover Metadata for Any Type of Data

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.

Automatic Semantic Identification

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.

Automatic semantic identification
ML powered Semantic Learning

ML Powered Semantic Learning

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.

Faster Performance Delivered

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.

Faster Performance delivered
Know your Sensitivity Level Footprint

Know your Sensitivity Level Footprint

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.

API Powered

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.

API Powered
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Modules

Leverage a self-learning platform for out of the box Data Quality analysis with integrated catalog and governance.

Semantic Discovery

With DQLabs’ Data Sense™ capabilities, you can automatically enrich semantics for any type of data with or without metadata information.

Measure Data Quality

Scan various types of data sources and data sets in real-time and generate a trustable DQScore™ with the ability to track, manage and improve data quality over time.

Monitor Drift and Behavior Analysis

DQLabs Continuous DQ monitoring uses statistical and machine learning approaches to detect data outliers and anomalies.


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