Augmented Data Integration

Effectively combine all your data from a variety of sources to get a single view of your organization’s data using our AI powered built-in connectors.

Data Integration

In today’s world, organizations need faster access to integrated data across an increasingly distributed landscape with data spread across the cloud, on-premise and within legacy systems. Extreme levels of diversity, distribution and scale are adding tremendous complexity to the efforts of overall data integration. The traditional architectures and tools for data integration focused on replicating and moving data. They were slow and inexact in delivering semantically enriched and integrated datasets.

However, with DQLabs’ augmented data integration platform you now have the ability to use AI/ML algorithms to deliver “just-in-time” data management. Implementing infrastructure and processing maps for data integration use cases, both business and technical users can solve their requirements for hybrid/multi-cloud data management including augmented data integration and data fabric designs.

Augmented Data Integration Features

Leverage DQLabs augmented data integration featuring the user-friendly ready-made plug and play platform, applicable for any data source type and any data location with support for governance and enterprise-wide management of data assets.

Combine different data destinations

Supports easy access to and delivery from various data silos by implementing well-understood, established integration technologies utilizing best practices and standards. More importantly it’s flexible enough to support integrated data through a combination of data delivery styles.

Active metadata analysis

AI/ML-augmented data integration enables active metadata analysis, semantics and a knowledge graph by offering an augmented data catalog which provides an inventory of all types of metadata assets and their relationships.

Versatile Data Access

DQLabs provides multiple levels of access at the attribute, dataset, and data source level to provide for ease of data access and permissioning.

Process at speed

DQLabs provides massive parallel processing capabilities to deliver distributed processing to rationalize the performance of data integration workstreams, distributable integration flows (via push-down processing) and easy-to-deploy optimization capabilities.

API support

Provision data dynamically using APIs in synergy with data and application integration techniques including access, lookups and also delivery of data through a centralized permissioning layer.

Data Integration

The extreme levels of diversity, distribution, scale, and complexity in an organizations’ data assets are adding tremendous complexity to the overall data integration and data management tasks. DQLabs combines different data integration styles and by using proven integration technologies, best practices, and standards, delivers scalable out of the box connectors with a unified data integration strategy.

  • DQLabs continuously finds and integrates catalogs, sharing all forms of metadata
  • Save time and money with the ability to deliver a just-in-time data management infrastructure
  • Simplify your DQ process as DQLabs extracts and centralizes data across multi-cloud, hybrid cloud, on-premises, and third-party applications
  • Benefit with the use of metadata analysis, semantic identification, and knowledge graphs as part of the data ingestion process
  • Easily and effectively scale and distribute your DQ process based on your workload in real-time

Best Practices

Semantic Discovery for Data Quality Management


Semantic Discovery for Data Quality Management

Fundamentals of Semantic Discovery

A data-driven business gains no value from its data which lacks a clear meaning and context. Such businesses will, therefore, constantly try to make sense of their massive data. How? Some businesses have teams of business analysts, data specialists, and other personnel employed to manually review, analyze, and classify all available data. In large businesses, it becomes difficult to do this manually, hence the need for automation. They are using ML algorithms to automate the process of analyzing and classifying massive datasets through self-learning capabilities. This automation has been proven to save up to 90% of project time.

What is Semantic Discovery?

Semantic Discovery is the approach to profiling data based on its semantic categories. Semantic Discovery supports the possibilities of exploring semantic categories of data in question and querying complex semantic relationships in datasets to create tabular analyses which have indicators and patterns which may be pre-defined.

Why Semantic Discovery?

Semantic Discovery is a process that helps businesses to automatically derive business meaning from data to enable understanding and automating business processes. With no clear meaning and context of data, the data may of be minimal value to a business, especially the data-driven businesses.

With Semantic Discovery, you can;

  • Explore semantic categories and query complex semantic relationships in the data to be analyzed.
  • Scan through data, analyze the characteristics and values of the data,
  • Create table analyses preconfigured with indicators and patterns that best suit your data.
  • Compare your data against other fields with an aim to propose semantic meaning and relationships with other available datasets.
  • Semantic Discovery for a data-driven company enables further automation.
  • Automatically generate data quality rules for a given dataset.
  • Provide the basis for protecting personal data to enable self-service data ingestion.

How does DQLabs Semantic discovery work?

Data quality measurement without meaning, semantics, or understanding of the business context of the data does not help get better business practices. With DQLabs’ Data Sense™ capabilities, a business can automatically enrich semantics for any type of data, whether it has metadata information or not. As a result, a business can automate the process of discovering, inventorying, profiling and tagging using a simplified form of metadata management and auto-discover rules and sensitivity classification in alignment with the business landscape.

With semantic identification and extensive integration into data catalog or data governance systems, you can derive end-to-end views of your data assets for the purposes of governance, privacy, compliance, and data quality. This will allow the data stewards to search and discover metadata as well as understanding the data quality associated with each attribute.

What are’s Semantics Discovery Features?

DQLabs’ Semantics Discovery will be of great help for any type of data source. It has built-in support and integration for simplified metadata management and automates the process of discovering, inventorying, profiling, and tagging data. Some of the features include;

Auto Discover Semantics

To help you discover and extract semantics from various enterprise data warehouses, operational databases, enterprise applications, cloud data stores, and nonrelational data stores with the help of just a few clicks using out-of-the-box connectors.

Automatic Sensitivity Classification

This will help you configure at ease your own sensitivity levels per your data governance programs and automatically identify the sensitivity footprint and classification at each attribute level.

Identify True Data Type

This is a feature that will help to ignore formatting, locale, and culture and identify the true data type at the attribute level to find relevant data quality rules.

Auto Discover Relevant Rules

With enriched semantics and business context for each attribute, let the platform discover all relevant data quality measurement rules for you.

Auto Detect Necessary Remediations

Measurement without remediation does not help a business to improve data quality. With enriched metadata and semantics, now you can enjoy remediation libraries that can perform smart curation at every attribute or dataset level.

Search and Discover with Relevance

Perform semantic searches across datasets and find the most relevant datasets by various metrics such as data quality score, drift level, sensitivity classification, etc., all within one platform.

Auto-tagging and Classification

Includes classification per the most up-to-date data privacy and security compliance regulations — such as the EU General Data Protection Regulation (GDPR) and California Consumer Privacy Act (CCPA).

How will your company benefit from’s Semantics Discovery?

Leveraging DQLabs’ Semantics Discovery will help you to scan through your data, analyze the characteristics as well as values, compare the data against other fields, and eventually propose semantic meaning and relationships with other datasets. Semantic Discovery capabilities can be purposed for any company’s needs and data and will work in different languages and parameters resulting in metadata that will allow further automation.

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