In today’s world, organizations need faster access to integrated data across increasingly distributed landscapes with data spread across cloud, on-premise and legacy systems. Extreme levels of diversity, distribution, scale are adding tremendous complexity to the overall data integration. Traditional ways of data integration architectures and tools focused on replicating and moving data are slow in delivering semantically enriched, integrated datasets.
However, with DQLabs’ augmented data integration we have the ability to use AI/ML algorithms to deliver “just-in-time” data management infrastructure and processing maps for data integration use cases and solve requirements for hybrid/multicloud data management, augmented data integration and data fabric designs.
Leverage DQLabs augmented data integration for business user friendly ready-made plug and play for any data source type, any location with support for governance and management of data assets.
Supports easy access to and delivery from various data silos by implementing well-understood, established integration technologies, best practices and standards. More importantly its flexible enough to deliver integrated data through a combination of required data delivery styles
AI/ML-augmented data integration enables active metadata analysis, semantics and knowledge graph by performing augmented data catalog along with inventory of all types of metadata assets and their relationships.
DQLabs provides multiple levels of classification at the attribute, dataset, data source level for ease of sharing and permissioning.
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
Provision data dynamically using APIs in synergy with data and application integration techniques including not only access, lookups but also delivery of data through a centralized permissioning layer.
All complex tasks around connection, data profiling, curation, governance and reporting is all done in few clicks.
Leverage data quality powered catalog to automate the process of discovering, inventorying, profiling and tagging by DQ score.
Ability to discover patterns, insights, fraud, missing values and correlations across attributes using all connected datasets and data sources within fraction of minutes using AI from users’ behaviors and automatic business rules configurations.
ML based smart curation modules to automatically curate the data based on the metadata definition and using Reinforcement Learning at all three levels of curation – Basic, Reference and Advanced.
CURIOUS ABOUT AUGMENTED DATA INTEGRATION