Data integration involves combining the organization’s data from different sources to create one usable consolidated stream of data. When well-executed, data integration results in one accurate view which can be used for data analysis. With DQLabs, data integration is made seamless by utilizing AI-powered built-in connectors.
Benefits of Data Integration
Traditional Data Integration
Traditional extraction, transformation, and loading tools are slow, error-prone, and time-consuming. Data analysts spend a lot of time going through the data, comparing the schemas and formats. Where an organization has a large amount of data, this process can be very expensive and not end up providing the expected quality of consolidated data. The slow process of integration results in a delay in the generation of valuable insights to be used in decision making.
Traditional data integration also provides integrated data as batches and not real-time. The lack of real-time consolidated data means that the organization can’t get up-to-date reports.
Modern Data Integration
Augmented data integration tools provide an organization with real-time consolidated data. They also provide the ability to store, stream, and deliver any data when needed from any cloud warehouse. It is possible to perform an error check on streaming data thereby enriching it at a faster rate which reduces the time from integration to usable, accurate insight.
DQLabs utilizes AI/ML algorithms to provide a “just-in-time” data processing map and data management infrastructure that solves requirements for data fabric designs, augmented data design, and multi-cloud data management. By tracking the flow of data during the integration process, modern data integration tools can reduce the possibility of data loss or security breaches. This also ensures that individual data flow streams can be analyzed for inconsistencies, thus reducing the possibilities of errors.
Try augmented data integration in action.