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In particular, the regular data integration processes must be run during batch windows, the relevant parts of the IT infrastructure must be administered, licenses must be managed, technology upgrades must be installed, and users must be supported.

Depending on your company’s specific requirements, for example, a specialized data profiling or data movement tool might be needed to complement a more general-purpose ETL tool.

Let us briefly touch on the concepts of data science and data engineering. If we go back to the DIKW triangle, we can say that data science focuses on extracting knowledge and wisdom from the information we have. Data scientists combine tools from mathematics and statistics to analyze information to arrive at insights. Exponentially increasing amou

Data integration. The data architecture must also provide a means by which data from different sources, which can range from operational systems to enterprise applications such as ERP to data from individual spreadsheets, can be brought together to create an integrated, consistent view of related parts of the company’s business.

The important point to consider here is that the choice of data architecture influences what tasks are undertaken and the methods for performing those tasks.

Data Augmentation is an amazing feature available in Fusion Analytics Warehouse, it gives users the power to augment their reports with additional datasets created by extending an existing entity, by adding a new dimension and/or by adding a new fact. This blog walks you through the steps for extending Department entity by supplementing with Depart

Enigma.io, which may become one of the most interesting Open Data businesses. Enigma.io was launched because two friends working with very different kinds of data realized they faced some common problems. Hicham Oudghiri was a currency trader working