Data integration is an enabling technology for both BI and analytics. DI for traditional BI is a relatively straightforward proposition: its focus is the DW, which is also (in most cases) its terminus, too. The BI or analytic discovery use cases change this, but they’re still working almost exclusively with SQL or semi-structured data. DI for advanced analytics is a very different proposition, however: advanced analytic processes tend to consist of multiple analytical workloads and mix traditional structured (SQL) data with multi-structured data from semi-structured (machine logs, event messages), semantic (texts, e-mail messages, documents, blog postings), and file-based (audio and video files, etc.) sources. All of this data must somehow be staged, transformed, and prepared for initial analysis, which — by definition — is itself a mere prelude to additional analysis.

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