Posts Tagged ‘Semantic ETL’

Cambridge Semantics Launches ‘Anzo Smart Data Integration’

Anzo Smart Data Integration (ASDI)

Extract,  Transform, and Load (ETL) and the business problem ETL solves — Data Integration — are complex to say the least. As the team at Cambridge Semantics points out:

Data integration and data on-boarding are time-consuming, manual, costly & error-prone processes.

  • Complex integrations require developing a large number of point-to-point source-target mappings.
  • Each mapping must be jointly developed by experts in all involved systems before being handed off to a team of ETL developers.
  • Each hand-off increases both the time it takes to complete the integration and also the risk of errors as requirements are misunderstood or not fully validated.
  • The lineage and meaning of data are often lost in the process, limiting the trustworthiness and utility of the data.


Cambridge Semantics today announced the launch of its Anzo Smart Data Integration (ASDI) software to help enterprises rapidly understand and integrate information assets. Described as a “design time tool for business analysts,” ASDI is “designed to reduce integration time frames and costs by 10X and enhance time-to-revenue when on-boarding new customers, partners and data,” according to the official announcement.

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Time for Semantic ETL?

What’s the link between the trends of more and more objects and even commercial transactions on the web being described in a machine-readable, semantic format and the endless streaming of all that data? Revenue-funded startup First Retail, whose principals Anne Jude Hunt and Simon G. Handley will be speaking at the upcoming Semantic Technology Conference in June, thinks the answer is semantic ETL.

Extract, transform, load (ETL) is a widely known concept in the well-charted terrain of the IT world. That’s about transforming a bunch of heterogeneous data to unify it within a data warehouse and get some use out of it.

Semantic ETL, says Hunt, is brought on by the fact that today people want to deal with the growing loads of streaming data while it’s streaming and that “people want intelligent data, machine-readable tags,[they want] to slice and dice it for BI in lots of different ways, so the  traditional data warehouse and relational database approach is just not working for people.” Cleansed and integrated semantic data loaded into distributed, scalable triple stores can come to the rescue.

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