Data Lakes attempt to avoid the high costs and brittle results of building Data Warehouses by delaying making the data interoperable. But their lack of structure makes more difficult both finding the correct data and analyzing it correctly. We describe how using Semantic Technology standards and tools supports simple (but not simplistic) relationships that document and reflect the meaning of the data, making the Lake smarter. At the same time, these standards and tools support accessing and querying the various data sources in the Lake as a coherent, interoperable whole. All of which improves the turn-around and costs for analytics product development. Participants will learn:

The basic data structure (RDF triples) behind all Semantic Technology

How triples may be derived from existing structured & unstructured data

How triples can be self-assembled into rich, complex graphs (and what “graphs” means)

How semantic schemas (ontologies) add still more “smartness” to the data

How tools using the R2RML standard transform a Data Lake’s parts into a whole (without necessarily changing the original datastore)