Pat Finerty interview: Big data analytics with SAS on Hadoop


    At this week’s SAS Live event in Toronto, we caught up with Pat Finerty, the vice-president of alliances and business development at SAS Canada.

    He discusses how SAS sees itself in the middle of three factors that enterprises need to manage for success – data, deployment, and discovery. Whereas businesses used to have to spend weeks or months running analyses on its data, there’s new visualization technology that can help deliver insights on demand.

    To help deliver such solutions, SAS is partnering with Hortonworks, which offers the Hadoop platform that makes big data manageable. Finerty discusses how SAS works with its clients closely to deploy its solutions and highlights some of the most exciting examples of putting big data analytics to work in the field. Watch the video above and read our coverage on the event here.


    1. Hortonworks uses the obsolete SQL – SAS uses the obsolete SQL as well.
      Instead of structuring EXTERNAL to data queries SAS-Hortworks should structure data itself.
      For instance, there are two sentences:
      a) ‘Pickwick!’
      b) ‘That, with the view just mentioned, this Association has taken into its serious consideration a proposal, emanating from the aforesaid, Samuel Pickwick, Esq., G.C.M.P.C., and three other Pickwickians hereinafter named, for forming a new branch of United Pickwickians, under the title of The Corresponding Society of the Pickwick Club.’
      Evidently, that the ‘ Pickwick’ has different importance into both sentences, in regard to extra information in both. This distinction is reflected as the phrases, which contain ‘Pickwick’, weights: the first has 1, the second – 0.11; the greater weight signifies stronger emotional ‘acuteness’; where the weight refers to the frequency that a phrase occurs in relation to other phrases.
      SQL does not see and cannot produce the above statistics – SQL is out.

      I, however, discovered and patented how to structure any data without SQL, the queries – INTERNALLY: Language has its own INTERNAL parsing, indexing and statistics and can be structured INTERNALLY. (For more details please browse on my name ‘Ilya Geller’.)
      My technology structures data itself.

        • I do, I know.
          NoSQLs have nothing to do with data, it’s about queries and some descriptions of them: you search within DB using queries, which are not connected with data.
          For example, the query ‘what is platonic love?’ has nothing to do with City bank database on financial transactions.

          Mongo, Cassandra, etc. – they don’t structure data – I am the first and only ever who does – I patented that.
          My technology create personal profiles and filters queries through them.
          Read my IBM blog and patents

        • However, MongoDB does support a rich, ad-hoc query language
          of its own. http://docs (dot) mongodb (dot)org/manual/reference/operator/

          Cassandra Query Language (CQL) v2.0
          https://cassandra (dot) apache (dot) org/doc/cql/CQL.html#CassandraQueryLanguageCQLv2.0
          They use queries – they don’t structure data


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