myITscrapbook

Prakash Karia with a short overview of the new in-memory engine for SQL Server 2014:

Today I am going to discuss the new SQL Server 2014 feature In-memory OLTP a.k.a. Hekaton — a Greek word which means hundred or hundredfold. In-memory OLTP significantly reduces the time required for OLTP workload processing. The best part of this feature is that this is installed with the SQL Server 2014 Engine without requiring any additional installations. It also allows its benefits without rewriting your database application.

It’s not EDW so I cannot use it to prove my scenario.

To make a SPARQL Query, you can use

sparql.org

Virtuoso

These are both generic endpoints where you can run SPARQL queries. Each endpoint provides a form where we can input a query, and the results are returned (as HTML).

TRY IT

Using sparql.org, paste this into the large box:

PREFIX foaf: <http://xmlns.com/foaf/0.1/> SELECT ?name WHERE {  ?person foaf:name ?name . }

and this into the Target graph

Some time ago Nick at Garlik wrote a UNIX-flavoured command line tool for interacting with SPARQL query endpoints. He released it under the GPL, and we occasioanly make small hacks on it.

SPARQL Cheat Sheet

View more presentations from LeeFeigenbaum

This is the story of how I tried to use Semantic Web technologies, like RDF and Linked Data, to achieve a basically simple task, and of everything that prevented me successfully doing so.

The Task

Impressive roster for Teradata. I’d also love to see a list of deployments where Teradata and Hadoop are meeting.

Kathryn Kelly for SmartDataCollective:

This is the one that really got people. Companies need solutions that enable them to use and customize their data easily, because it is the whole team, not just the individual analyst, that knows the business best. By offering business users intuitive data solutions, we bypass the need for the data scientist, who works in isolation. In fact, most data scientists are associated with the old school of business intelligence, where systems were so complicated that they needed someone with a data science background to run and get value from them. The new generation of solutions, on the other hand, is making it easy for business users to engage big data. An interdisciplinary team will see and use the visuals provided, and collaborate on the best decisions on a regular basis.

It’s better not to make predictions when you miss the point.