Archive for the ‘Lingual’ Category

Practical Sentiment Analysis Tutorial

Wednesday, March 5th, 2014

Practical Sentiment Analysis Tutorial by Jason Baldridge.

Slides for tutorial on sentiment analysis.

Includes such classics as:

I saw her duck with a telescope.

How many interpretations do you get? Check yourself against Jason’s slides.

Quite a slide deck, my reader reports four hundred and thirty-five pages, 435 pages.


Cascading into Hadoop with SQL

Wednesday, February 20th, 2013

Cascading into Hadoop with SQL by Nicole Hemsoth.

From the post:

Today Concurrent, the company behind the Cascading Hadoop abstraction framework, announced a new trick to help developers tame the elephant.

The company, which is focused on simplifying Hadoop, has introduced a SQL parser that sits on top of Cascading with a JDBC Interface. Concurrent says that they’ll be pushing out over the next couple of weeks with hopes that developers will take it under their wing and support the project.

According to the company’s CTO and founder, Chris Wensel, the goal is to get the commuity to rally around a new way to let non-programmers make use of data that’s locked in Hadoop clusters and let them more easily move applications onto Hadoop clusters.

The newly-announced approach to extending the abstraction is called Lingual, which is aimed at putting Hadoop within closer sights for those familiar with SQL, JDBC and traditional BI tools. It provides what the company calls “true SQL for Cascading and Hadoop” to enable easier creation and running of applications on Hadoop and again, to tap into that growing pool of Hadoop-seekers who lack the expertise to back mission-critical apps on the platform.

Wensel says that Lingual’s goal is to provide an ANSI-standard SQL interface that is designed to play well with all of the big name distros running on site or in cloud environments. This will allow a “cut and paste” capability for existing ANSI SQL code from traditional data warehouses so users can access data that’s locked away on a Hadoop cluster. It’s also possible to query and export data from Hadoop right into a wide range of BI tools.

Another example of meeting a large community of uses where they are, not where you would like for them to be.

Targeting a market that already exists is easier than building a new one from the ground up.