Posted on November 14th, 2008 by Tasso Argyros
Last week I conducted a course at the TDWI World Conference in New Orleans, LA called, “Introduction to Map/Reduce Data Transformations“. If you weren’t able to make the session, my slides are embedded here.
I’m pleased to have been given the opportunity to introduce this new approach to in-database analytics and parallel data processing, in general. The most consistent feedback I had was that there wasn’t enough time to cover this topic in-depth, and attendees were eager to learn more! In that case, my previous post on educational resources for MapReduce may be of interest.
Since Aster Data Systems introduced In-Database MapReduce for the Aster nCluster relational database, there has been tremendous interest in the data warehousing and technology community, with recent coverage in the NY Times and by influential blogs like DBMS2, Beyond Search, and Cloud N, just to name a few.
Hopefully TDWI will turn this into a 1/2-day course in the future. (If you agree, feel free to contact them at info@tdwi.org).
If anyone knows of other good resources on this emerging topic, please feel free to put links in the comments here.
After reading your product info and your blog, I am quite impressed with your solution.
But sometimes we really need a global FS available like HDFS in Hadoop, not just a database.
So my question would be with your in-database Mapreduce, how is it compared with HIVE or Pig based on Hadoop, which can apply MapReduce on structured data in files/logs?
The results from HIVE or Pig can feed into a traditional database like MySql or PostgreSQL.
How we can combine bothe the benefit of In-database Mapreduce, with HDFS and MapReduce of Hadoop?
Thank you.