hadoop vs teradata what is the difference

Question

I've touched a Teradata. I've never touched hadoop, but since yesterday, I am doing some research on that. By description of both, they seem quite interchangable, but in some papers it is written that they serve for different purposes. But all I found is vague. I am confused.

Has anybody experience with both of them? What is the serious difference between them?

Simple Example: I want to build ETL which will transform billions rows of raw data and organize them to DWH. Then do some resources expensive analysis on them. Why use TD? Why Hadoop? or why not?

Answer 1

I've been asked this question several times, the answer that I usually give is a car analogy (which is pretty silly because I'm not a car person - but it seems to work)

• Teradata is the car/dbms for the masses - it is reliable, mature, works well and is there when you need it. It is difficult (compared to Hadoop) to customise and add functionality to the base product.
• Hadoop is the car/dbms for the enthusiast - it isn't as reliable or mature, it works well so long as you attend to it. It is easy (compared to Teradata) to customise and add functionality to the base product.

Put another way, Teradata is the reliable workhorse where you put your mission critical process (operational reporting, enterprise reporting, decision support etc). Hadoop is the place where you can do alot of this stuff, but don't be surprised if you come in one morning and find that your regulatory reports can't be produced because someone applied a patch or you've suddenly got a "too many small files" problem.

To loop back into the analogy, if you don't want to be too techy and the manufacturers product (dbms and/or car) works for you out of the box, Teradata is a good option. On the other hand, if you like to tinker under the hood, swap out the carburettor (or whatever), adjust the gear ratios, tweak the fuel air mixture depending on whether you are country or city driving, bolt on a Turbo charger and/or your family complain about how long you spend in the garage on weekends - Hadoop is the place for you.

IMHO, Most, if not all organisations need both. I hope this helps :-)

Answer 2

I think this article titled 'MapReduce and Parallel DBMSs: Friends or Foes' does quite a good job describing the situations where each technology works best. In a nutshell, Hadoop is excellent for storing unstructured data and running parallel transformations to 'sanitize' incoming data, where DBMSs excel at executing complex queries quickly.