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Ask Slashdot: Changing Career From OLTP To OLAP Dev
First time accepted submitter xby2_arch writes "After spending over 12 years writing OLTP applications (Java EE/JDBC/ORMs), I decided to dabble in the OLAP world. I had decent DB skills, considering most of my previous projects had involved data modeling and coding using Stored Procs, etc. Yet I hadn't designed or implemented any dimensional databases. Luckily for me, I had enough relevant domain knowledge to land a developer job in a data warehousing project. The work was enjoyable enough that it motivated me to spend that extra time and effort I needed to cope with the different dynamics of coding in the OLAP realm. In my past life, data volumes weren't the primary concern (instead, transaction volumes were), here, everything was about data. ETL/Integrations present another set of problems you generally skirt in a typical web/app-tier developer role. All in all, it turned out to be a non-trivial, yet worthwhile transition. I am certain that there are plenty of seasoned developers out there who plan to make a similar move (or have made already), who see data as the next chapter in their careers evolving toward becoming Enterprise Architects. I want to hear what's holding them back, or what helped them move forward. What should be considered a prerequisite to make this switch, and what are the risks, etc.?"
Please stand by while the trolls try to figure out how to respond to your question.
In the meantime, you should expect about 50 posts by PHP developers asking what a stored proc is, and suggesting you move to RoR if you want an ORM.
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Utilizing our synergistic functionalities, we were then focused towards mesh visionary markets and envisioneering collaborative initiatives with our partners. The net result was the incubation of plug-and-play experiences to transform vertical vortals and utilize cutting-edge deliverables.
Plus, I have a large cock. That helped a lot.
So... you're saying you've already made the switch from OLTP to OLAP and you'd like to take this opportunity to gloat about it, but you'd still like to hear from other developers what they think the prerequisites are for making such a move and what has held them back from doing all the cool stuff you're doing? Or am I missing the question?
Breakfast served all day!
OLTP: Online Transaction Processing: you buy a ticket, you want it immediatly. The seller types it in the computer and prints your ticket, a database checked if there were free seats and immediatly reserves one. "Immediatly" is the important word, the customer is not waiting. OLAP: Online Analytical Processing: How many seats from the US to EMEA have been sold by that kind of sellers with such produyt code. Managements wants the results by the end of the month, it is OK if the query runs a couple of hours/days. Many real life systems contain two databases, one tuned for speed (containing only the current tickets) and one for reference later (containing all tickets sold in the past n months/years). The difference between them is database tuning and SQL tuning. All the rest (such as path to architect: yeah, the more different systems you know, the more chance you will have to design new ones) is hype.
/. refugees on Usenet: news:comp.misc
If it's on the main page the target audience is pretty general, so you really shouldn't have to check Wikipedia. You should ALWAYS tell people what acronyms mean before using them exclusively. And if you have time enough to be an arrogant *&$# while posting links to Wikipedia you could also take the time to spell out what the acronyms mean. At the very least give the long form, e.g. Online Transaction Processing (OLTP), Online Analytical Processing (OLAP). See it's not that fucking hard.
And since I am taking the time to rant (and because any technical article in Wikipedia gets hijacked by propellor heads who like to inject as much as of their industry specific double speak so that they sound important and a layman can't get at least an understandable overview in the summary):
OLTP - Online Transaction Processing. An application/database designed for larger, equal, or nearly equal volumes of database inserts, updates, and deletes, as there are reads. The database is generally more (and often highly) normalized, meaning that there is less data duplication across tables and/or within tables.
OLAP - Online Analytical Processing. Essentially a data warehouse designed for larger volumes of reads than there will be inserts, updates, or deletes (often relatively very, very few deletes). Less normalized meaning that there may be duplicated data across and/or within tables in order to increase query speed at the cost of possible consistency issues due to the data duplication.