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I'm developing an application for iOS, Android, Windows Phone and in the future a web version of it.
I'm relatively new in databases so I started learning a bit of SQL and PostgreSQL.
My application is for industry and will show pruducts and other information relatively with that.
My question is should I use SQL database or NoSQL database, and which should I use?
Thanks!
Use the database that makes it easiest to model your data. If your data is relational, use a relational database. If your data is best represented as a document, use a document database. If your data is best represented as a graph, use a graph database.
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My question is that I want to learn nodejs/express, and make a super simple web project. It would be a database with tables : users, video_games, categories.
The web site will just show list of games (just an example).
In this typical case, what would be more efficient : Mysql or MongoDB (SQL or NoSql) ?
In this particular case were you want to show only list ( you don't want to actual store videos, doc, texy, etc..) SQL database will be a good choice.
Another reason to use SQL database is that your data is relational ( I am assuming that the data i.e video_games, category...etc are linked to users) were SQL database suits more.
You should go to nosql database only when there is to relationship between your data ( well this is not the only case, but for beginners were your aim is to simply pick the right database this suffice)
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Can anyone articulate what the key differences are between SAS and SQL? I haven't worked much with SAS but went on a weeks training course, and basically it seemed like the equivalent but more convoluted and was able to do graphs.
Would appreciate some key bullet differences between them.
Standard SQL is a language to query, manipulate and define data in any(!) database. It is like the "latin language" of DB systems. Everyone knows it in order to perform standard tasks. SAS is like an extension to that with many functions.
I found a good document:
http://www.sascommunity.org/mwiki/images/5/52/CMSSUG-0506-SQL.pdf
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I have a crystal report connected to a sql server DB that uses complex statistical queries.
What are the disadvantages or advantages, especially in terms of speed, to converting views into tables to run in the reports.
For sql server I know that tables take up more space.
thanks
Disadvantages:
occupies more spaces.
processing time for the table to load time.
Advantages:
faster access
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I'm wondering what are recent advances in relational database theory and related domains? I'm interested in new approaches, query languages (alternatives to SQL and/or extensions to it), products (proprietary and open source, though I'm much more interested in open source) and research projects developed in last years.
I am not versed in the subject but there was a very nice project based on XML structures over RDB that caught my eye a few years back and now seems to work perfectly.
Have a look at http://exist.sourceforge.net/ to see a nice approach to RDB used to build a more flexible storage system with XQuery as the query language. You should like it, it's open source.
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I want to migrate data from one database to another database in Microsoft SQL Server 2005. I need to verify those rows retrieved before I insert them to the destination database's tables. Which approach is reasonable for this kind of things?
I am trying to use two datasets in my VB.NET program. Is it reasonable? Can you suggest me?
Thanks in advance,
RedsDevils
It depends on how much data you're talking about, but I'd tend to pass on .Net datasets for a migration task, as that means pulling all the data into memory. If you must do this via a .Net client program, at least use a DataReader instead. But what's even better is to keep it all in Sql Server via Sql Server Integration Services.