Dabble DB Bridges Database, Spreadsheet Divide

I put Dabble DB to the test with some of my most vital data—stats and standings from my fantasy basketball league.

I wanted a solution that required less attention to plumbing than the spreadsheet I was using, and this is just what Dabble DB provided.

I began the process by importing the daily league stats into Dabble via cut and paste. The stats source I use comes formatted in fixed-width format, which is easy to import into Excel or Calc, but which Dabble DB was unable to accept. As a result, I had to first import my source into Calc and then select and paste the table of data to paste into Dabble DB.

Next, Dabble DB gave me a preview of my imported data, and asked if I wished to use the first row of the table as the column headers, which I did. Also at this point, I could see the data types that Dabble had guessed for my imported data.

With my data imported into a new category within my application, Dabble dropped me off at its view creation interface, from which I could select which columns of data I wished to include in the view and begin transforming the data in those columns in a number of ways.

For instance, to calculate field goals made per game, I clicked on the "field goals-made" column and chose, through a series of nested menus, to divide the field goals-made column by the "games-played" column. Dabble added a new column to my view to hold the new information. I could then remove field goals made from the view, and continue adding columns, performing operations on them and dismissing the unneeded columns.

When building future views, I could reference my derived columns, such as field goals made per game, in any other view within my application.

I needed to extend my database schema to include the fantasy league team, if any, to which the players in my data set belonged, and I was able to add this field while creating my view. I first set the data type for the field as text, but later, after I had entered the correct fantasy team values, I switched the data type to list, and was pleased to see that Dabble DB created the list automatically from the existing values in my data.

I was able to access the data in each of my views very flexibly, with HTTP links to my data in RSS, Excel, CSV (comma-separated values), plain text and JSON formats.

When I needed to import new daily stats into my application, I clicked on a link labeled "new similar" beside the log entry of my initial import operation and pasted in my updated data as before, and Dabble DB performed the import without a hitch.

eWEEK Labs Executive Editor Jason Brooks can be reached at jbrooks@eweek.com.