Dabble DB in Action

By Jason Brooks  |  Posted 2009-01-20 Print this article Print


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.

As Editor in Chief of eWEEK Labs, Jason Brooks manages the Labs team and is responsible for eWEEK's print edition. Brooks joined eWEEK in 1999, and has covered wireless networking, office productivity suites, mobile devices, Windows, virtualization, and desktops and notebooks. JasonÔÇÖs coverage is currently focused on Linux and Unix operating systems, open-source software and licensing, cloud computing and Software as a Service. Follow Jason on Twitter at jasonbrooks, or reach him by email at jbrooks@eweek.com.

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