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You can utilize 3 approaches to store extension data to the database.

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As first two are straight forward, we will cover #3 in more details. 
Dataset aproach offers quick and worry free method to handle data without dealing with SQL and creating specific database tables. Data can be handled via easy dataset class methods.
Good example for dataset usage will be occasionally edited information for products, categories, customers and orders data extension, polls, questioners, etc.

What is Dataset?

Say you need to develop a poll extension and need to store records of poll questions and answers. Normally, you would create and add 2 or more tables to the databases to handle data for the poll. With the dataset, we created an abstraction layer to the database, that allows you to skip database creation and go directly to handling data.
Dataset class will handle all the data storing and management for you with interface of methods provided. It is very easy. You name new dataset (traditionally table) or use existing dataset . Dataset can be created during extension installation from XML file provided in the extension package. It can be also removed with extension uninstall. No direct access to SQL or database is needed. If restriction access required to the dataset, it needs to be handled pragmatically based on AbanteCart permissions. If you decide to use traditional database tables in extension, you will need to create SQL scripts with install, uninstall and disable extension management process.

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$my_data -> setDatasetProperties(	
 array( 	
"some_property_name"=>”some_value”>"some_value", 			
"some_property_name"=>"some_value”value") );

Properties can be accessed with getDatasetProperties() method. This method returns an array with all the properties:

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// define columns for questions
$poll_qs->defineColumns (
   		 array ( "name"=>"language_code",
   			 	"type"=>"varchar"),
   		 array ( "name"=>"question",
   			 	"type"=>"text"),
      		 };

Dataset’s columns definition contain list of value’s descriptions (fields). Every dataset consist of columns or fieldset. Now dataset concept supports following column types: 
integer
float
varchar
text
boolean
timestamp

//set properties for the dataset
$poll_qs->setColumnProperties ( 
array ( "column_name" => "language_code",
   				 "property_name" => "length",
   				 "property_value" => "2" ),		
array ( "column_name" => "question",
   				 "property_name" => "translation_key",
   				 "property_value" => "question_txt" ),		 
   			 );

Column properties are used to keep global information for the columns for the given dataset . This properties can be used to identify data type and constrains for the column. Validation code can use these properties before saving data.

In some cases, column properties can be used to store data and act as 1 row table. If you have only 1 row of data and it is not changing you can just use columns properties to store data and avoid extra step to use rows.
Columns properties are not required, but helpful.
Actual data for the datasets is stored in rows that are similar to rows in traditional database or spreadsheet. Every row have columns (fields) that are set in the defineColumns method described above.

Let first see how we can add a row to example dataset:

$poll_qs->addRows (  array(
	array(   language_code => “en”"en", question => “What"What color do you like?”" ),
	array(   language_code => “en”"en", question => “What"What shape do you like?”" )  
) );

If all rows are inserted successful the return will be total number of rows added that shouldbe same as imputed array count.

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$rows = poll_qs->searchRows ( array(   name=> “language"language_code”code",  operator => “"=”",  value => “en”"en"   )  ); 	

or

$rows = poll_qs->searchRows ( array(   name=> “question”"question",  operator => “LIKE”"LIKE",  value => “color”"color"   )  ); 

where “condition” may be one of list:
“=”
”>”
”<”
LIKE

To delete the row from the dataset you can simply call method deleteRows

$my_data->deleteRows( array( "column_name"=>string, "operator"=>string, "value"=>string )  );

If success, method will return total number of rows deleted.

To drop dataset you can use simple command:

$my_data->dropDataset();

To make things simple to batch load there is a method to load data set via XML. You can create and populate data set with one function loadXML and simple XML node structure

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