Mydac is the best when you try to connect to MySql, it's fast stable and real powerful components.

Also the same for other components like Sdac and Odac.

and if you would like to use more than database you can use Unidac, which you can use to connect to (oracle, Sql server, Mysql, Interbase, Firebird and ODBC drivers.).

Another great point for Devart components that you don't need client library to be installed on the clients machine (libmysql.dll).

Experience with direct data access components
I have a dataset which I want to split based on the first column. 

The data looks like this 

AA =
 {{"Symbol", "Full Name", "Date", "Open", "High", "Low", "Close", 
  "Adj Close", "Volume", "Type"},
 {"KO", "The Coca-Cola Company", {2013, 7, 19, 0, 0, 0.}, 40.88, 41.1,
   40.79, 41.09, 41.09, 1.14011*10^7, "Stock"},
 {"KO", "The Coca-Cola Company", {2013, 7, 18, 0, 0, 0.}, 40.86, 
  41.07, 40.74, 40.81, 40.81, 9.7088*10^6, "Stock"},
 {"KO", "The Coca-Cola Company", {2013, 7, 17, 0, 0, 0.}, 40.55, 
  40.98, 40.31, 40.84, 40.84, 1.85131*10^7, "Stock"},
 {"KO", "The Coca-Cola Company", {2013, 7, 16, 0, 0, 0.}, 39.78, 40.5,
   39.5, 40.23, 40.23, 3.35756*10^7, "Stock"},
 {"KO", "The Coca-Cola Company", {2013, 7, 15, 0, 0, 0.}, 41.05, 
  41.25, 40.93, 41.01, 41.01, 1.14184*10^7, "Stock"},
 {"KO", "The Coca-Cola Company", {2013, 7, 12, 0, 0, 0.}, 41.03, 
  41.13, 40.73, 41.03, 41.03, 1.06864*10^7, "Stock"}
 {"MCD", "McDonald", {2013, 7, 19, 0, 0, 0.}, 100.2, 100.41, 99.53, 
  100.27, 100.27, 4.5083*10^6, "Stock"},
 {"MCD", "McDonald", {2013, 7, 18, 0, 0, 0.}, 100.48, 100.77, 99.99, 
  100.18, 100.18, 3.4016*10^6, "Stock"},
 {"MCD", "McDonald", {2013, 7, 17, 0, 0, 0.}, 100.05, 100.35, 99.3, 
  100.1, 100.1, 5.3774*10^6, "Stock"},
 {"MCD", "McDonald", {2013, 7, 16, 0, 0, 0.}, 100.18, 101.12, 99.47, 
  100.88, 100.88, 4.4062*10^6, "Stock"},
 {"MCD", "McDonald", {2013, 7, 15, 0, 0, 0.}, 101.6, 101.73, 100.7, 
  100.75, 100.75, 4.4807*10^6, "Stock"},
 {"MCD", "McDonald", {2013, 7, 12, 0, 0, 0.}, 100.59, 101.81, 100.5, 
 101.58, 101.58, 4.7668*10^6, "Stock"},
 {"MCD", "McDonald", {2013, 7, 11, 0, 0, 0.}, 100.75, 100.96, 99.76, 
  100.79, 100.79, 4.0641*10^6, "Stock"}}


I've been able to do it: 

Split[AA, First[#1] === First[#2] &amp;]


But I do not understand the theory behind it. This is how far I got


  Split[list, test] treats pairs of adjacent elements as identical 
  whenever applying the function test to them yields True. 


From this I get that Split is a function where AA is my list, and that it splits the data into groups when the test yields true. 

But I get confused here. 

First[#1] === First[#2] &amp;

First[{a, b, c}] 



  a


Based on this I get that it picks the first element of the list within the list, in my case "KO" This means that when First[#1] equals First[#2], it yields true. However, I don't understand the function of #.

How does this predicate function work? Does it take

Row 1 column 1 = Row 2 column 1 --&gt; False
Row 2 column 1 = Row 3 column 1 --&gt; True 
Row 3 Column 1 = Row 4 column 1 --&gt; True 
...


and then group all the True and all the False together and create seperate lists? 

Same function again: 

I'm just not sure what #1 does in the list and what #2 does in the list and why &amp; needs to be added at the end. 

Same function as above ---> Split[AA, First[#1] === First[#2] &amp;]
