Book Image

Delphi GUI Programming with FireMonkey

By : Andrea Magni
4 (1)
Book Image

Delphi GUI Programming with FireMonkey

4 (1)
By: Andrea Magni

Overview of this book

FireMonkey (FMX) is a cross-platform application framework that allows developers to create exciting user interfaces and deliver applications on multiple operating systems (OS). This book will help you learn visual programming with Delphi and FMX. Starting with an overview of the FMX framework, including a general discussion of the underlying philosophy and approach, you’ll then move on to the fundamentals and architectural details of FMX. You’ll also cover a significant comparison between Delphi and the Visual Component Library (VCL). Next, you’ll focus on the main FMX components, data access/data binding, and style concepts, in addition to understanding how to deliver visually responsive UIs. To address modern application development, the book takes you through topics such as animations and effects, and provides you with a general introduction to parallel programming, specifically targeting UI-related aspects, including application responsiveness. Later, you’ll explore the most important cross-platform services in the FMX framework, which are essential for delivering your application on multiple platforms while retaining the single codebase approach. Finally, you’ll learn about FMX’s built-in 3D functionalities. By the end of this book, you’ll be familiar with the FMX framework and be able to build effective cross-platform apps.
Table of Contents (18 chapters)
Section 1: Delphi GUI Programming Frameworks
Section 2: The FMX Framework in Depth
Section 3: Pushing to The Top: Advanced Topics

Taking advantage of memory tables

Even if most DAC libraries were born to provide you with the basic capabilities to read, manipulate, and store data from/into a DBMS, this is not always the case. Memory tables (in-memory datasets) are a very multi-purpose capability that may often change the way you deal with data in your applications.

There are a number of situations where an in-memory dataset can be useful. Let's cover some of the most common:

  • Handling data that does not come from a DBMS: Sometimes you need to handle or collect data and the data source is something different from a DBMS. For example, you may want to collect data from sensors or other devices you can reach through networking or other communication channels. When the amount of data reaches a certain point, you may actually want to treat it as a dataset and possibly still use all the goodies you are familiar with (like filtering, sorting, aggregate functions, and so on). Also, having data in a format that is...