Book Image

Expert C++ - Second Edition

By : Marcelo Guerra Hahn, Araks Tigranyan, John Asatryan, Vardan Grigoryan, Shunguang Wu
5 (1)
Book Image

Expert C++ - Second Edition

5 (1)
By: Marcelo Guerra Hahn, Araks Tigranyan, John Asatryan, Vardan Grigoryan, Shunguang Wu

Overview of this book

Are you an experienced C++ developer eager to take your skills to the next level? This updated edition of Expert C++ is tailored to propel you toward your goals. This book takes you on a journey of building C++ applications while exploring advanced techniques beyond object-oriented programming. Along the way, you'll get to grips with designing templates, including template metaprogramming, and delve into memory management and smart pointers. Once you have a solid grasp of these foundational concepts, you'll advance to more advanced topics such as data structures with STL containers and explore advanced data structures with C++. Additionally, the book covers essential aspects like functional programming, concurrency, and multithreading, and designing concurrent data structures. It also offers insights into designing world-ready applications, incorporating design patterns, and addressing networking and security concerns. Finally, it adds to your knowledge of debugging and testing and large-scale application design. With Expert C++ as your guide, you'll be empowered to push the boundaries of your C++ expertise and unlock new possibilities in software development.
Table of Contents (24 chapters)
1
Part 1:Under the Hood of C++ Programming
7
Part 2: Designing Robust and Efficient Applications
18
Part 3:C++ in the AI World

Designing data-intensive applications

Designing data-intensive applications in C++ involves considering various aspects, including data storage, data processing, and efficient utilization of system resources. This section will list some key considerations and best practices for designing data-intensive applications in C++.

To begin with, some best practices for data modeling and storage are listed as follows:

  • Identify the nature and structure of the data: Analyze the data requirements and determine the appropriate data model, such as relational, NoSQL, or a combination of both, based on the application’s needs.
  • Choose efficient data storage solutions: Select appropriate data storage technologies, such as databases (MySQL, PostgreSQL, and MongoDB) or distributed filesystems (Hadoop HDFS and Apache Cassandra), based on factors such as scalability, performance, and data integrity requirements.
  • Optimize data access and retrieval: Employ techniques such as indexing...