Azkaban, un posto come un altro dove vivere

Ecco la TOC della prima parte da affrontare:

Part 1: Foundational Skills and Knowledge

• Chapter 1: Data Can Be Agile.  Explores how application developers, database administrators (DBAs), enterprise architects, and data administrators can work together effectively in an agile environment.

• Chapter 2: From Uses Cases to Databases – Real-World UML. Object technology is the norm for modern projects, therefore it is critical for everyone to understand the basics of object orientation and the Unified Modeling Language (UML) 2.x (including UML data modeling).

• Chapter 3: Data Modeling 101.  Data modeling is a fundamental skill that all software professionals, including object professionals, require if they wish to store data effectively.  UML data modeling is covered in detail.

• Chapter 4: Data Normalization.  Normalization is a collection of design strategies that ensure data is stored in one place and one place only, promoting the design of highly cohesive and loosely coupled data schemas.

• Chapter 5: Class Normalization.  The concepts of normalization can be applied to object schemas, a complementary technique to design patterns and programming idioms.

• Chapter 6: Relational Database Technology, Like it or Not.  Relational databases (RDBs) have been the dominant technology for persisting business objects and will likely remain so, therefore you need to understand technology.

• Chapter 7: Overcoming The Object-Relational Impedance Mismatch.  Object technology and relational technology are based on different paradigms, presenting us with a technical impedance mismatch that we must overcome.  Worse yet is the cultural mismatch between object professionals and data professionals that must also be bridged.

• Chapter 8: Legacy Databases – Everything You Need to Know But Are Afraid to Deal With.  Virtually every project team finds that it needs to work with legacy data sources, and when doing so quickly discover serious data quality, database design, and data architecture problems that they need to overcome.

Ieri sera è nato ufficialmente il GSI, come lo chiamo io, Gruppo di Studio sull’Informatica.
Non contenti di lavorare oltre le 8 ore giornaliere, ci siamo trovati (io, Stefano, Ciga ed Alberto) per dare vita a questa iniziativa sicuramente molto interessante.

Argomento di interesse Agile Database Techniques – Effective strategies for the Agile SW developer, che detta così sembra parecchio paurosa come cosa, però discutendo è apparso subito come un argomento molto interessante da approfondire.
Insomma una sfida decisamente avvincente da affrontare.

Di seguito un breve riassunto degli argomenti che verranno affrontati:

Part 1 describes the fundamental skills and knowledge that everyone on an agile project team should have. This includes the basics of object orientation, relational databases, the object-relational impedance mismatch, data modeling, and how to deal with legacy data issues. Without this common base of knowledge it is very difficult for application developers and data professionals to work together effectively. A significant problem in the IT industry is that most data books do not cover object-oriented development issues and most object books seem to ignore data issues. This needs to stop.

Part 2 focuses on how to take an evolutionary approach to data. This section sets the foundation for a model-driven development (MDD) approach, or more accurately an agile model-driven development (AMDD) approach where your application code and database schemas are based on agile models. This isn’t the only way to work, you may decide to take a test-driven development (TDD) approach instead or better yet combine it with AMDD. Both methods support evolutionary development but because MDD is very common within the data community and I suspect that they will gravitate more towards an AMDD approach rather than a TDD approach. However, some agile developers, particularly extreme programmers, prefer TDD over AMDD. Luckily the two approaches work very well together so it really doesn’t matter. The implication is that TDD will become more important to data professionals in the coming years. This section also describes database refactoring, an evolutionary technique that enables you to improve your database design in small steps. In many ways database refactoring is normalization after the fact. Chapters describing mapping objects to relational databases, performance tuning, database encapsulation, and supporting tools are included in this part because they enable evolutionary development

Part 3 focuses on implementation techniques and strategies such as concurrency control, security access control, finding objects in relational databases, referential integrity, and the effective use of XML. An important observation is that many of these topics are traditionally thought of as data issues, but as you’ll see there is far more to them than this – it isn’t a black and white world.

Part 4 describes strategies for adopting agile database techniques. These chapter provides advice for individuals who want to become agile software developers and for organizations that want to adopt agile techniques.