Database Development

A database is a structured database that contains data collected through electronic means. Small databases are usually held by a file system, whereas big databases are hosted in computer cluster systems/cloud. The designing of databases includes formal techniques of import and export facilities and practical considerations, including data modeling and efficient database representation. A database system is software designed and developed to interface to end-users, applications, and databases in an effective manner.

The database development process can be broken into five stages listed below.

1-Defining the business requirement: The first step of writing any software project is to write down what should be done (the requirements) and how it will benefit the customer (the scope). For a database, this usually involves interviewing the customer or users. This stage should be completed before any other work is done.

2-Creating the architecture: After you've defined the project scope, you must document your design so that others understand how your application operates at a high level. This document also helps developers understand how their pieces fit into the big picture. An important part of any good architecture is having modular subprojects that are as independent as possible from other projects. By breaking up your application into separate modules, you can understand each module individually instead of understanding the application as a whole at first.

3-Developing components: Once an architecture has been developed, it's time to start developing the executable (and runnable) pieces of your project. This doesn't involve too much change from other kinds of projects because it's mostly just programming; however, keep in mind that the design should be flexible enough to accommodate future changes if necessary.

4-Unit testing: Database software development life cycle isn't finished until you test individual units and ensure they meet requirements. This is known as unit testing, which is necessary for all but the simplest projects because any nontrivial piece of code will probably have bugs or other issues that you don't discover until after the code has been written.

5-System testing: You can get away with not doing much system testing if your project is simple, but most projects require some level of evaluation to make sure the entire application works correctly. The best way to verify this is to run several tests (known as test cases) against your system and monitor for any abnormal behavior or errors.

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Database Linking Techniques

A database link allows two or more databases to access each other's data directly. This provides several benefits like allowing data in one central place while keeping it accessible throughout an organization, improving management by cutting down on redundant data entry, improving security by limiting view access only to trusted users, and more. In a database link, one database is called the source and the other is called the target. The target database has an equivalent view of data as in the source but with some elements renamed or filtered out.

Database links are created using standard SQL syntax since both databases have to be accessible through a common interface. A simple example would be a case where a branch office needs access to customer information that might not be available on its local system; instead of transferring all that data over slow connections, you can create a view (a virtual table) that contains only those rows needed by that office. You then create a generic link between that branch server and your main server such that any queries made by the branch server will make use of that view.

This method is useful for any situation where you want to limit access to data but also want it available in multiple places. One instance where this might be useful would be if an application needs regular data updates or constant access, but the network connection or server load aren't high enough to justify putting that application on its dedicated system somewhere else -- with database designers linking, you can have your cake and eat it too!

Related Posts: Database Development, Databases, SQL

Domain Modeling Made Functional by Martin Fowler The Domain Model pattern has become extremely popular over the last few years as object-oriented programming has become widely accepted as a solid way of modeling business systems. A domain model is a conceptual model of the business rather than a physical model of the data. Domain models are used to capture both behavior and entity details including validations, calculations, business rules, etc.

Developing a domain model for your application can be difficult because it requires carefully balancing how much detail you want to include versus how much you don't care about yet. One common mistake is to implement everything in one big chunk instead of taking an iterative approach by focusing on defining only the core elements needed to satisfy current requirements. This makes future changes more troublesome than they have to be since additional features will require corresponding updates throughout the domain model code base.

The solution is to use a functional approach that involves defining individual types that focus on describing one or two particular behaviors surrounding specific entities or interactions without worrying about the larger system. This allows your resulting domain model to be flexible and extensible without having to worry about changing it all in one large refactoring session when new requirements come along.

Logical design

Logical design is the process of mapping out your business domain to determine what types are needed and what each type should do. This is where most of your attention will go when building up your model since it's easy to get lost in all these different entities, associations, states, etc. The trick is to start simply by identifying only the core elements necessary for now and adding more details as they come along instead of doing everything at once.

Domain models also have certain properties that need to be considered before you can decide on a final design. These include things like performance requirements, how mobile or flexible the application needs to be, whether or not it will eventually use distributed computing techniques, etc. Each one of these factors has an impact on how you implement your domain model so it's not just important to define your domain but also to identify constraints that might affect how it needs to be implemented.

Object-Oriented Design by Craig Larman

From conversations I've had with people who are new to software engineering design, it seems that object-oriented design is one of the hardest things for them to get right. The problem is exacerbated by the fact that many developers learn their craft using a structured query language like C++ or Java which are statically typed and support multiple inheritances. While these features make type checking easier, they can lead you into thinking all problems can be solved using class hierarchies. In other words, if you don't know what objects in an OO system should do, just add another superclass! These people are usually left completely bewildered when they try to apply OO techniques in Ruby or Smalltalk, for example, since those languages don't offer multiple inheritances.

The problem is that object-oriented design requires you to think about your application in terms of the set of responsibilities--the functions that an object exposes as well as the messages it can receive--that each of its classes should encapsulate. This adds another level of complexity above and beyond just defining what an object should be able to do. Even taking this approach, one may find it difficult to keep track of all these things so I'm not surprised it's difficult for beginners.

One thing I've noticed though is that if developers take the time to learn how responsibility-driven design works, they'll quickly find that it becomes second nature. Responsibility-driven design is a technique for creating object-oriented class hierarchies that grew out of the recognition that too many OO hierarchies used inheritance to reuse code rather than to properly identify and separate responsibilities. There's been so much emphasis on using class hierarchies for this purpose that Spring framework creator Rod Johnson went so far as to say "Inversion of Control is new Object-Oriented Design".

The way responsibility-driven design works is by focusing on what objects do instead of how they're implemented. As you'll see in the next section, one way of thinking this is in terms of a class's public API instead of its implementation. To figure out what an object should do, you first need to identify all the tasks that can be performed on it by other objects and then group those into related functionality that will map onto a hierarchy of classes. This process is sometimes described as finding boundaries within which all changes within a class relate to each other; anything outside the boundary doesn't affect the class definition and thus shouldn't be included in it (e.g., database schema changes).

There's another way this idea applies: even if different aspects of an application may change at different rates, there should still only be one line of code that causes those changes to happen since any switch statements or conditional logic makes your design less flexible. Thus, the best way to identify responsibilities is by looking for differences in how objects change. This will tell you not only what an object should represent, but also how it shouldn't be implemented since the less interdependence between objects, the simpler your design can be.

The simplest way to ensure that all changes are made through one interface is to use delegation instead of inheritance or composition (although I give up on trying to convince people about this point). Delegation means that rather than creating a subclass, you create another object whose responsibility is to forward messages onto another target. There's nothing new here; even Smalltalk database programmers have known this for years. However, many developers who went through their OO education using SQL data definition language like C++ or Java typically found that delegation isn't as simple (or optimal) as it appears in these languages.

To understand the impact of delegation, consider this for a moment: if you have a method within an object and another method within a separate object that forwards messages to the first one, is there any difference between having those two methods defined directly inside each object or using delegation? The answer is no--aside from perhaps some extra runtime costs associated with forwarding messages. Of course, if you're dealing with database schema updates then those may affect your design somewhat but I've yet to see someone devise a solution that eliminates those problems.

In most modern programming languages, there's no reason why all objects should not only receive messages but also forward them on as well. The Java language allows either behavior through the use of interfaces, but database developers often bypass this technique by creating multiple concrete classes per single interface. This works as long as you don't run into situations where one class is about to be removed and another added that does completely different things (i.e., breaks polymorphism)--and these kinds of problems are far more likely to occur when using delegation than when using interfaces for forwarding messages.

You can also identify responsibilities at a higher level by focusing on what objects provide instead of how they're used. A good example comes from a project I worked on that provided a user-extensible view engine for rendering HTML from templates stored in XML files. There were three possible actions related to template processing: loading, processing, and rendering. Each of these actions was performed by a different object since different objects were responsible for doing the actual work--although there was only one line of code that performed each operation because they all called similar APIs on a single object.

The reason why this approach works is that it's easier to understand how processing occurs when you look at a single step instead of three separate ones. It doesn't matter if the user interface chooses which template needs to be rendered, loads it from disk, or currently identified user processes the XML into HTML; as long as those steps are all called from a common location then your design will perform properly without any additional effort on your part. In fact, another advantage of using delegation over inheritance is that it creates fewer dependencies between objects, which allows changes to occur more easily.

If you're interested in learning more about design then I recommend reading 97 Things Every Programmer Should Know. Although it's written for programmers who use C/C++ there are still many good ideas that can be applied to other languages as well. And although it's not related to the topic of this article, Design Patterns by Gamma et al. is also an excellent resource if you want more details on how object-oriented patterns can make your projects simpler and easier to maintain.

Finally, remember that nothing is set in stone; even if delegation isn't used within a particular application doesn't mean it should never be used when designing future applications. It may very well turn out that when considering all the pros and cons, delegation works out to be a better solution. In the end, you should always try to find the best database tools for any given job--no matter what language they're written in.

Database management system

(Database management system DBMS) is a computer program that interacts with the user, other programs, and the database administration itself to store and retrieve data.

Depending on the kind of DBMS you are using it may be named differently. For example, Microsoft Access is an example of file based-database corporate data management system (FBDMS). File-based databases imply that they do not run as separate server processes; instead, they use local or network files to store data. On the other hand, Oracle SQL Server 2005 is an example of a server process-based database management system (SCPDS). Server process-based databases imply that they run as separate server processes which reside outside the user's environment.

Terminology and overview

The development of a DBMS involves four steps: An analysis phase where the problem is identified and a functional specification for the database to be developed must be created. The second step is the database design phase where an overall data model for storage and access is specified. This results in the creation of a logical schema that will identify each table, its columns, primary key(s), foreign keys, indices, etc. The third step is the implementation or coding phase that transforms the logical schema into a physical schema (i.e., makes sure that data are stored in specific places). Once the physical schema has been defined it may need to undergo some changes before it can go into production; these changes are often referred to as migration phases. Lastly, there is the testing and deployment phase where the database is tested and then rolled out to its end users.

A database can be managed by a DBMS that supports one or more models. For example, if a software or hardware limitations of any system only supports a hierarchical model, then it cannot manage databases conforming to the relational model. Conversely, if it supports the relational model but not the hierarchical model, many things will have to be done manually which may result in numerous inconsistencies.

In general terms, a DBMS conforms to a particular data model. Some models are Hierarchical network-relational, etc.

The biggest problem with traditional file based-DBMS such as Microsoft Access is that they're limited by 2GB in their size capacity—i.e., the largest file they can support is 2GB. This poses a problem if you're working with extremely large tables and/or data sets that go over the 2GB limit.

Another problem associated with file-based DBMSs is that they tend to be slower than their server process counterparts, which makes them less efficient at handling very large datasets. The reason why they're slower stems from how Microsoft Access, for example, has to lock an entire table for writing when it opens it as opposed to allowing each record within a table to be modified independently of one another as in the case of SCPDS.