What we want from a properly designed database is to be consistent, cost-effective, and easy to maintain. However, database development tasks are complex and require precision. At the same time, the speed of work is a key factor in many processes. All tasks must be done faster.
But what is “faster” in terms of database development? Does it mean that, as databases consist of SQL code, the developers should just code faster? It would be wrong to equate thoughtful coding with simple typing. Plenty of tools help us achieve faster typing. However, the resulting code is judged by its quality.
Fast database development requires that you understand how to do it most effectively. Then you can implement your ideas through the code written according to strict rules — and you can use professional tools that help you do it faster.
Planning and Visualization
The process of database development starts with the initial study of requirements. You define the purpose of the database: the types and volume of data it will store, the tables that you will need, the items you will store, and which details about each item you need. It also requires looking at the database from the user’s point of view – what information they will need to retrieve from that database. Once you understand it clearly, you can start developing the database.
The most common database type used in applications is a relational database, which stores information in tables that are interrelated with each other. The key demand is to ensure the accuracy and efficiency of those relations. To make the database work, developers establish these links, assign identifiers, and take care of all database objects.
It could be done under Oracle, MySQL, SQL Server, PostgreSQL, or another database management system. The choice of the optimal DBMS is never random; it depends on your needs, preferences, and, in many cases, the technical specifics of your project.
The database structure is the most important task at the planning stage. This is where the ER modeling technique helps developers speed up the database design process and ensure accuracy.
Let’s talk about it in detail. ER (Entity Relationship) modeling creates a comprehensive database model in a visual format. It includes all the database elements along with their relations and allows database developers to edit them. Diagrams let them specify and change the data types, name, rename, and edit the existing database objects, add or delete them, set and change relations, etc.
All in all, an ER diagram is a visual model of the database. It lets database developers understand how different users will interact with the software that, naturally, includes the database in question. The more detailed and accurate this diagram is, the better the database will work for all users.
See how it looks in dbForge Studio for SQL Server, a good example of an IDE designed to effectively assist database developers.
Thanks to visual tools, this stage of database development can be passed really fast. There is no need for typing the DLL of tables and establishing relationships between them. Instead, just draw tables on a diagram and tie them visually.
Forward engineering allows developers to create and alter databases visually. Database tools that support forward engineering can generate SQL scripts from the ER diagram – a collection of ready, accurate scripts for creating the database with all of its objects and relations. Execute these scripts, and the visual model becomes the physical database schema for the required database management system.
The visual model can be reused whenever you need to change anything in the physical database: just make amendments to the diagram and use forward engineering to apply those changes to the database schema.
However, there will still be lots of tasks remaining. To perform them, coding is necessary.
Database Coding
Now if we speak of database coding, it usually relates to programming and debugging stored procedures as well as some other database objects.
Your SQL code must be of excellent quality in terms of readability and structure, regardless of its complexity. Any database developer’s goal is to produce high-quality code that works correctly — and to produce it fast.
What is the best way to achieve this level of professionalism? It is the combination of in-depth knowledge of programming languages, frameworks, relations, and methods, understanding SQL coding best practices, and using appropriate tools that assist coding. A must-have feature for fast database development is a tool that delivers SQL autocomplete.
IntelliSense-like Suggestions
Typing code is a rather tiresome routine and much more demanding than typing conventional text. First off, it deals with a programming language. SQL has strict spelling and syntax rules, which are the basis of the code completion functionality.
How does it work? Well, instead of typing the entire element name (as a rule, extremely demanding to every symbol), the developer starts typing, and the code completion tool suggests how the phrase should be completed. The efficiency of the tool is measured by its capability of giving appropriate and precise suggestions.
In dbForge Studio for SQL Server, which we used to illustrate ER diagrams, the code completion functionality is implemented as well. It has a dedicated module that identifies the most relevant suggestions for the current context and displays them on the list, sorted by relevance and object type.
The developer selects the correct suggestion, and the tool inserts it accurately. It takes significantly less time than typing the full phrase manually.
Effective tools provide plenty of additional options to simplify routine coding. For instance, SQL Complete, an SSMS add-in for code completion, can also suggest methods for variables and columns, assign aliases to table objects automatically, insert unique matches at once, and perform many other tasks.
Reuse of Code Fragments
Repeated sections are common in the code, whether they comprise just a couple of lines or lengthy fragments with many keywords, variables, and other elements. Therefore, it seems logical to reuse such fragments instead of typing them anew every time.
Developers save code fragments and templates for recurring use. To help them organize such fragments and access them easily, coding assistance tools provide libraries of snippets with editable templates and the possibility to add custom snippets to the collection.
Thus, database developers can see which code fragment they need and where it should be inserted. They simply select the necessary fragment from the library and place it into the code.
Code Debugging
SQL developers must always focus on code quality. The goal is to maintain it at an all-time high from the very beginning. Still, even with the most advanced coding tools, absolute accuracy is not guaranteed, and code debugging is necessary.
Debugging lets you detect errors in the code you compose and fix them at once. It helps you get rid of errors, incorrect constructions, and excessive lines.
You can edit and correct the code to improve it and make it clean and efficient. And if debugging becomes automated, it saves you even more time.
There is one more benefit: code debugging teaches database developers to prevent such issues in the future.
Code completion with context-aware suggestions, reusable snippets, and automated debugging are the solid database development pillars to rely on in daily work. They help any developer reach the desired level of coding skills – great quality and fast delivery.
But it does not end here.
Database Testing
At this stage, the database schema and all procedures are firmly in place. However, it does not mean the new database is ready to work in the production environment. Before the tables are populated with data, and before the entire solution is all set to work for end users, the developer must make sure that every piece of code works correctly.
Unit Tests
Unit testing is a specific method of software testing. It suggests checking and validating source code units – program modules with their usage and operating procedures and associated control data. The purpose of unit testing is to define if such units work correctly.
Unit tests are isolated – they apply to the particular source code module only. There is no need to apply tests to the entire database. If problems are detected, developers can fix them and update their code without worrying about breaking anything related to that module.
The role of unit tests could hardly be overestimated. It is the method that allows you to understand if the code is correct in every component. Developers can test every individual view, trigger, procedure, or transaction, and get consistent results.
These tests take place in the initial development stages. With all bugs and issues detected and fixed before rolling the code out, the application that reaches end users becomes much more stable and efficient.
Database testing is vital, and it is impossible to omit this stage if you want the database to perform correctly. However, the time factor remains. Is it possible to make unit testing faster? Yes.
First, unit tests should be automated. They run faster by default, and the results are more precise. Second, it is possible to accelerate the process of writing unit tests.
To create effective unit tests, developers need to have in-depth knowledge of SQL and DML. Here, specialized tools can simplify this job. For example, the Unit Tests module below uses the tSQLt framework that allows writing unit tests in regular T-SQL and configuring their execution.
There are no silver bullets in software development, but thorough unit testing is very close to that concept in terms of ensuring the correctness of work. All tests require time and precision, but automation via specialized tools makes it possible to accomplish the goals much faster.
One more thing: unit tests are not the only testing method. Database development, management, and administration suggest many other tests, especially when data processing is the focus.
Test Data Generation
Should developers use the actual data stored in the production database for testing? It depends on their specific needs and data management policies. Still, many scenarios suggest that it would be better to use test data instead. Still, test data must be realistic and meaningful to serve the testing purposes properly. With the help of a specialized data generation tool, it is possible to generate any volume of test data quickly and easily.
Conclusion
Database development is a process that requires both creativity and patience. And the speed of delivery is always essential. Lots of best practices, directly related to SQL coding, focus on improving the speed by equipping the developers with tips on resolving tasks faster — and with the proper tools to help them do it. All in all, database development tasks can become much simpler and faster when you have powerful coding assistants at hand.
