How do you learn database theory

Database design basics: how to get started

IBM proclaimed the data age back in 2014. Internet, video, call records, customer transactions, health records, news, literature, scientific publications, economic data, weather data, geospatial data, stock market data, etc. - data is the new currency for businesses and is at the heart of artificial intelligence (AI) and machine learning.

The points at which data is accumulated, structured and prepared for further use are databases. Those who are able to shape them not only have good job prospects, but also help to shape part of the future.


The most important basics in database design

A Database is a named collection of tables. It can contain, among other things, views, indices, sequences, data types, operators and functions.

With a command you send a string of characters to the server, which triggers a desired process. One of the most important commands is the Query. With their help, data is retrieved from the server. Commands are made in a framework Tables and Lines as you know it from Microsoft Excel, for example.

Two programs must work together so that both the issuing and the execution of commands are automated. On the one hand we have that serverthat stores, retrieves or changes data and, on the other hand, the Client. It asks a server to perform work and provide data. In contrast to the server, the client has a user interface. The central server component that manages all database files and all connections to the database server becomes Postmaster called. One and the same database usually allows different ones Views. Depending on the user group, the data can be prepared and made available in different ways. This also goes hand in hand with different levels of usage rights.

These basic concepts are usually used in the context of the most widely used SQL databases used. SQL stands for Structured Query Language and is used to manage data that is stored in a relational database management system. A good example of this is the body mass index. The weight and height of a large number of people are stored in a database in the form of tables and rows. The client can now request the server via SQL to provide the BMI, i.e. the data, size and weight in relation to one another.

So much for the basics. Let us now devote ourselves to the different structures, processes and of course the design of databases.


Different structures of database systems

At the most fundamental level, a distinction can be made between database systems in SQL and NoSQL:

SQL always refers to relational databases and is used in approx. 75% of all database systems. The advantages are obvious: This technology was developed back in the 1970s, is offered by major players such as Microsoft, Oracle or IBM and is accordingly mature. The standards are clearly defined, generally recognized and run on all common operating systems. Accordingly, many different user groups such as developers, data analysts and logisticians are familiar with it.

But in recent years NoSQL solutions, i.e. non-relational database systems, have been on the rise. Why? Relational database systems do not work well, or not at all, with unstructured or semi-structured data due to schema and type restrictions. This makes them unsuitable for large analysis or IoT event loads.

This is precisely the crucial difference between the two structures. Relational database systems define exactly how all data inserted into the database must be typed and put together, while NoSQL databases can be schema-independent so that unstructured and semi-structured data can be stored and processed. They are therefore more flexible and easier to manage. In addition, they are to a high degree fault-tolerant. However, the technology is far from being as mature as that of SQL and less standardized.


The goals of a database design

A well-structured database enables the simultaneous, fast and error-free provision of data. In order to achieve this goal in the best possible way, it is worth prioritizing the following functionalities.

The database supports the retrieval of both required and unplanned ad hoc information. The database must be designed in such a way that it stores the data necessary for the support, the defined information requirements and possible ad hoc queries of the users.

The tables are set up correctly and efficiently. Each table in the database can only display a single subject and should consist of relatively different fields that keep redundant data to an absolute minimum.

Data integrity is determined at the field, table, and relationship levels. These integrity levels ensure that the data structures and their values ​​are always valid and as accurate as possible.

The database should be suitable for future growth and development. The database structure should be easy to change and dispensable as the company's information requirements continue to change and grow.

The database is continuously maintained and updated. Nobody likes to clean up, but a well-maintained database saves time and money for the entire company.


The benefits of good database design

Easier retrieval of information: When the design is developed correctly, information is easier to get hold of. Proper design means that the tables, constraints, and relationships created are error-free.

Easy change: The design is perfect when changes in one field do not affect changes in another field.

Better information: With a good design, you can improve the quality and consistency of existing data.

In addition to these equally obvious and central advantages, there is much more to consider when designing databases.

The database should be strong enough to store all relevant data and requirements. Multiple users should be able to access the same database without affecting the other user. For example, multiple teachers can work on a database at the same time to update students' grades. Teachers should also be allowed to update grades for their subjects without changing other grades. A single database offers different views to different users. For example, in a school database, teachers can see the breakdown of student grades. However, parents can only see their child's report, so the parent's access is read-only. At the same time, the teachers have access to all information and assessment details of the learners with change rights. All of this can be done in the same database.


How do I design a database in 6 steps?

1. Define the goal of your database.

For the basic structure, it is crucial whether relational data or non-relational data are to be evaluated. As already described, relational databases are the most common. A simple example of this are the well-known customer cards. With every purchase, data is saved in the database. As a customer, you have a column there and the corresponding lines are continuously filled with data - for example, when you do your shopping, in which branches you shop, how big your average shopping cart is, etc. But if your database is to provide a chatbot with situation-related commands, then a NoSQL, i.e. a non-relational database system, is recommended.

2. The right choice of data modeling software.

There are currently many online tools available for database design such as Lucidchart, and Microsoft Visio, all of which support the design of database entities. The whole point of using data modeling is to visualize the complexity and identify shortcomings that can be improved upon.

3. Fill your database with the appropriate data.

So that the data can be modeled or, put simply, converted into understandable diagrams, the data must be entered and divided into subject areas. The 3 basic data types are strings, numbers, and time and date. Let's just assume you were a retailer with suppliers, customers and products, then each of these 3 areas would represent a separate entity.

4. Identify the primary key.

The next step in improving your database design is to choose a primary key for each table. This primary key is a column or set of columns that is used to uniquely identify each row. For example, in your customer table, the primary key could be the customer ID. In this way you can clearly assign each line based on the ID.

5. Determine how your table should be linked.

Now the information in your tables needs to be merged in a meaningful way. In general, it is important to know that there are different types of relationships.

  • At asymmetrical relationship a change in A affects the value of B but not vice versa.
  • At symmetrical relationships A and B influence each other.
  • In reflexive relationships a reference value or average value of A is established and any new information concerning A causes this reference value to be changed. For example, the temperature is measured every day in Austria and an average for the last year is calculated from this. Each new temperature measurement changes the reference value A.
  • Transitive Relationships state that a change in A affects the value of B and that in turn affects the value of C. Consequently, A also directly affects the value of C.

You slowly realize that a certain mathematical understanding is definitely an advantage.

6. Implement the normalization rule.

The final step is to implement the normalization rules for your database design. It is a systematic approach that eliminates redundancy and undesirable features such as anomalies in inserting, updating, and deleting. This multi-step process saves data in tabular form, which removes redundant data from the relationship tables.


More on the topic of databases

Are you interested? Anyone with an enthusiasm for mathematics, technology and complex relationships can look forward to a promising future in this industry. In the meantime, word has got around in every large company that the power of data is the secret of the success of Amazon, Google, Facebook and Co.

So if you already have a basic understanding of databases and want to improve your job prospects, here's how to design your first database. As soon as this is in place, the question arises as to how you can best visualize the hidden insights and make them accessible to the company. Here, too, we have the most important tools in our article Data visualization: the tools you absolutely need to know already summarized for you.

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