DBMS Tutorial For Beginners: Everything You Need To Know

Types of Database Management Systems

Database Management Systems can be classified into two types:

• Relational Database Management Systems: A relational database is a data store that organises data into connected tables, as the name implies. Structured Query Language (SQL) lies at the heart of these systems since it is used to interface with and administer these databases, giving rise to their second moniker — SQL databases. 

In relational databases, data is stored in rows (records) and columns (attributes) that follow a specified model(a.k.a schema), ensuring that data is organised logically. Generally, there is one value for each property in each record, creating evident relationships between distinct data points.

• Non-Relational Management Systems: A non-relational database is not tabular. It employs a variety of data models for saving, organizing, and obtaining information. They are referred to as NoSQL databases because they are not restricted to a table structure. 

They enable the storage of unstructured material such as texts, images, and various other file types. However, unlike in a relational database, data is not necessarily organised into rows and columns, as it would be in a flat-file system.

What are Keys in DBMS?

Keys are used to uniquely identify individual records or rows of data in the table. They also aid in the establishment and identification of links between table rows.

Different Types of Keys:

• Primary Key: This key is used to identify a single instance of an object. Each schema has only one primary key. This key can be a value or a string that is not repeated more than once in the entire table. For example, an employee ID is the Primary Key for salary table or schema of employees. The employee ID is unique, and no two employees will have the same employee ID.
• Foreign Key: Foreign keys are columns in a table used to refer to another table’s Primary key. A foreign key may also serve as the Primary key for another table.
• Composite Key: When a Primary key has many attributes(more than one), it is referred to as a Composite key.
• Candidate Key: Except the primary key, all other properties are considered candidate keys.
• Super Key: A super key is a collection of attributes used to identify a tuple uniquely.

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Basic SQL commands for Managing Databases

SQL commands are a set of guidelines. It is used to exchange information with databases. Aside from that, it is also used to conduct certain activities, procedures, and data searches.

The most used SQL commands are explained below:

• Data Definition Language(DDL): The DDL is used to create or edit database objects or the database’s structure, or to put it another way, to ‘define’ the objects or structure of the database.
• CREATE: In the database, the command ‘CREATE’ creates new tables.
• ALTER: The command ‘ALTER’ is used to add new columns or attributes to a table, or to modify the values of already-existing ones.
• TRUNCATE: The command ‘TRUNCATE’ is used to remove all of the rows from a table and cleanse the available space in the particular schema you are working with.
• DROP: The ’DROP’ command is used to remove or ‘DROP’ the structure and records that have been stored in the schema.
• Data Manipulation Language(DML): The DML is used to modify databases per the user’s wishes by performing queries in the database. These are the most often used commands while dealing with databases. Data may be changed by adding or removing values from the database and altering values already present in the database.
  • INSERT: This command is used to enter or ‘INSERT’ data into a row of a table or schema.
  • UPDATE: This command is used to ‘UPDATE’ the column value of a table or schema.
  • DELETE: This command removes or ‘DELETE’ rows from a table. The DELETE command can be combined with the ‘WHERE’ command to delete more than one row.
• Data Control Language(DCL): The DCL determines how users may access information stored in a database. For example, if a database has 50 users working on it, the Database Administrator may give or revoke access to certain areas of the database to the engineers.
  • GRANT: Access rights provide user access to a database using the ‘GRANT’ command.
  • REVOKE: It is basically the opposite of the ‘GRANT’ command. It removes a user’s permissions from one or more schemas or tables.
• Transaction Control Language(TCL): The TCL is used to ensure that the database remains consistent and to handle the transactions that are initiated by commands of the Data Manipulation Language(DML).
  • COMMIT: This command is used to save the current state of a database after the required queries have been executed.
  • ROLLBACK: This command is used to return to the point of all unsaved changes in the schema.
  • SAVEPOINT: This command is used to roll back to a certain point in time without having to roll back the whole transaction in its entirety.

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Top Careers to Pursue After Learning DBMS

Now you’ve taken the plunge and mugged up DBMS tutorial for beginners, it’s time to consider what careers can be pursued with this knowledge. Here are some of the top career paths for those who have a strong understanding of database management systems:

Database Administrator

A Database Administrator (DBA) is responsible for managing, maintaining, and troubleshooting databases in an organization. They store and organize data in order to ensure that it is accessible to users when needed. A DBA must also perform regular backups of the data in case of system failure or other issues. This role requires strong technical skills as well as a deep understanding of how databases work.

Data Analyst

Data Analysts use their knowledge of DBMS to collect, organize, and analyze data. They may use this data to find patterns or trends that can be used for decision-making within an organization. As a Data Analyst, you must be comfortable working with large datasets and using SQL queries to extract the information you need.

Database Developer

Database Developers are responsible for designing databases according to an organization’s business requirements. They create tables, store procedures, views, triggers, and other database objects in order to ensure that the data is properly stored and organized. Database Developers also write complex SQL scripts and queries in order to retrieve data when needed.

Business Intelligence Developer

Business Intelligence (BI) developers use their knowledge of DBMS to design and develop systems that allow organizations to collect, store, and analyze data. They create reports and dashboards using BI tools such as Tableau or Power BI in order to help management make informed decisions.

Data Scientist

Data Scientists use their knowledge of DBMS to gather, organize, cleanse, and analyze large datasets to discover hidden patterns and insights. This information is then used by businesses to improve their operations. To be successful in this role, you must have strong problem-solving skills as well as an understanding of various programming languages such as Python or R.

System Engineer

System Engineers are mostly responsible for planning, developing, and deploying databases within an organization. They also monitor the performance of existing databases and perform regular backups of data. To be successful in this role, you must have experience with database design and development as well as strong technical skills.

With a strong understanding of DBMS, there are many career paths to pursue. Depending on your interests and experience, you may pursue any of the above roles or even combine them for a more comprehensive job position. No matter which path you decide to take, good luck!

Curious about the foundational elements of database management? Learn about the types of keys in DBMS that play a crucial role in organizing, accessing, and maintaining data efficiently.

Conclusion

The need for highly skilled data-oriented employees is increasing with the proliferation of technological advancements in business. Because of the increased demand, the rivalry is becoming fiercer. Data scientists and analysts with a solid foundation in data science are in high demand by both large and small firms. They must stay updated with the data management systems on the market.

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