Pick the Best Database Type for Your Next Project
This blog post is a result of our collaboration with the University of Padua, wherein the student Isil Atabek played a significant role in selecting the topic and contributing a major portion of the content.
Choosing the right database platform that aligns with your project’s requirements is a shared objective that might be accompanied by a certain level of confusion.
In this blog post, we will provide a brief description of six different database types, emphasizing their pros and cons, and we will share some useful tips for selecting a suitable database for your project.
Let’s delve into the details and find out which type of database to choose.
What is a Database?
To begin with, it is important to understand what a database is.
A brief explanation of databases is that they are an essential part of modern technology and are used to store, manage, and retrieve large amounts of data.
Databases collect and organize data in a manner that facilitates convenient access, management, and updates for users; they are used in various applications and industries to handle and manipulate data effectively.
There are several types of databases, each having its strengths and weaknesses.
Database Types:
When it comes to databases, it is difficult to give an exact number of how many there are, due to the continuous development and proposals of new database systems and designs.
However, for the purpose of this blog, we will focus on six commonly recognized and widely used database types:
1) Relational Databases
Relational databases are the most common type of databases and are based on the relational model, which organizes data into tables, which consist of rows and columns. Each row represents a record or an entity, while each column represents a specific attribute or field of that record.
A relational database emphasizes relationships between tables through keys, follows ACID (Atomicity, Consistency, Isolation, and Durability) properties for data integrity, and uses SQL for queries.
Popular relational database management systems (RDBMS) include Oracle Database, MySQL, PostgreSQL, Microsoft SQL Server, and SQLite.
- Well established
- Popularly used
- Advanced tools and libraries
- Robust data integrity and consistency
- Support advanced querying and indexing
- Poor performance in unstructured data
- Slow when handling very large scaled data
2) NoSQL Databases:
Unlike relational databases, NoSQL databases (or “not only SQL” databases) are designed to handle large scales of unstructured or semi-structured data, as they allow for a schema-less or flexible schema design, thus enabling the storage of diverse data types and structures.
They are preferred for big data and real-time web applications but may not be suitable for those requiring complex transactions and strict data consistency.
Examples of NoSQL database platforms include MongoDB, Cassandra, Redis, and Couchbase.
- Designed to handle unstructured data
- Convenient for big data and real-time web applications
- Horizontally scalable
- Weaker consistency (compared to relational databases)
- Less advanced tooling (compared to relational databases)
- Less querying and indexing capability (compared to relational databases)
3) Object-Oriented Databases:
Object-oriented databases (OODBs) are based on an object-oriented programming paradigm.
In contrast to relational databases that use tables and rows to organize data, they store data as objects, which encapsulate both data and the methods (operations) that can be performed on that data.
An entity is represented as an object (which has members such as fields, properties, and methods) and objects are stored in memory.
OODBs typically support these four attributes that enhance modularity, code reuse, data integrity, and abstraction: Polymorphism, Inheritance, Encapsulation, and Abstraction.
Examples of object-oriented database platforms include Db4o, ObjectDB, GemStone/S, etc.
- Designed to handle complex data structures
- Good balance between flexibility and performance
- Less mature and supported (compared to relational databases)
- Less querying and indexing capability (compared to relational databases)
4) Graph Databases:
Graph databases are a type of NoSQL database that store and manage data using nodes to represent entities, edges to represent relationships between entities, and properties to store additional information about nodes and edges. They are often used for applications such as recommendation engines, social networks, and fraud detection.
Examples of graph database platforms include Neo4j and OrientDB.
- Designed to handle graph data
- Designed to handle highly connected data
- Less mature and supported (compared to relational databases)
- Less querying and indexing capability (compared to relational databases)
5) Time-Series Databases:
Time-series databases are designed for managing and querying time-stamped data, capturing the sequence and temporal ordering of events. They are optimized for handling large volumes of data points that are ordered chronologically, such as sensor readings, stock market prices, website clickstream data, server logs, and other time-stamped events.
Examples of time-series database platforms include InfluxDB, TimescaleDB, and OpenTSDB.
- Designed to handle time-series data
- Designed to handle complex time-based queries and aggregations
- Less mature and supported (compared to relational databases)
- Less querying and indexing capability (compared to relational databases)
6) In-Memory Databases:
In-memory databases are a type of database management system (DBMS) that primarily relies on main memory (RAM) for data storage. The purpose is to gain faster access to data, providing significant performance benefits compared to disk-based systems. Because of this, they are commonly preferred for real-time data processing.
Examples of in-memory database platforms include Redis, Memcached, and Aerospike.
- Fast access to data
- Convenient for real-time data processing
- Limited data storage capacity
- More expensive to run (compared to disk-based databases)
- Volatile storage
Other Factors to Pay Attention
After briefly exploring the various types of databases, it is crucial to consider several factors that will influence your decision-making process. Let’s break down each factor:
– Data Structure: understanding the nature of your data is essential. Different database platforms have distinct data structures, and the choice you make can impact the performance of your project.
– Query Requirements: consider the types of queries you will be executing on your data. It is important to choose a database that offers features and optimizations aligned with your query needs. This ensures that your project performs efficiently and delivers the desired results.
– Performance: evaluate the performance requirements of your project. Select a database that can effectively handle the expected load and deliver the necessary performance for your application.
– Scalability: anticipate the growth of your data over time. Failing to consider scalability may result in choosing a database that cannot accommodate your expanding data requirements. Ensure that the chosen database can scale seamlessly to handle the increasing volume of data in the future.
– Ecosystem: take into consideration the ecosystem surrounding the database. Choosing a database with a mature and robust ecosystem can simplify your development process.
– Cost: some databases may come with higher operational costs, including licensing fees or infrastructure requirements. It is important to align your database choice with your budget and ensure that the costs associated with the selected database are feasible for your project.
By carefully considering these factors and their implications, you can make an informed decision about the type of database best suited to your specific needs.
Tips!
After gaining knowledge about different database types and the factors to consider in their selection, here you can find some data scenarios along with the appropriate database types that are best suited for each case:
- Online e-commerce websites –> Relational databases
Online e-commerce websites mainly deal with large-scale structured data, including product, customer, and purchase details. Therefore, it is wise to use relational databases since they provide high data integrity, consistency, and support for querying and indexing. - Social media platform –> NoSQL databases
Social media platforms mainly deal with large scaled unstructured data, including posts, images, and videos. Therefore, it is wise to use NoSQL databases since they are designed to handle large amounts of unstructured data. Moreover, being horizontally scalable, they are well suited to big data and real-time web applications. - Gaming/ online gaming –> In-memory databases
Gaming or online gaming applications deal with large amounts of data, including profiles and game scores. It is wise to use In-memory databases since they are specifically designed for such data and use cases, providing fast data storage and retrieval due to their high-speed data access capabilities.
Conclusion
In conclusion, selecting the appropriate database platform for a project is a crucial decision that can determine the success and efficiency of the system.
By understanding the pros and cons of different database types discussed in this blog post, we hope you can make an informed decision.
If you have any questions, please leave a comment below!
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This blog post is part of our collaboration with the University of Padua.
If you are a University student or professor and want to collaborate, contact us through e-mail.
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