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With the rapidly evolving technological world, businesses are constantly contemplating the debate of traditional vs vector databases. This blog delves into a detailed comparison between the two data management techniques.

In today’s digital world, businesses must make data-driven decisions to manage huge sets of information. Hence, databases are important for strategic data handling and enhanced operational efficiency.

However, before we dig deeper into the types of databases, let’s understand them better.


Understanding databases

Databases are a structured way to store and organize data effectively. It involves multiple data handling processes, like updating, deleting, or changing information. These are important for efficient data organization, security, and control.

Rules are put in place by databases to ensure data integrity and minimize redundancy. Moreover, organized storage of data facilitates data analysis, enabling retrieval of useful insights and data patterns. It also facilitates integration with different applications to enhance their functionality with organized access to data.

In data science, databases are important for data preprocessing, cleaning, and integration. Data scientists often rely on databases to perform complex queries and visualize data. Moreover, databases allow the storage of training datasets, facilitating model training and validation.


Read more about Understanding Databases


While databases are vital to data management, they have also developed over time. The changing technological world has led to a transition in available databases. Hence, the digital arena has gradually shifted from traditional to vector databases.

Since the shift is still underway, you can access both kinds of databases. However, it is important to understand the uses, limitations, and functions of both databases to understand which is more suitable for your organization. Let’s explore the arguments around the debate of traditional vs vector databases.


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Exploring the traditional vs vector databases debate

In comparing the two categories of databases, we must explore a common set of factors to understand the basic differences between them. Hence, this blog will explore the debate from a few particular aspects, highlighting the characteristics of both traditional and vector databases in the process.


traditional vs vector databases
Traditional vs vector databases


Data models

Traditional databases:

They use a relational model that consists of a structured tabular form. Data is contained in tables divided into rows and columns. While each column represents a particular field, each row represents a single record within that field. Hence, the data is well-organized and maintains a well-defined relationship between different entities.

This relational data model holds a rigid schema, defining the structure of the data upfront. While it ensures high data integrity, it also makes the model inflexible in handling diverse and evolving data types.

Vector databases:

Instead of a relational row and column structure, vector databases use a vector-based model consisting of a multidimensional array of numbers. Each data point is stored as a vector in a three-dimensional space, representing different features and properties of data.

Unlike a traditional database, the vector representation is well-suited to store unstructured data. It also allows easier handling of complex data points, making it a versatile data model. Its flexible schema allows better adaptability but at the cost of data integrity.


Based on the data models of both databases, it can be said that when making a choice, you must find the right balance between maintaining data integrity and flexible data-handling capabilities. Understanding your database requirements between these two properties will help you towards an accurate option.


Here’s your guide to top vector databases in the market


Query language

Traditional databases:

They rely on Structured Query Language (SQL), designed to navigate through relational databases. It provides a standardized way to interact with data, allowing data manipulation in the form of updating, inserting, deleting, and more.

It presents a highly focused method of addressing queries where data is filtered using exact matches, comparisons, and logical operators. SQL querying has long been present in the industry, hence it comes with a rich ecosystem of support.

Vector databases:

Unlike a declarative language like SQL, vector databases execute querying through API calls. These can vary based on the vector database you use. The APIs perform similarity searches and nearest-neighbor operations as part of the querying process.

The process is based on retrieving similar data points to a query from the multidimensional vector space. It leverages indexing and search techniques that are suitable for complex vector databases.


Hence, query language specifications are highly particular to your choice of a database. You would have to rely on either SQL for traditional databases or work with API calls if you are dealing with vector spaces for data storage.


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Indexing techniques

Traditional databases:


Different data representation in a Hash and B-Tree Index
Different data representation in a Hash and B-Tree Index – Source: IT Tutorial


Indexing techniques for traditional databases include B-trees and hash indexes that are designed for structured data. B-trees is the most common method that organizes data in a hierarchical tree format. It assists in the efficient sorting and retrieval of data.

Hash indexes rely on hash functions to map data to particular locations in an index. On accessing this location, you can retrieve the actual data stored there. They are integral for point queries where exact matches are known.

Vector databases:

HNSW and IVF are indexing methods that specialize in handling vector databases. These differentiated techniques optimize similarity searches in high-dimensional vector data.


A visual representation of HNSW
A visual representation of HNSW – Source: Pinecone


HNSW stands for Hierarchical Navigable Small World which facilitates rapid proximity searches. It creates a multi-layer navigation graph to represent the vector space, creating a network of shortcuts to narrow down the search space to a small subset of similar vectors.

IVF or Inverted File Index divides the vector space into clusters and creates an inverted file for each cluster. A file records vectors that belong to each cluster. It enables comparison and detailed data search within clusters.

Both methods aim to enhance the similarity search in vector databases. While HNSW speeds up the process, IVF also increases its efficiency.


While traditional indexing techniques optimize precise queries and efficient data manipulation in structured data, vector database methods are designed for similarity searches within high-dimensional data, handling complex queries such as nearest neighbor searches in machine learning applications.


Learn more about the mystery of indexing


Performance and scalability

Traditional databases:

These databases manage transactional workloads with a focus on data integrity (ACID compliance) and support complex querying capabilities. However, their performance is limited due to their design of vertical scalability, making it a costly and hardware-dependent process to handle large data volumes.

Vector databases:

Vector databases provide distinct performance advantages in environments requiring quick insights from large volumes of complex data, enabling efficient search operations. Moreover, its horizontal scalability design promotes the distribution of data management across multiple machines, making it a cost-effective process.


Performance-based decisions can be made by finding the right balance between data integrity and flexible data handling, similar to the consideration of their data model differences. However, the horizontal and vertical scalability highlights that vector databases are more cost-efficient for large data volumes.


Use cases

Traditional databases:

They are ideal for applications that rely on structured data and require transactional safety while managing data records and performing complex queries. Some common use cases include financial systems, E-commerce platforms, customer relationship management (CRM), and human resource (HR) systems.

Vector databases:

They are useful for complex and multimodal datasets, often associated with complex machine learning (ML) tasks. Some important use cases include natural language processing (NLP), fraud detection, recommendation systems, and real-time personalization.


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The differences in use cases highlight the varied strengths of both databases. You cannot undermine one over the other but understand both databases better to make the right choice for your data. Traditional databases remain the backbone for structured data while vector databases are better adapted for modern datasets.



The final verdict

Traditional databases are suitable for small or medium-sized datasets where retrieval of specific data is required from well-defined links of information. Vector databases, on the other hand, are better for large unstructured datasets with a focus on similarity searches.

Hence, the clash of databases can be seen as a tradition meeting innovation. Traditional databases excel in structured realms, while vector databases revolutionize with speed in high-dimensional data. The final verdict of making the right choice hinges on your specific use cases.

March 8, 2024

As data-driven decision-making gains popularity, more tech graduates are learning data science to enter the job market. While Python and R are popular for analysis and machine learning, SQL and database management are often overlooked.

However, data is typically stored in databases and requires SQL or business intelligence tools for access. In this guide, we provide a comprehensive overview of various types of databases and their differences.

Through this guide, we give you a larger picture to get started with your database journey. So, if you are a beginner with no prior experience, this guide is a must-read for you 

What is a database? 

Databases are used to store and organize large amounts of data in a structured way. They are designed to manage and handle large volumes of information efficiently and effectively, making it easy to retrieve, update, and delete data as needed.

In simple terms, it is a collection of data that is organized in a specific way, making it easy to search, sort, and analyze. It is like a digital filing cabinet, where information is stored and accessed by different users, applications, or systems.

There are various types of databases, such as relational, NoSQL, and object-oriented, each with its own unique characteristics and applications. However, the core purpose of any database is to provide a centralized and secure location for storing and managing data, ensuring data consistency and accuracy, and making it accessible to authorized users or applications.

Understanding databases
Understanding databases

Types of databases

There are several types of databases that are used for different purposes. The main types of databases include:

1. Relational databases:

A relational database is the most common type of database used today. It stores data in tables that are related to each other through keys. Each table in a relational database has a unique primary key, which is used to link it to other tables. They use Structured Query Language (SQL) for managing and querying data. Some popular examples of relational databases are Oracle, Microsoft SQL Server, MySQL, and PostgreSQL.

2. NoSQL databases

NoSQL databases are used for unstructured and semi-structured data. They do not use tables, rows, and columns like relational databases. Instead, they store data in a flexible format, such as key-value pairs, document-based, or graph-based. NoSQL are commonly used in big data and real-time applications. Some popular examples of NoSQL databases are MongoDB, Cassandra, and Couchbase.

3. Object-oriented databases

Object-oriented databases store data in objects, which are similar to the objects used in object-oriented programming languages like Java and C#. They allow for complex data relationships and provide a more natural way of storing data for object-oriented applications. They are commonly used in computer-aided design, web development, and artificial intelligence. Some popular examples of object-oriented databases are ObjectDB and db4o.

4. Hierarchical databases

Hierarchical databases organize data in a tree-like structure, with each record having one parent record and many child records. They are suitable for storing data with a fixed and predictable structure. These were popular in the past, but they have been largely replaced by other types of databases. IBM Information Management System (IMS) is a popular example of a hierarchical database.

5. Network databases

Network databases are similar to hierarchical databases, but they allow for more complex relationships between records. In a network database, each record can have multiple parent and child records. They are suitable for storing data with a complex structure that cannot be easily represented in a hierarchical database. They are not widely used today, but some examples include Integrated Data Stores (IDS) and CA-IDMS.

What is RDBMS?

RDBMS stands for Relational Database Management System. It is defined as a type of database management system that is based on the relational model. In an RDBMS, data is organized into tables and relationships between tables, allowing for easy retrieval and manipulation of the information. The most popular RDBMSs include MySQL, Oracle, PostgreSQL, SQL Server, and SQLite. 

  1. MySQLMySQL is an open-source RDBMS that is widely used for web-based applications. It is known for its high performance, reliability, and ease of use. MySQL is compatible with a wide range of operating systems, including Windows, Linux, and macOS.
  2. OracleOracle is a commercial RDBMS that is widely used in enterprise environments. It is known for its high performance, scalability, and security. Oracle is compatible with a wide range of operating systems, including Windows, Linux, and Solaris. 
  3. PostgreSQLPostgreSQL is an open-source RDBMS known for its advanced features, such as support for complex data types, concurrency control, and full-text search. It is widely used in data warehousing, business intelligence, and scientific applications.
  4. SQL ServerSQL Server is a commercial RDBMS developed and maintained by Microsoft. It is known for its high performance, scalability, and security. SQL Server is compatible with Windows operating system only. 
  5. SQLiteSQLite is a small, lightweight RDBMS that is embedded into the application. It is known for its high performance, reliability, and ease of use. SQLite is compatible with a wide range of operating systems, including Windows, Linux, and macOS.

Database design

Designing a database is a critical step in creating a functional and efficient database system. It involves creating a structure that will organize the data and enable efficient storage, retrieval, and manipulation. The following are the key components of design:

Designing a database

Designing a database involves identifying the data that needs to be stored and organizing it into tables that are related to each other. The tables should be designed in a way that minimizes redundancy and ensures data consistency.

Entity-relationship diagrams (ERD)

An entity-relationship diagram (ERD) is a visual representation of the its structure. It shows the tables, their relationships, and the attributes that are stored in each table. ERDs are essential as they provide a clear and concise view of the database structure.


Normalization is the process of organizing data in a database to minimize redundancy and ensure data consistency. It involves breaking down large tables into smaller, more manageable tables that are related to each other. Normalization helps to eliminate data redundancy and ensures that each table contains only the data that is relevant to it.

There are several levels of normalization, with each level building upon the previous level. The most common levels of normalization are:

  1. First Normal Form (1NF)
  2. Second Normal Form (2NF)
  3. Third Normal Form (3NF)
  4. Boyce-Codd Normal Form (BCNF)

Normalization is an important aspect of design as it helps to minimize data redundancy, ensure data consistency, and improve its performance.

What is SQL?

SQL is used to manage and manipulate databases. Whether you are a beginner or a seasoned developer, understanding the basics of this programming language is essential for anyone working with data.  

Types of SQL commands 

First, let us talk about the several types of SQL commands. SQL commands are grouped into four main categories:  

1. Data definition language (DDL) – DDL commands are used to create and modify a database’s structure, such as creating tables, altering table structures, and deleting tables. Some examples of DDL commands include CREATE, ALTER, and DROP. 

2. Data manipulation language (DML) – DML commands are used to manipulate the data within a database. These commands include SELECT, INSERT, UPDATE, and DELETE.  

3. Data control language (DCL) – DCL commands are used to manage access such as granting and revoking permissions. Examples of DCL commands include GRANT and REVOKE. 

4. Data query language (DQL) – Primarily, DQL commands are used to query the data. Most used commands include SELECT which are used to retrieve data from a table. 

Difference between SQL and NoSQL 

One of the main differences between SQL and NoSQL databases is how they store and retrieve data. SQL databases use tables and rows to store the data, while NoSQL databases use documents, collections, or key-value pairs. SQL databases are better suited for structured data, while NoSQL databases are better suited for unstructured data. 

Another difference between SQL and NoSQL databases is the way they handle scalability. As these databases are vertically scalable, SQL databases can handle more load by adding more resources to the same server. NoSQL databases are horizontally scalable and can handle the additional load by adding more servers. 

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In conclusion, this guide provides a comprehensive overview of various types and their differences, including relational, non-relational, object-oriented, hierarchical, and network databases. Designing a database is a critical step in creating a functional and efficient database system. By understanding the types and their unique features, you can choose the right database for your specific use case and design one that meets your data management needs.

April 6, 2023

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