An Overview of the Different Database Types for Python Backend Development

As a backend developer understanding your database is as critical as your code itself. It is common for developers to learn a popular database and use it for various applications. However, there are better approaches than this in practice since each database has strengths and weaknesses.

This article highlights the advantages and disadvantages of the different databases we have and their ideal use cases.

Introduction.

What's a database article without a definition of the term?
Databases are collections of data organized and stored to allow easy access, manipulation, and analysis.
There are two major types of databases: relational and non-relational databases.

The significant difference between these two is that one stores data in a table-like structure and the other in a JSON and unstructured way.
This means that for a relational database, you have columns that need filling every time you enter new data. In contrast, with a non-relational database, you need to push a JSON-like object with zero regards for what's inside the object.

Relational Databases.

This is known as the oldest database form, arguably the most popular form of database today. It uses a query language called SQL (Structured query language) pronounced "sequel" Don't ask me why, to define and manipulate data.

A spreadsheet with rows and columns is one way to visualize a relational database. Every data input has to fit the already defined structure. Only this time, instead of using a mouse and a keyboard, you write SQL.

Advantages and disadvantages:

Already we can begin to imagine why this is popular.
For starters, data integrity. You don't have to worry about whether your data is valid, accurate, and consistent, as the row and column system was designed specifically for that purpose. With features like primary key, foreign key, and column type ( columns for everything including text, email, number, binary, etc.), your data is as accurate as possible.

It is also easy to remove redundant data, as removing it is as easy as dropping a column. This would be difficult in the other type of database as there is no structure, to begin with.
You can drop columns you no longer need in your application and make your application as light as possible.

As exciting as relational databases sound, they have three scary words to define their limitations: scalability, performance, and rigidity.

Relational databases have issues handling large amounts of data without affecting performance.

There are also issues with performance generally as a lot is taken into account, like schema design, query optimization, and hardware configuration. Each of these factors affects the speed in some way.

Last but not least rigidity. It is impossible to change the schema of a relational database without affecting the application or the data.

Popular Options.

Some popular relational databases include PostgreSQL, MySQL, SQLite, and Oracle.

The PostgreSQL database supports advanced features such as JSON data types, full-text search, geospatial queries, and user-defined functions.

The MySQL database is another open-source database that is widely used for web applications due to its high performance, scalability, and compatibility with many platforms.

An SQLite database is a lightweight database that runs within an application rather than on a separate server. For applications that only require a little concurrency or security, SQLite is a great choice.

With Oracle, you can take advantage of enterprise-grade features such as high availability, security, backup and recovery, and analytics.

Non-Relational Databases.

Also known as Non-SQL Databases, they are nontraditional databases without tables, rows, and columns. Instead, they store data in various formats, such as documents, key-value pairs, graphs, or columns.

Advantages and disadvantages:

It is clear from their definition that non-relational databases are flexible, as there is no need to define any schema or structure. This means you can store data in any format without worrying about changing application requirements and sources, allowing you to keep unstructured and semi-structured data.

They also allow horizontal scaling. This is by adding more servers to the existing server to allow for more traffic without compromising performance.

The databases are built with performance in mind. Therefore, they use techniques like caching, indexing, sharding, replication, and partitioning to improve speed.

As much as these all sound exciting, non-relational databases have their limitations that you should worry about when picking them as an option.

For starters, we have data inconsistency. This was an advantage a minute ago, but imagine a database where you don't know what to expect. In addition, you would have to write your code to cover a wide range of requirements, which becomes a limitation.

More so, there is a lack of a universal language for all non-SQL databases. Unlike SQL databases where SQL is the general language for everyone, Non-SQL databases have unique query languages and syntaxes, bringing a learning curve when you need to switch from one database to another.

There are also security issues with Non-SQL databases, as unauthorized data modification can become an issue. Although there are measures to prevent this from happening, it is still a common problem.

Popular Options.

Some popular non-SQL databases include MongoDB, Redis, Cassandra, and DynamoDB.

• MongoDB: MongoDB is a document database that stores data as BSON documents (binary JSON). It is one of the world's most widely used non-relational databases, with a rich set of features and tools. For example, MongoDB supports dynamic schemas, full-text search, geospatial queries, an aggregation framework, map-reduce functions, and more.

• Redis: Redis is a key-value database that stores data in memory for fast access. It is often used as a cache or message broker for web applications. Redis supports various data types, such as strings, lists, sets, hashes, bitmaps, hyper logs, and streams. Also supported are transactions, pub/sub mechanisms, Lua scripting, and persistence options.

• Cassandra: Cassandra is a column database that stores data as rows of columns in distributed tables. It is designed for high availability, fault tolerance, and linear scalability.
  Cassandra supports dynamic schemas, secondary indexes, materialized views, user-defined types, and more.

• DynamoDB: DynamoDB is a key-value database that stores data as items in tables on the AWS cloud.

  It is a fully managed service that offers high performance, durability, and scalability.
  DynamoDB supports various features, such as global tables, streams, transactions, backup, and restore.

It is worth noting that all these non-SQL databases have Python drivers that allow ease of use.

Conclusion.

In conclusion, every database is flawed, and as a developer, you must use it on a project-by-project basis. Hence, you get the opportunity to reevaluate your choice.

As a quick reference guide to database decision-making, this article can help you make better database decisions.

That would be all about databases. Good luck with your next project.