Choosing The Right Tool: Best Databases For Python

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Python is a high-level but popular programming language used to create web applications. A database plays a significant role in storing and managing information. Python supports numerous databases like SQLite, MySQL, Oracle, PostgreSQL, etc. But which of them are the most effective?

The ten best databases for Python are:

  • PostgreSQL
  • MySQL
  • SQLite
  • MongoDB
  • Cassandra
  • Redis
  • Oracle Database
  • Elasticsearch
  • Hadoop 
  • InfluxDB 

Best Databases for Python

Let’s learn about these databases in detail. And by the end of the article, you might also find your favorite one.

10 Best Databases For Python

Given below is the list of the 10 best databases for Python.


It is a highly scalable database with extensive features like full-text search, spatial data support, and JSON support.

Integrating PostgreSQL with Python is easy with the psycopg2 library, which provides a high-level interface for interacting with PostgreSQL databases.


It is a potent database that handles large measures of data. It is highly scalable, can be customized to meet specific needs with enhanced security, and supports SSL encryption.


MySQL is another open-source RDBMS widely used in web and data-driven applications. Also known for its versatility and is easy to use. Integrating MySQL with Python is easy with the MySQL Connector/Python library, which provides a high-level interface for interacting with MySQL databases.


MySQL is the fastest, which makes it a preferable selection for web applications. It also includes full-text search, spatial data support, and JSON support.


SQLite can easily integrate with Python using the sqlite3 library, which provides a high-level interface for interacting with SQLite databases.


It is lightweight and easy to use, making it a good choice for small and mobile web applications. It has a small memory footprint, can integrate into applications, is very reliable, and provides atomic transactions.


Integrating MongoDB with Python is easy using the PyMongo library, which provides a high-level interface for interacting with MongoDB databases.


It provides a flexible document-based data model. It also offers high performance and supports high availability and automatic sharing.


Cassandra is a popular NoSQL database designed for high availability and scalability. Integrating Cassandra with Python is easy using the DataStax Python driver, which provides a high-level interface for interacting with Cassandra databases.


This database gives a column-based data model optimized for large-scale data storage. It is powerful and supports automatic partitioning and replication. It is easy to use and provides extensive functionality, such as secondary indexes and materialized views.


Redis can process large amounts of data in real-time and supports data structures such as strings, hashes, lists, and sets.


Integrating Redis with Python is easy using the Redis-Py library, which provides a high-level interface for interacting with the Redis database. It offers powerful performance, supports high availability and clustering, and provides extensive features like Pub/Sub messaging and Lua scripting.

Oracle Database

Oracle Database is an RDBMS type that can handle large volumes of structured data and offers extensive features such as advanced analysis and data mining.


It provides advanced functions such as advanced analysis and data mining. It has enhanced security features, supporting encryption and data masking.


Elasticsearch is a search and analytics engine built on the Apache Lucene search engine library.nIt contains a Python package called elasticsearch-py that offers Python developers a simple user interface. It is schema-less, meaning it does not require predefined schemas or data types. This makes it easy to store and search unstructured data.


Elasticsearch provides a powerful search API that allows you to search documents based on their content and other criteria, such as date ranges, geo-coordinates, etc. 


It is designed to handle data too big to fit on a single computer and often finds its use for big data analytics applications.


It has a Python library called Pydoop that provides an easy-to-use interface for Python developers. Hadoop also includes replication and fault-tolerance mechanisms to ensure data is always available even if nodes fail. Hadoop is written in Java but provides APIs that users can access from other programming languages, including Python.


InfluxDB often finds its use for collecting, storing, and analyzing time-series data from sensors and IoT devices that produce time-stamped data. It has a Python library called influxdb-python that provides an easy-to-use interface for Python developers.


It supports data retention policies, which allow you to configure how long it should store data in the database and how it should aggregate over time. Overall, InfluxDB is a powerful tool for working with time-series data. It widely finds its use in applications that require collecting, storing, and analyzing large amounts of time-stamped data.


What are the best databases to use with Python?

Several databases work well with Python, including: - SQLite: A lightweight, serverless, file-based database. -A potent open-source relational database is PostgreSQL. - MySQL: A relational database management system that is open-source. - Document-based NoSQL database called MongoDB. - Redis: A cache-like in-memory data structure store.

What part does SQLAlchemy play in how Python interacts with databases?

SQLAlchemy is an ORM library that abstracts away the differences between various database systems and provides a unified interface to interact with databases in Python. Instead of using straightforward SQL queries, you can use Python classes and objects to interact with databases.

Can I use Excel as a database with Python?

Yes, you can use Python libraries like `pandas` to read and manipulate data from Excel files like a database. However, Excel is not a full-fledged database and may not be suitable for more complex data storage needs.

What about performance considerations when choosing a database for Python?

Performance considerations depend on the scale and nature of your application. Relational databases like PostgreSQL and MySQL are often better suited for complex queries and transactions. In contrast, NoSQL databases like MongoDB excel at handling large volumes of unstructured data with high read/write throughout.

Are there any cloud-based database services for Python applications?

Yes, many cloud providers offer managed database services that you can use with Python applications. For example, Amazon RDS (Relational Database Service) for PostgreSQL, MySQL, or Oracle, and MongoDB Atlas for MongoDB are popular choices.

Is it possible to switch databases without changing Python code?

Using an ORM like SQLAlchemy can make it easier to switch databases because it provides a consistent interface. However, some database-specific SQL queries may need adjustments when switching between different database systems.


Finally, Python offers a variety of options for storing and managing databases. Choosing a suitable database depends on your specific needs and requirements, such as scalability, flexibility, and ease of use.

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