SQL Databases Overview

SQL databases are great for organizing, storing, and retrieving structured data essential to modern business operations. These databases use Structured Query Language (SQL), a gold standard tool for managing and manipulating data, which is universally recognized for its reliability and robustness in handling complex queries and vast datasets.

SQL is so instrumental to database management that databases are often categorized based on their use of SQL, leading to the distinction between SQL and NoSQL databases.

What is an Open Source Database?

Open source databases are software systems whose source code is publicly available for anyone to view, modify, and enhance. This article covers strictly open source SQL databases. Open source databases are developed through collaboration and innovation, eliminating licensing fees and creating a rich environment of community-driven enhancements.

Why Use Open Source Databases?

  • Cost-effectiveness: Most open source databases are free to use, significantly reducing total cost of ownership.
  • Flexibility and Customization: Users can modify the database software to meet their specific needs.
  • Community Support: Robust communities contribute to the development and security of these databases.

OLTP vs OLAP

When selecting a database, it is important to determine your primary use case—whether you frequently create, update, or delete data (OLTP) or need to analyze large volumes of archived data (OLAP).

What is OLTP?

OLTP or Online Transaction Processing databases manage and handle high volumes of small transactions.

Characteristics of OLTP:

  • Handles large numbers of transactions by many users.
  • Operations are typically simple (e.g., updating a record).
  • Focus on quick query processing and maintaining data integrity in multi-access environments.
  • Data is highly normalized.

When to use OLTP?

Common use cases include:

  • E-commerce: Order placement and payment processing.
  • Banking: Account transactions and loan processing.

What is OLAP?

OLAP or Online Analytical Processing databases are designed for complex analyses and queries on large volumes of data.

Characteristics of OLAP:

  • Designed for analysis and reporting functions.
  • Queries are complex and involve large volumes of data.
  • Focus on maximizing query speed.
  • Data may be denormalized.

When to use OLAP?

Common use cases include:

  • Retail Sales Data Analysis: Analyzing trends and product performance.
  • Corporate Performance Monitoring: Tracking financial and operational metrics.

Open Source OLTP Databases

PostgreSQL

A row-oriented database considered the world’s most advanced open source database.

Best Uses: Enterprise applications and handling large volumes of data.

SQLite

A self-contained SQL database engine often used for embedded applications.

Best Uses: Mobile applications and small websites.

MariaDB

A columnar database offshoot of MySQL, widely respected for performance.

Best Uses: Web-based applications and cloud environments.

Firebird

A flexible relational database that supports both OLTP and OLAP.

Best Uses: Small to medium enterprise applications.

Open Source OLAP Databases

ClickHouse

Known for its speed, suitable for real-time query processing.

Best Uses: Real-time analytics and managing large volumes of data.

DuckDB

An embedded file-based database designed for fast analytical SQL queries.

Best Uses: Analytical applications requiring fast querying capabilities.

StarRocks

A performance-oriented, columnar distributed data warehouse.

Best Uses: Real-time analytical processing on large-scale datasets.

Doris

A column-oriented data warehouse providing high performance and real-time processing.

Best Uses: Real-time OLAP applications.

Trino

A query engine that queries multiple databases and data sources rapidly.

Best Uses: Distributed SQL querying for big data solutions.

Citus

An extension to PostgreSQL that transforms it into a distributed database.

Best Uses: Scalable PostgreSQL applications.

Conclusion

Open source SQL databases offer various flexible, cost-effective solutions for data management suitable for handling large datasets, real-time analytics, or robust enterprise applications.