Skip to content

A Comprehensive Guide to Relational Database Management Systems

The Evolution of Database Technology

The history of databases is closely tied to the history of computing itself. As computers became more powerful and widespread in the 1960s, the need for efficient, reliable data storage and retrieval grew. Early databases were based on flat files, which are simple, unstructured collections of data. While suitable for small-scale use, flat files quickly become unwieldy as data volumes grow.

The first step towards more structured databases came with the development of hierarchical and network models in the late 1960s. These models organized data into tree-like or graph-like structures, with records connected by links. While an improvement over flat files, these models were still relatively inflexible and required complex programming to navigate.

It was against this backdrop that computer scientist Edgar F. Codd proposed the relational model in 1970. Codd, then working at IBM, outlined a new way of structuring and querying data based on mathematical set theory. His key insights included organizing data into tables, using keys to establish relationships between tables, and manipulating data using a high-level query language.

Codd‘s relational model was a major breakthrough, providing a level of abstraction and flexibility that did not exist in earlier databases. By abstracting data into tables that could be recombined through keys and queried through a structured language, the relational approach enabled more complex modeling of data relationships and more powerful information retrieval.

Codd‘s 12 Rules for Relational Databases

In addition to his initial relational model, Codd went on to define a set of rules that a database must follow to be considered truly relational. Known as Codd‘s 12 rules, they serve as a benchmark for evaluating the "relational completeness" of a database system. Some of the key rules include:

  1. The information rule: All information in a relational database is represented explicitly as values in tables.
  2. The guaranteed access rule: Every value in a relational database is accessible through a combination of table name, primary key, and column name.
  3. Systematic treatment of null values: The DBMS must allow each field to remain null (empty).
  4. Dynamic online catalog based on the relational model: The database description is represented at the logical level in the same way as ordinary data, allowing authorized users to query it using the same query language as they use for regular data.
  5. Comprehensive data sublanguage rule: A relational system may support several languages, but it must support at least one language that is declarative and comprehensive, capable of defining data, defining views, manipulating data, and setting constraints.

While not all RDBMSes fully adhere to all 12 rules, they provide an important theoretical foundation for relational databases.

Relational vs. Non-Relational Models

So how does the relational model compare to earlier hierarchical and network models? Let‘s look at some key differences:

Feature Hierarchical Network Relational
Structure Tree-like Graph-like Tables
Relationships Explicit parent-child links Explicit owner-member links Implicit through keys
Querying Navigational Navigational Declarative (SQL)
Flexibility Low Medium High
Scalability Low Medium High
Complexity High High Low

The relational model‘s use of tables and declarative querying provides a higher level of abstraction and flexibility compared to the more rigid, link-based structures of hierarchical and network models. This makes relational databases easier to understand, maintain, and adapt to changing requirements.

Relational Databases in Action

Relational databases are used across virtually every industry and domain. Some common use cases include:

  1. Banking and financial services: RDBMSes are used to store and manage customer accounts, transactions, loans, and other financial data. The ability to enforce strong data integrity and consistency is crucial in this domain.

  2. E-commerce: Online retailers use RDBMSes to manage product catalogs, customer orders, inventory levels, and shipping information. The ability to handle high transaction volumes and complex queries is essential.

  3. Healthcare: Hospitals and clinics use RDBMSes to store patient records, medical histories, prescription information, and billing data. The need for secure, HIPAA-compliant data storage makes RDBMSes a good fit.

  4. Government: Government agencies use RDBMSes for a wide range of purposes, from tracking tax records and driver‘s licenses to managing social security benefits and census data. The scalability and security features of RDBMSes are important considerations.

  5. Education: Universities and schools use RDBMSes to manage student records, course catalogs, faculty information, and alumni data. The ability to handle complex relationships between data entities is key.

These are just a few examples – the versatility and robustness of relational databases make them suitable for countless other applications.

Challenges and Criticisms

Despite their many strengths, relational databases are not without their challenges and criticisms. Some of the main issues include:

  1. Scalability: While RDBMSes can scale to very large datasets, they can face challenges in extremely high-volume, high-velocity scenarios. This has led to the rise of NoSQL databases for certain use cases.

  2. Complexity: The relational model can be complex to understand and implement properly, especially for highly interconnected datasets. Poorly designed schemas can lead to performance issues.

  3. Rigidity: The structured nature of relational databases can make them less suitable for handling unstructured or semi-structured data, which is becoming more common in the big data era.

  4. Performance overhead: The ACID properties of relational databases provide strong consistency, but can also introduce performance overhead, particularly for write-heavy workloads.

Despite these challenges, relational databases remain the dominant paradigm for structured data storage and management.

The Current RDBMS Landscape

The RDBMS market is well-established and mature, with a mix of commercial and open source offerings. According to Gartner, the top 5 operational database management system vendors by market share in 2020 were:

  1. Oracle (48.8%)
  2. Microsoft (18.7%)
  3. IBM (8.6%)
  4. SAP (5.6%)
  5. Teradata (3.6%)

Other notable players include open source databases like MySQL, PostgreSQL, and MariaDB, as well as cloud-native offerings like Amazon Aurora and Google Cloud Spanner.

One interesting development in recent years has been the rise of "NewSQL" databases, which aim to combine the scalability and flexibility of NoSQL databases with the strong consistency and relational features of traditional RDBMSes. Examples include Google Spanner, CockroachDB, and VoltDB. While still a relatively niche category, NewSQL represents an evolution of the relational model to meet the demands of modern, large-scale applications.

The Future of Relational Databases

Looking ahead, relational databases are likely to remain a core part of the data landscape, even as newer models gain popularity. The well-established nature of the relational model, the robustness of SQL, and the vast ecosystem of tools and expertise around RDBMSes provide a strong foundation that will be hard to fully replace.

That said, relational databases will need to continue evolving to meet the needs of an increasingly data-driven world. Some key trends and developments to watch include:

  1. Cloud-native databases: The shift towards cloud computing is driving demand for databases that are optimized for cloud environments, with features like automatic scaling, multi-region replication, and pay-per-use pricing.

  2. Hybrid transactional/analytical processing (HTAP): There is growing interest in databases that can efficiently handle both transactional and analytical workloads, eliminating the need for separate OLTP and OLAP systems. Many RDBMSes are adding features like columnar storage and in-memory processing to better support real-time analytics.

  3. Machine learning integration: As machine learning becomes more widely adopted, there is a need for closer integration with databases. Some RDBMSes are starting to incorporate machine learning capabilities directly, allowing for model training and inference to be done in-database.

  4. Blockchain and distributed ledger technology: While still an emerging area, there is potential for convergence between relational databases and blockchain technologies. Projects like Oracle Blockchain Tables and Amazon QLDB are exploring how to bring the benefits of immutability and decentralization to relational data.

In the context of big data and analytics, relational databases play a crucial role as a source of structured, consistent data that can feed into data warehouses, data lakes, and machine learning models. While NoSQL databases are well-suited for handling large volumes of unstructured data, relational databases excel at managing the "single source of truth" data that is essential for many analytical use cases.


The relational database has been a cornerstone of computing for over 50 years, and its impact cannot be overstated. From its theoretical foundations laid by E.F. Codd, to its commercialization in the 1980s, to its current status as a mature and widely-used technology, the relational model has demonstrated remarkable staying power.

While the database landscape has become more diverse and the relational model faces new challenges, its fundamental strengths – the simplicity and flexibility of tables and SQL, the robustness of ACID transactions, the ability to model complex relationships – ensure that it will continue to play a vital role in the data infrastructures of the future.

As data becomes an ever-more critical asset for organizations of all types, understanding and leveraging relational databases will be an essential skill for data professionals. Whether working with a traditional commercial RDBMS, an open source alternative, or a cloud-native relational database, the principles and techniques of the relational model will continue to be relevant and valuable for years to come.