Relational vs. Document Database: Key Differences & Challenges!
Share this article
Relational vs document databases offer solutions to the painpoint of data modeling flexibility. In traditional relational databases, data is organized into structured tables with predefined schemas, which can be rigid and challenging to modify.
Document databases, on the other hand, offer a more flexible approach. They allow developers to store data in a document format, typically using formats like JSON or BSON.
Modern data problems require modern solutions - Try Atlan, the data catalog of choice for forward-looking data teams! 👉 Book your demo today
This blog delves deep into understanding both paradigms, highlighting their differences, advantages, and the popular tools that power them.
Whether you’re an experienced developer or just a curious tech enthusiast, this article will provide you with the insights you need to make an informed choice.
Ready? Let’s dive in!
Table of contents
- What is a relational database?
- What is a document database?
- Key differences
- Key advantages
- Challenges to consider
- Popular tools and technologies
- Summarizing it all together
- Related reads
What is a relational database?
A relational database is a collection of data items organized as a set of formally described tables. Stemming from the relational model proposed by E.F. Codd in 1970, this type of database allows for data to be structured in rows and columns.
Each row represents a unique record or entity, while columns denote the attributes of these records.
4 Key characteristics define a relational database
- Tables: The primary unit of data storage. Each table contains rows (records) with related data. For example, you might have a table named “Customers” that stores information like customer ID, name, and contact details.
- Attributes: Every table column signifies an attribute of the data, acting as a descriptor. In the “Customers” table, attributes might include “First name”, “Last name”, “Email”, and so on.
- Primary key: Every relational table has a unique identifier known as the primary key. This ensures each record within the table can be distinctly identified. In our “Customers” example, “customer id” could be the primary key.
- Relationships: One of the most distinctive features of a relational database is its ability to maintain relationships between tables. Through primary and foreign keys, one can associate records across multiple tables, allowing for complex queries and data retrieval operations.
SQL is the standard language for managing and querying data in a relational database. Its ubiquity and powerful command set have made it an industry staple for decades. Relational databases offer a high degree of flexibility and are commonly used across industries for applications ranging from simple contact management systems to complex ERP and financial solutions.
What is a document database?
A document database is often referred to as a NoSQL database, is a type of database designed to store, retrieve, and manage document-oriented or semi-structured data.
Instead of using tables with rows and columns as in traditional relational databases, a document database represents data as documents, typically using formats like JSON (Javascript object notation) or BSON (Binary JSON).
5 Key features of a document database include
- Documents: At the heart of this type of database lies the document – a self-contained unit that stores data in an encoded form, usually as JSON. Each document can have a unique structure with varying fields and can encapsulate all information related to a specific item, making them inherently flexible.
- Collections: Documents are grouped into collections, which can be equated to tables in relational databases. However, unlike tables, collections do not require a fixed schema, allowing documents within the same collection to have different structures.
- Flexibility: One of the primary advantages of document databases is schema flexibility. As businesses evolve and requirements change, data models in a document database can be easily adapted without major disruptions.
- Scalability: Most document databases are built to scale out by distributing the data across multiple nodes or clusters. This makes them particularly suited for large-scale applications and big-data scenarios.
- Integrated data retrieval: Document databases often provide querying capabilities using the same language as the stored documents. For instance, a JSON-stored data might be queried using a JSON-based query language.
Document databases are especially favored in scenarios where the data model is fluid, where scalability is a primary concern, or where rapid development cycles require frequent changes to the data model. They’re commonly used for content management systems, e-commerce platforms, mobile applications, and more.
In the realm of digital innovation, document databases address the challenges presented by the diverse, high-volume, and dynamic nature of modern data, offering a more agile approach to data storage and management.
Relational database vs document database: 9 Key differences
Here’s a tabular presentation of the key differences between relational and document databases:
| Aspect | Relational database | Document database |
|---|---|---|
| Data structure | Tables with rows and columns. | JSON, BSON, or XML-like documents. |
| Schema | Fixed schema. Requires predefined schema and changes can be complex. | Dynamic schema. Schema can be altered on the fly. |
| Query language | SQL (Structured query language). | Varies; often use a JSON-style query language. |
| Scalability | Vertical scalability. | Horizontal scalability. |
| Data integrity | High. Enforces ACID properties. | Varies. May prioritize availability over immediate consistency (eventual consistency). |
| Relationships | Strong relationships using foreign keys. | Embedded documents and linking but generally less emphasis on relationships. |
| Complexity | Higher complexity due to relationships and schema changes. | Lower complexity, more flexible to changes. |
| Best use cases | Situations requiring complex transactions and data accuracy. E.g., Banking systems. | Situations with rapidly evolving data models or large amounts of unstructured data. E.g., Content management systems. |
| Example | MySQL, PostgreSQL, Oracle, SQL server. | MongoDB, CouchDB, RavenDB, Couchbase. |
This table summarizes the primary distinctions between relational and document databases. Each type has its strengths and is well-suited for different scenarios.
Relational vs document database: 7 Key advantages
Let’s dive deeper into the advantages of both relational and document databases, offering an expansive understanding of their strengths.
Advantages of relational database
Relational databases, with their legacy dating back several decades, have established themselves as the cornerstone of many enterprise applications. Their systematic, structured approach to data management offers numerous benefits:
- Structured data model
- Data integrity and relationships
- Robust querying with SQL
- ACID compliance
- Advanced security features
- Maturity
- Elimination of data redundancy
Let us understand the advantages of relational databases in detail.
1. Structured data model
The very foundation of a relational database lies in its organized tabular format. Data is systematically stored in tables made up of rows and columns, which ensures data consistency and ease of querying.
This structured approach simplifies the establishment of relations between different data entities, promoting a coherent and integrated data environment. Furthermore, the tabular nature aids in creating a visual representation of data, facilitating analytics and reporting.
2. Data integrity and relationships
By utilizing primary and foreign keys, relational databases can create complex relationships between tables. This not only prevents data anomalies but also ensures the relational integrity of the database.
Furthermore, these relationships make data retrieval more efficient, allowing for comprehensive views and joins that reflect real-world scenarios. The structured approach of defining relationships also aids in maintaining data consistency across the entire database system.
3. Robust querying with SQL
SQL (Structured query language) provides a comprehensive language for querying, aggregating, and manipulating data. The depth and breadth of SQL commands allow for intricate data operations, setting relational databases apart.
Its universal acceptance ensures compatibility and interoperability across myriad systems and applications. Furthermore, the mature ecosystem around SQL includes a wealth of tools, tutorials, and community support, empowering both novices and experts to derive maximum utility.
4. ACID compliance
The commitment to ACID (Atomicity, Consistency, Isolation, Durability) properties guarantees that all transactions are processed reliably and predictably. This makes relational databases ideal for applications where data reliability is non-negotiable.
As a cornerstone of the relational model, ACID compliance underscores the database’s resilience, making it a trusted choice for mission-critical applications where data reliability and consistency are paramount.
5. Advanced security features
With features like user roles, access controls, and encryption, relational databases prioritize data security, ensuring that sensitive information remains inaccessible to unauthorized entities.
Moreover, routine audit capabilities and fine-grained permissions further bolster the integrity and confidentiality of data, making relational databases a trusted choice for organizations that prioritize data protection.
6. Maturity
Having been around for a long time, relational databases have been tried and tested across various scenarios. This maturity translates to a stable ecosystem, a wide array of tools, a rich knowledge base, and an expansive community of experts.
Their long-standing presence means that many common challenges and pitfalls have already been addressed, leading to refined solutions and best practices.
Furthermore, the extensive history has cultivated a vast network of educators, forums, and resources, ensuring that support is readily available for both new and seasoned developers.
7. Elimination of data redundancy
Through processes like normalization, relational databases ensure data is stored without redundancy, optimizing storage space and maintaining data consistency.
This meticulous organization not only streamlines data retrieval but also reduces potential discrepancies in the system.
By preventing multiple copies of the same data, relational databases ensure that updates or deletions are straightforward, maintaining a single source of truth.
Advantages of document databases
Document databases, a subset of the NoSQL movement, have gained traction in the modern era of application development, catering to evolving requirements and dynamic data needs. Their advantages include:
- Schema agility
- Horizontal scalability
- Unified data model
- Rapid development cycle
- Native support for modern data formats
- Geographically distributed architectures
- Cost efficiency at scale
Let’s delve deep into the advantages of a document database.
1. Schema agility
The absence of a fixed schema is one of the defining features of document databases. This flexibility allows developers to evolve the data model seamlessly as application requirements change, without the constraints of rigid schemas.
Such agility accelerates the development process, enabling quicker iterations and adaptability. Additionally, it supports varied data structures, catering to diverse data needs without necessitating major database redesigns.
2. Horizontal scalability
Built for the modern web age, document databases excel at scaling out. They can distribute data effortlessly across servers or clusters, addressing the needs of massive data volumes or high user loads.
This distribution ensures that applications remain performant and responsive even as they grow, eliminating potential bottlenecks.
Additionally, this scalable design can lead to cost savings, as organizations can expand their infrastructure incrementally based on actual demand, rather than overinvesting upfront.
3. Unified data model
Storing related data within a single document simplifies data retrieval. This embedded structure can mirror object-oriented or functional programming models, promoting a more intuitive data representation.
Additionally, this design reduces the need for complex joins, streamlining query processes. As a result, developers can often find it more natural to work with, especially when constructing applications that closely align with the stored document structures.
4. Rapid development cycle
The dynamic nature of document databases facilitates quicker iterations in application development. Changes to the data model don’t necessitate comprehensive database redesigns, which can significantly speed up development timelines.
Moreover, this agility empowers developers to adapt to changing user requirements or market trends without heavy overhead. Coupled with native support for formats like JSON, it streamlines both backend and frontend development, fostering a cohesive and responsive application environment.
5. Native support for modern data formats
Document databases often natively support formats like JSON or BSON. These formats are integral to modern web and mobile applications, enabling straightforward data interchange.
Additionally, their compatibility with contemporary development frameworks makes them highly favored among developers.
This native support simplifies the process of data serialization and deserialization, streamlining application development and reducing backend complexities.
6. Geographically distributed architectures
Several document databases are designed with global distributions in mind, ensuring data is available with minimal latency, irrespective of where users are located.
This distributed approach not only bolsters data availability but also offers resilience against regional outages, ensuring uninterrupted access.
Furthermore, geographically-aware distributions can optimize data storage based on user concentrations, streamlining operations and reducing costs.
7. Cost efficiency at scale
The ability of document databases to operate on distributed, often commodity hardware can translate to cost savings, especially when scaling to accommodate vast amounts of data or traffic.
Furthermore, their inherent design supports dynamic cloud environments, allowing organizations to scale up or down based on demand, optimizing operational costs.
By leveraging distributed architectures, businesses can achieve not only enhanced performance but also a more economical use of resources, especially in fluctuating or unpredictable workloads.
Both relational and document databases cater to different scenarios and challenges. Their individual strengths underscore the need to select a database type based on the specific requirements of a project, rather than general trends or preferences.
Relational vs document database: 5 Challenges to consider
While both relational and document databases bring distinct advantages to the table, they also come with their own set of challenges. Here’s a breakdown of some key considerations for each:
Relational databases
- Scalability concerns
- Rigidity of schema
- Complexity with high-volume, varied data
- Performance impact with joins
- Learning curve
Here are the challenges to consider in relational databases in detail.
1. Scalability concerns
Traditional relational databases are often vertically scaled, which means boosting performance could require a significant investment in more powerful hardware.
Conversely, horizontal scaling, prevalent in many modern databases, involves adding more servers to the system, distributing the load, and potentially offering a more cost-effective scalability solution.
The vertical nature of relational databases can sometimes limit their ability to meet the demands of exponentially growing data or high user traffic without comprehensive architectural changes.
2. Rigidity of schema
Any alteration to the database schema can be complex and time-consuming, especially in large, established systems.
This rigidity can sometimes hinder rapid development and adaptation to changing business needs.
Moreover, migrating to a new schema might require substantial planning and resources, potentially impacting system availability and performance during the transition.
3. Complexity with high-volume, varied data
Handling vast amounts of unstructured or semi-structured data isn’t the forte of relational databases. They can become cumbersome when dealing with non-tabular data formats.
As applications increasingly integrate diverse data sources, from social media feeds to IoT sensors, relational databases might struggle to maintain efficiency and performance.
Furthermore, the rigid schema structure may require frequent alterations, adding to the overhead and potentially slowing down development cycles.
4. Performance impact with joins
As datasets grow, SQL queries with multiple joins can become performance bottlenecks.
Joins require the database system to combine rows from different tables, which, when dealing with vast volumes of data, can result in longer query execution times.
Efficient indexing and query optimization become critical to ensure that performance doesn’t suffer as the complexity of operations increases.
5. Learning curve
For beginners, mastering SQL and understanding the intricacies of relational database design can be challenging.
The strict schema requirements and nuances of normalizing data can be initially overwhelming.
However, with a plethora of tutorials, courses, and community forums available, dedicated learners can navigate these complexities and harness the full power of relational databases.
Document databases
- Consistency vs. availability
- Data redundancy
- Limited query capabilities
- Maturity and tooling
- Schema evolution
Let us explore the challenges to consider in the document database in detail.
1. Consistency vs. availability
While they excel in scalability and fault tolerance, some document databases may not guarantee immediate consistency across distributed systems (often described as eventual consistency).
This trade-off is a result of the CAP theorem, which dictates that, in the presence of network partition, a system can either prioritize consistency or availability but not both simultaneously.
Hence, in scenarios where immediate data consistency is paramount, careful consideration and configuration of the database settings are essential to strike the right balance.
2. Data redundancy
The flexibility of schema-less design can lead to data redundancy, as related data might be replicated across multiple documents.
While this can optimize read operations by reducing the need for joins or complex queries, it can also increase storage requirements and complicate update operations.
Ensuring data consistency becomes paramount, as changes to one document might necessitate updates in other replicated instances, thus demanding careful database design and management.
3. Limited query capabilities
Not all document databases offer the depth of querying capabilities found in SQL, which can be limiting for certain complex operations.
While many have developed their own robust querying languages, they may still require a steeper learning curve or not fully match SQL’s versatility.
Furthermore, developers accustomed to SQL might find it challenging to translate their knowledge directly, potentially slowing down development processes.
4. Maturity and tooling
Being relatively newer, some document databases might not have the extensive tooling, integrations, or community support seen with long-standing relational databases.
However, the rapid adoption and growth of these databases have led to a surge in development tools and third-party integrations.
As the ecosystem matures, the gap in tooling and community-driven resources is quickly narrowing, offering developers a more comprehensive and supportive environment.
5. Schema evolution
While they allow flexible schemas, managing how data structures evolve over time can be a challenge, especially when old and new document structures coexist.
This evolution can sometimes lead to inconsistencies or require migration strategies, adding a layer of complexity.
Ensuring that applications can seamlessly handle various document versions becomes crucial to maintaining performance and data integrity.
When evaluating which database type to choose, it’s essential to consider the specific application’s requirements, the nature of the data, and the long-term goals of the project. Both relational and document databases offer unique strengths, but understanding their limitations is crucial to making an informed decision.
Relational vs document database: 6 Popular tools and technologies
Popular tools and technologies of relational databases
Relational database management systems have been around for decades. Over the years, several tools and technologies have emerged as favorites in the industry:
- MySQL
- PostgreSQL
- Oracle database
- Microsoft SQL Server
- DB2
- Tools
Let us see the popular tools and technologies of relational databases in detail.
1. MySQL
An open-source relational database management system, MySQL is widely used for web applications and is an integral part of the LAMP stack (Linux, Apache, MySQL, PHP/Python/Perl).
Trusted by both startups and established enterprises, MySQL is revered for its reliability and fast performance. Its robust ecosystem, combined with an extensive online community, provides invaluable support, making it a preferred choice for a diverse range of applications.
2. PostgreSQL
A powerful, open-source relational database management system, PostgreSQL is known for its extensibility and standards compliance. It supports both structured query language and procedural languages.
Over the years, PostgreSQL has garnered a strong community following, thanks to its robust set of features and adaptability to diverse application needs.
Its ability to handle large datasets and work seamlessly with various data types makes it a preferred choice for many developers worldwide.
3. Oracle database
A commercial relational database management system, Oracle Database is renowned for its feature-rich environment, scalability, and reliability, making it a choice for many large enterprises. Its advanced capabilities, such as real application clustering, automated data warehousing, and machine learning integration, make it a powerhouse in the world of data management.
Furthermore, oracle’s commitment to security with features like advanced encryption and data masking ensures that data remains protected and compliant with industry regulations.
4. Microsoft SQL server
A product of Microsoft, this relational database management system is used across various industries and is known for its performance, security features, and integration with other Microsoft products.
Its rich set of tools, including SQL server management studio, simplifies database management and development.
Furthermore, with continuous updates and cloud integrations, SQL server remains a competitive choice for businesses seeking scalable and reliable database solutions.
5. DB2
Developed by IBM, DB2 is an enterprise-grade relational database management system known for its performance, security, and ability to handle massive amounts of data.
It’s highly adaptable, supporting both cloud-based and on-premises architectures, making it versatile for businesses of all sizes.
Additionally, with its deep integration capabilities, DB2 works seamlessly with advanced analytics and AI tools, positioning it at the forefront of data-driven decision-making processes.
6. Tools
Tools like phpMyAdmin, SQL developer, and SQL server management studio assist developers and database administrators in managing and querying relational databases.
These utilities streamline the intricacies of database operations, ensuring data manipulation becomes intuitive and efficient.
Additionally, they often come with visual interfaces, simplifying tasks like schema design, performance monitoring, and backup management.
Popular tools and technologies of document databases
The rise of NoSQL databases has brought forth various document database solutions tailored for modern application needs:
1. MongoDB
One of the most popular document databases, MongoDB stores data in a flexible JSON-like format. It’s known for its scalability and dynamic schema. The database’s distributed architecture ensures resilience and availability, making it a top choice for applications demanding high uptime.
Furthermore, with native sharding and real-time aggregation capabilities, MongoDB caters to both large-scale and evolving data needs.
2. CouchDB
An open-source database, CouchDB uses a schema-free JSON document format allowing for great flexibility in data storage.
It’s designed with a unique multi-version concurrency control system, which means users can access the database without locks, facilitating seamless and simultaneous reads and writes.
3. RavenDB
A NoSQL document database for .NET, RavenDB emphasizes ease of use and performance, with built-in features like full-text search and distributed data clusters ensures scalability and resilience.
Moreover, its intuitive interface and robust tooling simplify database management, making it a top choice for developers seeking a seamless integration within the .NET ecosystem.
4. Couchbase
Originally derived from CouchDB, couchbase provides a distributed architecture with a performance-oriented design and is often used in big data applications.
Its capabilities extend beyond mere document storage, encompassing features like full-text search, real-time analytics, and mobile data synchronization.
Couchbase’s emphasis on high availability and scalability positions it as a go-to solution for enterprises seeking responsive, resilient, and adaptable database systems.
5. DocumentDB
Provided by Microsoft azure, documentDB offers a globally distributed, multi-model database service built for the cloud. Its seamless integration with other Azure services ensures a cohesive cloud-based environment for developers.
Additionally, its automatic and instant scalability caters to dynamic workloads, making it a go-to choice for applications with varying traffic patterns and data volumes.
6. Tools
Tools like MongoDB Atlas, robo 3T, and Couchbase console aid developers in establishing, administering, and querying document databases. Furthermore, these tools streamline the database management process, offering intuitive interfaces and rich functionalities.
By leveraging these utilities, developers can enhance their efficiency and ensure that database operations are executed seamlessly and effectively.
Both relational and document databases have evolved with their respective ecosystems, resulting in a plethora of tools and technologies tailored to their strengths. As always, the best choice depends on the project’s specific needs, scalability requirements, and the development environment.
Summarizing it all together
In the vast realm of databases, both relational and document databases hold significant roles. Relational databases, structured around tables, rows, and columns, offer robust data integrity and a powerful querying language in SQL.
Conversely, document databases, part of the NoSQL family, shine with their schema flexibility, adaptability, and horizontal scalability.
While relational systems like MySQL and Oracle have dominated for decades, document-oriented solutions like mongoDB are tailoring themselves to modern, dynamic application needs.
However, each comes with its challenges, from scalability concerns in relational systems to consistency debates in document databases. The choice between them hinges on specific project needs, ensuring data is not only stored but also retrieved and managed efficiently.
Relational vs document database: Related reads
- Data Warehouse vs Database: Differences, Features, and Use Cases
- 7 Data Integrity Best Practices You Need to Know
- Achieving High Data Quality During Cloud Data Migrations: A 2023 guide!
- What is Data Modeling? 5 Steps to Build a Robust Model in 2023
- Data Catalog vs. Data Warehouse: Differences, and How They Work Together?
Share this article