Database technology has evolved rapidly over the past decade. In 2024, companies have more options than ever when choosing a database management system (DBMS) to meet their data storage and retrieval needs. Two major categories of databases continue to dominate the landscape relational (SQL) and non-relational (NoSQL). But which one is right for your business?
Understanding the Key Differences
Relational and non-relational databases take fundamentally different approaches for how they store, organize, and access data. Let’s look at some of the key differences:
Data Structure and Flexibility
Relational databases rely on predefined schemas and tabular relations between entities. All data must adhere to a rigid, uniform structure with columns and rows. This makes SQL databases highly structured, inflexible, and difficult to scale dynamically.
Non-relational systems use flexible data models with no strict schema. The schemaless architecture allows developers to store unstructured, polymorphic, and multi-dimensional data with ease. Scaling out is also simpler with NoSQL.
Query Methods
SQL databases support powerful querying through SQL (Structured Query Language). Complex analytic queries can be performed across related tables using joins. However, you must learn SQL to tap the full potential.
NoSQL databases typically support only basic CRUD operations for individual entries. But query capabilities vary greatly across NoSQL offerings like key-value, document, graph databases etc. Most provide proprietary APIs and query languages.
Performance Tradeoffs
Relational databases excel at complex transactions that require cross table consistency and integrity. But joins and ACID compliance introduce overhead that limits scalability and performance with big volumes of reads/writes.
Non-relational systems sacrifice ACID guarantees for faster reads/writes and linear scalability. But most NoSQL stores lack cross record transactions and advanced querying capabilities.
High Availability and Durability
SQL databases now offer high availability via replication, failover clusters, and cloud managed services. Durability is ensured through ACID compliant crash recovery and transaction logs.
NoSQL databases also provide configurable consistency, availability, partition tolerance and durability. High throughput key value caches offer low persistence guarantees. But replicated document and wide column stores are as durable as relational databases.
When Should You Use a Relational vs Non-Relational Database?
Determining which database type best suits your application depends on a variety of factors:
Transactional Workloads and Complex Queries
If your system requires ACID compliant transactions spanning multiple entities and tables, then a relational database is the best fit. The rigid schema and consistency constraints ensure data integrity across complex write operations. Joins and SQL also enable deep multi table reporting and analytics.
e.g. financial applications, ERPs, CRMs.
High Volume Reads and Writes
For applications with simple access patterns that demand scalability, availability and low latency, a non-relational database is preferable. The flexible schemas easily absorb high read/write throughput without complex joins. though analytics capabilities are limited. e.g. IoT systems, gaming platforms, media streaming services.
| Parameter | Relational DB | Non-Relational DB |
|---|---|---|
| Structure | Predefined schema | Flexible, schema-less |
| Query Capabilities | Powerful via SQL | Basic CRUD only |
| Scalability | Vertical, hard to scale | Linear, easy to scale |
| Consistency | Strictly ACID compliant | Eventual or tunable consistency |
Data Variety, Velocity and Volume
If your data is unstructured, temporal, spatially distributed or rapidly exploding in volume, then a NoSQL database removes modeling and storage limitations. JSON documents, columnar and graph models handle variety cleanly and scale smoothly via horizontal partioning. e.g. CMS, blogging platforms, social media, geospatial services.
Whereas relational databases excel at maintaining complex data integrity constraints in modest volumes of well defined records. Joins allow elegant handling of one to many and many-to-many cardinality.
Hybrid Data Requirements
Often transactional data requiring consistency co-exists with performance critical analytics data. In such cases, a hybrid database architecture works best. Critical transactional entities can reside in a relational database while high velocity analytics data streams into a NoSQL store. Application layers mediate between the databases for a unified view. Cloud services like AWS let you run managed RDS and DynamoDB instances side by side.
Development Skill
If your team is well versed in SQL, then applying those skills to operate a proven relational database stack makes sense. Schema and index optimization leverages years of collective wisdom.
If willing to learn new paradigms like denormalization and event sourcing, NoSQL grants flexibility and scalability at lower costs today. Development skills are maturing around popular document and graph database technologies. So weighing factors like queries, transactions, scale needs, data types and skill sets helps determine the ideal database for your application.
The Rise of NewSQL Databases
The battle lines between relational and non-relational databases are blurring in 2024 with the rise of NewSQL a category combining advantages of both models.
NewSQL databases aim to match the performance, scalability and availability of NoSQL systems while retaining the ACID guarantees and SQL query powers of traditional RDBMS. They remove overheads of older SQL technology while avoiding NoSQL limitations around transactions and reporting. Popular NewSQL databases include Google Spanner, YugabyteDB, MemSQL and CockroachDB. These are deployed as distributed SQL databases providing horizontal write scalability with ANSI SQL support. Hence you get the best of both worlds scale and speed of NoSQL with transactional consistency and convenience of full blown SQL.
| Parameter | Relational DB | NewSQL DB | Non-Relational DB |
|---|---|---|---|
| Query Language | SQL | SQL | Proprietary APIs |
| Scalability | Vertical | Horizontal, Linear | Horizontal, Linear |
| Consistency | ACID | ACID | Tunable Consistency |
| Structure | Rigid Schema | Flexible Schema | Schema less |
So if you seek easier scalability than standalone SQL databases without losing transactionality or query complexity NewSQL is the solution.
The Bottom Line
We have come a long way from the days when monolithic relational databases dominated enterprise applications. Today’s data architects enjoy a banquet of specialized database technologies relational, non-relational and NewSQL.
Each category and specific system comes with its own strengths and tradeoffs. Your choice should ultimately hinge on application data access patterns, consistency needs, scale and skill set considerations. In many cases, a hybrid model may be prudent to reap specific benefits.
As we accelerate into an era fueled by AI, IoT, VR and autonomous technologies data will grow more varied, dynamic and voluminous. So our capacity to effectively store, process, analyze and learn from this data will decide future success. Hence the enduring innovation in database technologies that lies ahead is key.
Conclusion
Determining the right database for your application requires understanding key differences between relational and non-relational systems around structure, querying, performance and other aspects. Relational databases excel at complex queries and transactions but are harder to scale and less flexible. Non-relational databases instead offer schemaless flexibility and linear scalability but lack advanced SQL capabilities.
Weigh factors like data variety and volume, transaction complexity, scale needs, consistency requirements and development skills when deciding between relational vs non-relational databases. Applications with hybrid data access patterns can utilize both in a tiered architecture for optimal results. The rise of NewSQL bridges gaps between both models. As data continues to explode and drive innovation, new database technologies will emerge to power the needs of tomorrow.
Frequently Asked Questions
Can you query non-relational databases?
Most non-relational databases provide limited query support beyond basic CRUD operations on individual data entries. Query capabilities vary by NoSQL type key-value stores have none while document databases offer primitive indexing and filtering. Graph databases however allow traversal queries across nodes with properties.
Is MongoDB relational or non-relational?
MongoDB is a popular document oriented non-relational NoSQL database. It stores schema less data as JSON like documents rather than tables with rigid relations. Scaling out is easy via automatic sharding across distributed node clusters.
What database does Facebook use?
Facebook built a customized non-relational data store called TAO to manage hundreds of petabytes of user generated content. TAO architecture spreads profiles across thousands of commodity servers, migrating hot content to faster cache servers. This bespoke NoSQL database powers the world’s largest social network today.
Can multiple users access a database at once?
Yes virtually all modern database systems, whether relational (SQL Server, Oracle) or non-relational (Cassandra, MongoDB), support concurrent multi-user access and concurrency control. Transactions queue up to handle conflicts as users query and manipulate the database simultaneously.
Should I learn SQL or NoSQL first?
We recommend starting with fundamental SQL since relational databases still power most custom enterprise applications today. SQL skills also transfer to NewSQL technologies. NoSQL is simpler to get started with but lacks transferable skills across proprietary query languages and APIs.
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