SQL vs NoSQL: Differences, Databases, and Decisions

From analysts and engineers to IT decision-makers, many are familiar with Relational Database Management Systems (RDBMS) and the Structured Query Language (SQL) used to interact with them. While these terms refer to a decades-old paradigm that remains a widely used standard, today the sheer variety and depth of database systems can be dizzying. What’s more, rising volumes of all kinds of data (especially unstructured data), availability of storage and processing power, and evolving analytics requirements have generated interest in fundamentally different technologies.

Collectively known as NoSQL, these popular alternatives to traditional RDBMSs show promise for a variety of modern use cases.

To make informed decisions about which to use, practitioners should be aware of the differences between SQL, NoSQL, individual Database Management Systems (DBMS) and query languages, as well as the situations each is best suited for, and how the landscape is changing.

Differences Between SQL and NoSQL

SQL is the programming language used to interface with relational databases. (Relational databases model data as records in rows and tables, with logical links between them). NoSQL is a class of DBMs that are non-relational and generally do not use SQL.

What is the difference between SQL and NoSQL? There are five practical differences between SQL and NoSQL:

  1. Structure
  2. Properties
  3. Query Language
  4. Scalability
  5. Support and communities

1. Structure

SQL database schema always represent relational, tabular data, with rules about consistency and integrity. They contain tables with columns (attributes) and rows (records), and keys have constrained logical relationships.

NoSQL databases need not stick to this format, but generally fit into one of four broad categories:

  • Key-Value stores function as dictionaries which access diverse objects with a key unique to each.
  • Document stores hold semi-structured data: objects that each contain all of their own relevant information, and can be completely different from each other.
  • Graph databases add the concept of relationships (direct links between objects) to documents, allowing rapid traversal of connected datasets.

2. Properties

At a high level, relational databases and NoSQL databases comply with separate rules for resolving transactions. RDBMSs must exhibit four “ACID” properties:

  • Atomicity means all transactions must succeed or fail completely as a unit. A transaction cannot be partially complete, even in the case of system failure.
  • Consistency means that at each step the database follows invariants: rules which validate and prevent corruption.
  • Isolation prevents concurrent transactions from affecting each other. Transactions must result in the same final state as if they were run sequentially, even if they were run in parallel.
  • Durability makes transactions final. Even system failure cannot undo the effects of a successful transaction.

3. Language

SQL has been around for over 40 years, so it is widely recognized, well documented, and widely used. Safe and versatile, it’s particularly well suited for complex queries. However, SQL restricts the user to working within a predefined tabular schema, and more care must be taken to organize and understand the data before it is used.

The dynamic schema of NoSQL databases allow representation of alternative structures, often alongside each other, encouraging greater flexibility. There is less emphasis on planning, greater freedom when adding new attributes or fields, and the possibility of varied syntax across databases. As a group, however, NoSQL languages lack the structure needed to support the SQL standard. As a result, other query languages specific to each NoSQL database type must be used. These one-off languages are usually less efficient and struggle with complex queries. 

Though there are many dialects of SQL, all share a common syntax and almost-identical grammar. When querying relational databases, fluency in one language translates to proficiency in most others. On the other hand, there is very little consistency between the query languages used for NoSQL databases, as they concern a diverse set of unrelated technologies. Many NoSQL databases have a unique data query language constrained by particular structures and capabilities.

4. Scalability

Most SQL databases can be scaled vertically by increasing the processing power of existing hardware. NoSQL databases use a master-replica architecture which scales better horizontally, with additional servers or nodes, but also have the ability to be scaled vertically. These are useful generalizations, but it’s important to note:

  • SQL databases can be scaled horizontally as well, although the onus of creating and maintaining sharding or partitioning logic often falls on the user.
  • Column-oriented databases transpose row-oriented RDBMSs, which allow efficient storage of high-dimensional data and individual records with varying attributes, and are an alternative format for SQL databases.
  • Savings made using more efficient data structures can overwhelm differences in scalability; most important is to understand the use case and plan accordingly.

5. Support and communities

SQL databases represent massive communities, stable codebases, and proven standards. Multitudes of examples are posted online and experts are available to support those new to working with relational data.

NoSQL technologies are being adopted quickly, but communities remain smaller and more fractured. However, many relational databases are proprietary or associated with large single-vendors, while NoSQL communities benefit from open systems and concerted commitment to onboarding users.

SQL is available for all relational databases of note and can be used directly and through a wide range of computer programming languages. Compatibility varies much more widely for NoSQL and dependencies need to be investigated more carefully.

SQL vs NoSQL databases: MySQL, MongoDB, and more.

SQL has minor variations across different relational databases, but maintains a high degree of consistency across all of them. Flavors of NoSQL vary far more across their attendant systems, so comparison can be more useful between multiple non-relational technologies vs. SQL generally.

Perhaps the most widely used relational database dialect is MySQL, an open-source, free RDBMS (though available through proprietary licenses as well). Its widely used in web applications, and it is known for compatibility, support, and good performance in the average case. MySQL has also made concessions to NoSQL practitioners with features such as a JSON data type, the “Document Store,” and support for sharding (horizontal scaling).

On the non-relational side, MongoDB is primarily a document store containing JSON-like structures and a JavaScript interface. It’s known for being user-friendly (less administration overhead), performant for simple queries, and flexible thanks to its NoSQL underpinnings. Great for hierarchical data storage, it also supports familiar relational concepts from indexing to aggregation to some measure of ACID compliance. Like MySQL, it is compatible with many platforms and programming environments, despite relative recency.

Other SQL databases

  • Microsoft SQL Server is Microsoft’s relational database product, accessed with the proprietary Transact-SQL (T-SQL), and offered in a dozen editions targeted to different end users. Microsoft Azure includes a dedicated component for scaling Microsoft SQL Server databases in the cloud.
  • Oracle Database is among the oldest and most-established RDBMSs, though it is adapting into a multi-model system. Oracle’s SQL dialect, PL/SQL, has more special features and variations from the norm than most dialects of SQL.
  • Other major RDBMSs include Access, Ingres, PostgreSQL, IBM DB2, Sybase, and SQLite.

NoSQL databases

  • Apache CouchDB, like MongoDB, is a document-oriented database with JSON schema and querying over JavaScript. CouchDB’s scaling capabilities stand out, employing a multi-master architecture over the typical single-master distributed design.
  • Redis (Remote Dictionary Server), is the most popular key-value database. It is open source, with a fast and distributed in-memory implementation, and supports many abstract data structures (some rarely found in other NoSQL).
  • InfinityDB and Amazon’s DynamoDB implement two other key-value stores. Columnar stores like Cassandra, MariaDB, and Scylla scale well horizontally, and popular graph databases include ArangoDB, InfiniteGraph, and Neo4j.

The cloud and the future of SQL/NoSQL

Modern brands emphasize interactivity between end users, justifying decentralized, cloud-based architectures, and exposing diverse, new data needing representation: Enter NoSQL, champion of massive, distributed, and morphing data.

But if this non-relational interest had caused traditional RDBMSs to flag at all, they’re now resurging. SQL remains more accessible, understandable, and most importantly, a lingua franca for data.

Often referred to as “Not only SQL” due to support of SQL-like query languages and coexistence with RDBMSs, NoSQL increasingly represents a set of technologies with generalist applicability and inclusiveness. As delineated in many examples above, traditional RDBMSs are also rebranding as generalized databases and connecting with NoSQL. Clearly both paradigms remain valid in the modern transition to the cloud.

When to use SQL vs NoSQL for your business

Generally, NoSQL is preferred for:

  • Graph or hierarchical data
  • Datasets which are both large and mutate significantly
  • Use cases which are growing extremely fast but lacking data schema

In terms of use cases, this might translate to social networks, online content management, streaming analytics, or mobile applications.

SQL is more appropriate when the data is:

  • Smaller
  • Easily modeled as tabular
  • Part of systems where consistency is critical

Think small business’ accounting systems, sales databases, or transactional systems like payment processing in e-commerce. When in doubt, SQL is usually more appropriate, as RDBMSs are better supported and fault-tolerant.

SQL vs NoSQL and your business ROI

SQL is older and sometimes constraining, but also time-tested and increasingly considered a universal interface for data analysis. NoSQL databases are newer and more flexible, but lack maturity and require user specialization. Pragmatically, both models are useful and even growing together.

Ultimately, a technology is only valuable when it serves your business — especially with increased ROI. Even companies like Google, with resources to innovate ad-hoc NoSQL systems from scratch (and foundational ones at that, such as MapReduce and BigTable), have found that SQL provided additional value and restored it within critical systems.

From migrating hand-coded SQL into compliant and governable ETL tools to managing difficult unstructured data to integrating relational and non-relational database systems under one convenient umbrella, Talend provides solutions across data storage paradigms.

Centralized and automated data integration software makes source systems, whether relational or otherwise, easier to manage. Talend products include tools that even users with little ETL experience can use to optimize processes. Connectors are available for all major RDBMSs as well as leading NoSQL databases.

When you’re ready to get started, try Talend Data Fabric and start connecting and accelerating your data and data processes.

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