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 which remains a wide standard, today the sheer variety and depth of database systems can be dizzying. What’s more, rising volumes of unstructured data, availability of storage and processing power, and evolving analytic 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 languages, as well as the situations each is best-suited for, and how the landscape is changing.

SQL vs NoSQL: Five Main Differences

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.

There are five practical differences between SQL and NoSQL:

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

1. Language.

SQL has been around for over 40 years, so it is recognizable, 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 schemata 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 standard interface which SQL provides, so more complex queries can be difficult to execute.

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 NoSQL languages, as they concern a diverse set of unrelated technologies. Many NoSQL databases have a unique data manipulation language constrained by particular structures and capabilities.

2. Scalability.

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

  • SQL databases can be scaled horizontally as well, though sharding or partitioning logic is often the user’s onus and not well supported.
  • NoSQL technologies are diverse and while many rely on the master-slave architecture, options for scaling vertically also exist.
  • Savings made using more efficient data structures can overwhelm differences in scalability; most important is to understand the use case and plan accordingly.

3. Structure.

SQL database schemata 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:

  • Column-oriented databases transpose row-oriented RDBMSs, allowing efficient storage of high-dimensional data and individual records with varying attributes.
  • Key-Value stores are dictionaries which access diverse objects with a key unique to each.
  • Document stores hold semi-structured data: objects which contain all of their own relevant information, and which can be completely different from each other.
  • Graph databases add the concept of relationships (direct links between objects) to documents, allowing rapid traversal of greatly connected data sets.

4. Properties.

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

  • Atomicity means all transactions must succeed or fail completely. They 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 roll-back the effects of a successful transaction.

NoSQL technologies adhere to the “CAP” theorem, which says that in any distributed database, only two of the following properties can be guaranteed at once:

  • Consistency: Every request receives the most recent result, or an error. (Note this is different than in ACID)
  • Availability: Every request has a non-error result, regardless of how recent that result is.
  • Partition tolerance: Any delays or losses between nodes will not interrupt the system’s operation.

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 programming relational data.

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

SQL is available to most major platforms, from operating systems to architectures and programming languages. Compatibility varies more widely for NoSQL, and dependencies need to be investigated more carefully.

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

Remember that SQL dialects share many properties though they interface with distinct databases. 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 recognizable SQL dialect is MySQL, an open source and free RDBMS (though available through proprietary licenses as well). Its use is widespread 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 like 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.

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Other SQL databases.

  • MS-SQL 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 MS-SQL databases in the cloud.
  • Oracle Database is among the oldest and most-established RDBMSs. Its relational store is interfaced by PL/SQL, though it is adapting into a multi-model system.
  • Other major RDBMSs include Access, Ingres, PostgreSQL, Sybase, and SQLite.

Other NoSQL databases.

  • Apache CouchDB, like MongoDB, is a document-oriented database with JSON schemata 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 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 programming 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 equally valid in the modern transition to the cloud.

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When to use SQL vs NoSQL for your business.

Generally, NoSQL is preferred for:

  • Graph or hierarchical data
  • Data sets which are both large and mutate significantly,
  • Businesses growing extremely fast but lacking data schemata.

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:

  • Small
  • Conceptually modeled as tabular
  • In 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 also more appropriate, as RDBMSs are better supported and fault-tolerant.

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SQL vs NoSQL and your business ROI.

SQL is old and sometimes constraining, but also time-tested and increasingly considered a universal interface for data analysis. NoSQL databases are new and 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, usually with increased ROI. Even companies like Google, with resources to innovate ad-hoc NoSQL systems from scratch (and foundational ones at that, see 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 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.

| Last Updated: August 7th, 2019