SQL vs NoSQL: differences, databases and decisions (2023)

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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 is still a broad standard, the sheer variety and depth of database systems today can be staggering. In addition, the growing volumes of unstructured data, the availability of processing and storage capacity, and evolving analytics requirements have generated interest in fundamentally different technologies.

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

To make informed decisions about which one to use, professionals need to be aware of the differences between SQL, NoSQL, database management systems (DBMSs), and individual languages, as well as the situations for which each is best suited and how it is changing the scenery.

SQL vs NoSQL: Five Key Differences

SQL is the programming language used to interact 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 not 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, making it recognizable, documented, and widely used. Safe and versatile, it is especially indicated 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 using it.

The dynamic schemas of NoSQL databases allow the representation of alternative structures, often side by side, encouraging greater flexibility. There is less emphasis on planning, more freedom when adding new attributes or fields, and the ability to vary syntax between databases. However, as a group, the NoSQL languages ​​lack the standard interface provided by SQL, so more complex queries can be difficult to execute.

Although there are many dialects of SQL, they all share a common syntax and nearly 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 belong to a diverse set of unrelated technologies. Many NoSQL databases have a single data manipulation language limited by specific frameworks and features.

2. Scalability

Most SQL databases can scale up, increasing the processing power of existing hardware. NoSQL databases use a master-slave architecture that scales better horizontally with additional servers or nodes. These are useful generalizations, but it is important to note:

  • SQL databases can also scale horizontally, although the sharding or partitioning logic is often burdensome to the user and not well supported.
  • NoSQL technologies are diverse, and while many are based on a master-slave architecture, there are also options for scaling.
  • Savings from using more efficient data structures can offset differences in scalability; the most important thing is to understand the use case and plan accordingly.

3. Structure

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

NoSQL databases need not be limited to this format, but they generally fall into one of four broad categories:

  • column-orientedthe databases span row-oriented RDBMS, allowing efficient storage of large data and individual records with different attributes.
  • key valuestore are dictionaries that access multiple objects with a unique key for each.
  • DocumentStores store semi-structured data: objects that contain all your relevant information and that can be completely different from each other.
  • GraphicDatabases add the concept of relationships (deep links between objects) to documents, allowing for quick traversal of tightly connected data sets.

4. Properties

At a high level, SQL and NoSQL enforce separate rules for resolving transactions. RDBMSs must display four "ACID" properties:

  • Atomicityit means that all transactions must succeed or fail completely. They cannot be partially completed even in case of system failure.
  • Consistencyit means that at each step the database follows invariants: rules that validate and prevent corruption.
  • Isolationprevents concurrent transactions from affecting each other. Transactions must result in the same end state as if they were executed sequentially, even if they were executed in parallel.
  • Durabilitymakes transactions final. Even system failure cannot reverse 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 the same time:

  • Consistency:Each request receives the most recent result or an error. (Note that this is different from ACID)
  • Availability:Every request has a result with no errors, regardless of how recent that result is.
  • Partition tolerance:Any delay or loss between nodes will not interrupt the operation of the system.

5. Support and communities

SQL databases represent massive communities, stable code bases, and proven standards. Many examples are posted online, and experts are available to help those new to relational data programming.

NoSQL technologies are being adopted rapidly, but the communities remain smaller and more fragmented. However, many SQL languages ​​are proprietary or partnered with large individual vendors, while NoSQL communities benefit from open systems and a joint commitment to user onboarding.

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

SQL vs NoSQL Databases: MySQL, MongoDB and more.

Remember that SQL dialects share many properties as they interact with different databases. NoSQL types vary much more in their servicing systems, so comparing various non-relational technologies to SQL in general may be more useful.

Perhaps the most recognizable dialect of SQL is MySQL, a free (although also available under proprietary licenses) open source RDBMS. It is widely used in web applications, and is known for its 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 is known for being easy to use (less admin overhead), efficient for simple queries, and flexible thanks to NoSQL fundamentals. Excellent for hierarchical data storage, it also supports the familiar relational concepts of indexing, aggregation, and some measure of ACID compliance. Like MySQL, it is compatible with many platforms and programming environments, despite being relatively new.

Other SQL databases.

  • msqlis Microsoft's relational database product, accessed with proprietary Transact-SQL (T-SQL) and offered in a dozen editions aimed at different end users. Microsoft Azure includes a dedicated component for scaling MS-SQL databases in the cloud.
  • oracle databaseit is among the oldest and most established RDBMS. Its relational storage is interfaced by PL/SQL, although it is adapting to a multi-model system.
  • Other important RDBMSs includeAcceso, Ingres, PostgreSQL, Sybase,miSQLite.

Other NoSQL databases.

  • apacheCouchDBGenericName, like MongoDB, is a document-oriented database with JSON schemas and JavaScript queries. CouchDB's scaling capabilities stand out, employing a multi-master architecture over the typical distributed single-master design.
  • redis(Remote Dictionary Server), is the most popular key-value database. It is open source, with a fast, distributed in-memory implementation, and supports many abstract data structures (some rarely found in other NoSQL).
  • InfinityDBand of amazonsDynamoDBNameimplement two other key-value stores. columnar stores likecassandra,MariaDB, miwhichscales well horizontally, and popular graphics databases includeArangoDBName,InfiniteGraph, mineo4j.

The cloud and the future of SQL/NoSQL.

Modern brands emphasize interactivity between end users, justifying decentralization,cloud-based architectures,and expose diverse and new data in need of representation: enter NoSQL, champion of big, distributed, and metamorphosed data.

But if this non-relational interest got traditional RDBMSs off the mark, they are now making a resurgence. SQL continues to be more accessible, understandable, and most importantly, a lingua franca for data.

Often referred to as "Not Just SQL" due to its SQL-like programming support and coexistence with RDBMS, NoSQL increasingly represents a set of technologies with generalist scope and applicability. As described in many previous examples, traditional RDBMSs are also being rebranded as generalized databases and plugged in with NoSQL. Clearly, both paradigms remain equally valid in the modern transition to the cloud.

When to use SQL vs NoSQL for your business.

In general, NoSQL is preferred for:

  • Graph or hierarchical data
  • Data sets that are large and change significantly,
  • Companies growing extremely fast, but without data schemas.

In terms of use cases, this could translate into social media, online content management, streaming analytics, or mobile apps.

SQL is more appropriate when the data is:

  • Short
  • Conceptually modeled as tabular
  • In systems where consistency is critical.

Think small business accounting systems, sales databases, or transactional systems like e-commerce payment processing. When in doubt, SQL is also more appropriate since RDBMSs are more compatible and fault tolerant.

SQL vs NoSQL and the ROI of your business.

SQL is old and sometimes restrictive, but also proven and increasingly regarded as a universal interface foranalysis of data🇧🇷 NoSQL databases are new and flexible, but they lack maturity and require user experience. Pragmatically, both models are useful and even grow together.

Ultimately, a technology is only valuable when it serves your business, often with the highest ROI. Even companies like Google, with the resources to innovate ad-hoc NoSQL systems from the ground up (and fundamentals, seeSmall mapand BigTable), found that SQL provided additional value and restored it to critical systems.

From migrating from hand-coded SQL to compliant and governableETL Tools, to manage difficult unstructured data, to integrate relational and non-relational systems under one convenient umbrella,Talend offers solutionsin data storage paradigms.

Centralized and automateddata integration softwareit makes source systems, whether relational or not, easier to manage. Talend's products include tools that even users with little ETL experience can use to streamline processes. Connectors are available for all major RDBMSs as well as major NoSQL databases.

When you're ready to start, tryTalend data structureand start connecting and accelerating your data and data processes.

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