NoSQL vs SQL

With Couchbase predicted to debut in the stock market in the first half of 2021 for as much as $3 billion — after quietly filing an IPO — the conversation of “SQL vs NoSQL” is even more rapidly travelling beyond just ears in niche community forums, and becoming a major public matter. Let’s take a look at the key differences when choosing between the two for your big data management.

SEE ALSO: Tutorial – Getting Started with NoSQLBench

An Overview of the Basics

Databases store information according to type and function. Individual databases have different strengths and weaknesses, and two important broad database categories are “relational” (SQL) and “non-relational” (NoSQL/Not-Only-SQL). The two frameworks are increasingly crossing over with each other but the two principally model data differently — offering either acute integrity or agile scalability.

NoSQL climbed in popularity during the 90s after the internet’s explosion and, with it, torrents of unstructured user generated content — modern NoSQL databases shored up important limitations and weaknesses found in SQL (notably, horizontal scaling). Today, the two are powerful tools at our disposal for organising big data, communication problem-domains, and rapidly evolving cloud capabilities.

What we’ll explore in this guide:

1. Handling data
2. ACID v. BASE
3. Key use cases
4. The takeaways

Handling Data

SQL and NoSQL have somewhat diverging contexts wherein they are the optimum option for ideal performance levels.

For OLTP transactions, such as withdrawing money from a single or joint bank account, relational databases have an advantage in speed. They are very effective at doing single updates on very localised parts of the database, such as a single account tuple, or a couple. Because they are modelled on the tabular, row-oriented schema, handling multiple attributes in this way is natural for this sort of database. Everything is optimised for transactional superiority as compared to data analytics. Other similar uses of OLTP are in online banking, text messaging, and booking airline tickets.

OLAP systems are a type of analytical processing that allows users to analyse data drawn from multiple database systems simultaneously — e.g. getting averages across tables or doing some other kind of aggregation. This can be done much faster via columnar NoSQL databases than with SQL databases. If you depended on the RDBMS model, you would have to wait for 100s-of-millions of entry rows for various columns to be searched individually, or have to create composite indexes ahead of time. NoSQL databases, however, can instantly hone in on pertinent columns and rows.

There is also a difference in how each handles varied data. RDBMS models require databases to be planned out ahead of time in great detail, and scaling can be expensive if new types of data need to be integrated. NoSQL is naturally suited to unstructured data, and so tends to be better suited to agile application development or DevOps.

In summary: SQL is biased towards extremely reliable daily transactions for organisations, allowing massive vertical scaling for an increasing customer base, with lightning-fast updates — via simplified single processes. Whereas NoSQL is biased towards meta-analytical needs, from forecasting and budgeting to planning and insights; as well as handling all sorts of unstructured data for user accounts, social networking, and so on — for a horizontally-scaled network.

ACID vs BASE

SQL is modelled on ACID database properties whereas NoSQL follows BASE properties. ACID is inflexible, relative to BASE. This isn’t necessarily a weakness; for instance, SQL’s ACID normalisations make it a superior option for dealing with daily financial transactions, as compared to NoSQL. This is partly why Oracle, the largest database provider in the world, is so heavily used in enterprise, government, and finance corporations.

BASE properties are far less pessimistic about consistency, i.e. they are less concerned about data safety. If you’re doing a banking transaction, you want to make sure that every chain along the transaction is successful, before committing to it (to constrain the possibility of errors like two people withdrawing the same funds at the exact same time). The benefit of less acute data safety (i.e. BASE), however, is better resiliency and scalability.

Key Use Cases

NoSQL –

• Key value stores — Stores data as sets of unique identifiers, with each set containing an associated value. No default query language is used. Very simple get, put, and delete commands create high-performance solutions for: web applications storing things like comments and user preferences; making real-time recommendations and adverts possible, as stores can quickly retrieve and deliver up-to-date ads or recommendations during the visitor journey; and it can speed up applications via in-memory data caches. Examples of key-value stores include: Redis, Riak, and Voldemort.
• Graph databases — An especially popular tool with social media companies such as Facebook and Twitter for doing social network analytics and social graphs. In the discussion of SQL v NoSQL, graph databases represent a grand union between the two worlds, because (unlike the typical concept of NoSQL databases), graph databases go hand-in-hand with the ‘Six Degrees of Separation’ idea. It’s an ‘emerging shape’ in big data and by no means found only in social networks; a wide array of industries use it, from finance to healthcare and crisis-response networks. Database examples include HyperGraphDB and Neo4J.
• Document databases — An ideal database for handling vast amounts of semi-structured data, which typically becomes stored as either XML, JSON, or BSON. Each record, and data associated with it, is stored in a single document. There isn’t relations or joins support but document stores are used in a wide array of circumstances: Many different web apps including blogs and CMSs; various real-time & in-game tracking, stats, and guild membership aspects in gaming; and various components of user accounts and user generated content. Document store examples include MongoDB and CouchDB.
• Wide column stores — And, as mentioned in the Handling Data section, column databases are exceptionally advantageous for meta-insighting across vast areas of databases, or multiple databases: business reporting, forecasting, budgeting, more. Column database examples include Cassandra and HBase.

For SQL, I’ll focus on the titans in the DBS industry. SQL has entrenched itself much deeper than NoSQL and so most people use one of these three systems –

• Oracle — Enterprises and larger organisations tend to use Oracle. It’s been around since the 70s and proven itself, inside-and-out, over that time. Oracle releases versions for different scales of corporate users with varying budgets. The SQL language can be used to access Oracle, but you must first modify the base proprietary database. It’s a high-end model and most commonly found in private cloud or intranet uses, rather than commercial web hosting, due to its cost.
• MySQL — Also widely used in commercial environments. Unlike Oracle, this is a viable web hosting solution. In fact, most Linux-based web hosting providers rely on MySQL for creating connected databases as it’s simple and trusted. Overall, it’s extremely well-integrated because hosting companies and programmers are so familiar with it and see it as secure, reliable, and fast at processing SQL queries.
• Microsoft SQL Server — The third titan, Microsoft SQL Server is Microsoft’s proprietary RDBMS. It’s the most popular Windows-based hosting and networking SQL option, although there is a Linux version. This is the go-to choice for Windows programmers and developers of ASP.net and ASP, i.e. those who use Windows systems. It’s also equipped for enterprises and robust but similarly as prohibitively costly as Oracle, so that small businesses usually do not choose this. Linux licences are substantially cheaper.

SEE ALSO: NoSQL and automated databases – “DBAs can be the thought leaders”

The Takeaways

SQL and NoSQL basically differ in that one attends more to acute data security and sharper constraints; while the other focuses on data availability and flexibility in handling unstructured and semi-structured data. Couchbase may enter the stock market at $3 billion, and with it, make a bold statement on that latter offering-set — and of the rise of NoSQL for handling massive data volumes in tandem with CI/CD.

To avoid prolixity, I’ve made a good number of noteworthy omissions to this overview — such as the various ways that intermediaries are bridging the gap between SQL vs. NoSQL. We also didn’t mention PostgreSQL, which is nipping at the heels of the three SQL titans, in popularity. PostgreSQL is also especially notable for being something of a hybrid between relational and non-relational models. Finally, regardless of the database, leading-edge cybersecurity awareness is immensely important today, as major breaches have become an omnipresent phenomenon in the 21st century.

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