The simple answer is that each one stores data differently. Specifically, SQL uses a “schema,” which is used to define how data that is composed will be put into the database. SQL schema is more rigid than the more flexible NoSQL schema.

With its more free-form style, any data can be stored in any record. This gives an organization faster access to data, an important consideration if you really need speed and simple accessibility over reliability or consistency. Another advantage: If you don’t want to be tied to a specific schema because you may want to make changes later, NoSQL allows you to do so, even with large amounts of data. NoSQL requires less management with automatic repair, easier data distribution and simpler data models.

Not every application will succeed under such flexibility. SQL provides more safeguards and greater consistency than NoSQL databases. At the same time, NoSQL is still the new kid on the block and there are potential problems that go along with being untested in many situations.

There are four basic types: key-value store, document-based store, column-based store and graph-based store (though you can build graph on top of key-value). Key-value is designed for storing, retrieving and managing associative arrays, a data structure known as a hash table. A good fit for key-value databases would be for storing session information, user profiles, preferences and shopping cart data. Document-based stores documents composed of tagged elements, and may be the most useful to content management systems, blogging platforms and web analytics. With a column store, data is stored in cells grouped in columns of data rather than as rows, and is also considered a good fit for content management, as well as expiring usage. Graph based has a flexible graph representation. It is often ideal for scalability concerns and where there are problem spaces such as connected data that is used in things like social networks.

The two most common consistency models are either ACID (atomic, consistent, isolated, durable) or BASE (basic availability, soft-state, eventually consistency). With ACID, once the data is written, you will be able to access the data and get a consistent view of it. With BASE, once data is written, it will eventually appear for reading, which is why it is seen as having looser consistency. The majority of NoSQL databases don’t provide ACID guarantees, which may not be a big deal when talking about having to wait a few moments to see a Facebook post. But if an organization is dealing with billion-dollar financial transactions, then that may be opening the door for fraud. (Note, there are just a couple NoSQL systems that have some form of strong consistency). Choosing the consistency needs to be decided on a case-by-case basis.

NoSQL databases use distributed clusters of hardware to scale up, and are considered to be cheaper and more scalable than SQL databases. The flexible, schema-less model can store, process and access various types of business data, while providing greater control over data storage and processing. Some enterprises are using relational databases along with NoSQL, while others are scrapping their relational databases in certain scenarios because they get better performance and scale at a lower cost with NoSQL.

The bottom line is that NoSQL databases are cheap and open source. NoSQL typically uses inexpensive servers to manage fast-growing amounts of data and transactions. This can be an important consideration if an organization can’t afford costly RDBMS databases that use big servers and storage systems. Since there are options in how NoSQL systems store data, they can use less or more servers, but typically at the detriment of some other performance characteristic.

There are a few, but the most common is in-memory (DRAM). In-memory has fast performance characteristics, but since DRAM is expensive, it can be costly to scale out. In addition, multiple copies of data need to be stored for redundancy as in-memory data isn’t persistent.

The complementary storage mechanism is on-disk, but performance degrades significantly with typical NoSQL systems. The benefit is fewer servers, however. Aerospike, however, combines the best of both worlds with their Hybrid Memory Architecture™, which only stores indexes on DRAM yet persists data on disk, with yet the same performance levels of in-memory. Aerospike also has All Flash, which preserves 95% of the performance but has all the data on disk, condensing server footprint. Lastly, Intel has their Optane DC Persistent Memory, which has the performance of DRAM with the persistence of disk, also for much higher data densities.

If your organization wants to provide a personalized experience, then it’s going to require a lot of data from demographics to behavioral – and the flexibility of NoSQL is just the ticket. Other top uses can be profile management, real-time big data, content management, catalogs, customer 360-degree view, mobile applications, the internet of things (IoT), digital communications and fraud detection (which is also dependent on behavioral analysis). Organizations that need low latency, high scalability and speed for large amounts of data find that NoSQL gives them the responsiveness they need.

There are a number of innovative features for NoSQL that are being developed, according to Forrester. Among them: greater automation that helps speed up NoSQL deployments and support more complex applications with little effort. In addition, open-source NoSQL solutions “are stable and ready for primetime,” Forrester reports.