NoSQL is an umbrella of database types, each designed for a different use case or data type. The first challenge for selecting a database is finding the best structure for the data you’ll be storing. Sometimes there is a natural fit—for example, airline flight information fits very well in a graph database as this mimics real-life patterns—while long-form web content can usually slot into document databases easily (hence the name).
Choosing structure is about ease of maintenance and optimizing resources. The right structure will fit data logically, deliver data to applications utilizing it, and easily expand and scale. Though not always predictable, a roadmap of how your database will serve your business and what changes are to be expected can show you what kind of flexibility you require. For maximum scalability and flexibility, a key-value store is the best bet. Put simply, it stores tuples of data without imposing any other structural model at all.
Document databases house data together that are relevant to each other, and don’t require a standard schema across documents. Additionally, these documents can reference other documents, giving the document an element of structured depth. Document databases are useful for data that are strongly related but non-standard across tuples.
When much of your data consists of relationships between data points, graph databases are a straightforward choice. Graph databases break data down into nodes and relationships, storing properties on each. Because any node can have unlimited relationships with other nodes with a trivial effect on performance, these are optimal for particularly relationship-oriented data such as social networks.
Key Benefits of NoSQL Databases
How useful a particular database’s structure is for an organization depends a great deal on how it is going to be utilized. A database backend for a business-critical application is going to require vastly different parameters of availability, consistency, and query-ability than a big data analytics solution.
Fitting a database to your use case is about performance—choosing a solution that will make development easier and deliver the required results most effectively. For example, though columnar and document databases have similar conceptual models, they perform differently. Document databases can quickly read entire documents, while columnar will be faster at reading only specific data in a column. As such, a document database will likely better suit a blog that needs all the content (and associated metadata and comments for a particular post) delivered together every time.
Graph databases are optimized for querying across the relationships between nodes, so finding friends of friends in a social network or connecting flights between airports is easy to do with a graph database. Any application that requires this kind of relationship-oriented discovery will perform best on a graph database.
If all you need is to render a value that you can be easily found by its key, then a key-value store is the fastest and can scale the best. The drawback is a much more limited querying ability, so this won’t work well for analytic data. That said, rendering a user’s email address based on her username or caching web data is a simple and fast solution in a key-value store.
Database administrators – Non-relational, or NoSQL, databases have grown in popularity recently since they are easier to implement, have greater flexibility, and tend to have faster data retrieval times. They are cheaper and easier to scale, but don’t have the same levels of standardization and reporting tools.
Non-native databases are the most common, but allow users outside the company to insert and retrieve data. Some people believe this enhances data by providing increased, more human knowledge. These tools typically serve niche purposes for specific applications.
Data scientists – Relational databases are the more traditional storage option, where all data is filed in rows and columns. However, as data has become more complex, many data scientists now choose NoSQL databases, which allow for greater flexibility because they do not hold the user to the row-and-column format.
Those that need to collect extra large data sets in real time should look into big data processing and distribution systems. These tools are built to scale for businesses that are constantly collecting enormous amounts of data. Pulling data sets may be more challenging with big data processing and distribution systems, but the insights received may be more valuable due to the granularity of the data.
Key-value stores – Key-value stores save data as discrete couplets of name and value associated together with a key. No key necessarily needs the same structure, so data is simply accumulated instead of sorted into tables.
Document databases – Document databases store related data together in documents, a semi-structured schema that maintains a level of reportability by keeping associated metadata within the data itself.
Graph databases – Graph databases utilize topographical schemas to map data as if it were a physical structure of nodes and edges. Usually a node represents a particular record with associated data, and edges represent relationships between nodes (along with whatever data particular to the relationship).
Object-oriented databases – Object-oriented databases help organize data models and are typically used by people trying to structure large, complex data sets. These tools utilize query languages to retrieve information and create tables to be set with information.
Column-oriented or columnar databases – Column-oriented databases are key-value stores that impose more structure on their data. Key-value pairs (or columns) are associated together into families and tables. Unlike a relational database, the data within the tables and families are not consistent but the overlying structure allows greater potential for associating data together in hierarchies.
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