Ocean of NoSQL databases. Which one is best for your Business Application

Updated: Aug 1

Today, most data is stored digitally. Most of the information on the internet is stored in some type of server or database, operated by different companies. For us to be able to use and manipulate this data, we need a medium that is convenient and accessible. SQL (Structured Query Language) is one of the earliest database management systems, streamlining the process of data collection, storage, and retrieval. The phrase refers to a decade-old paradigm that still exists today in the sheer variety and depth of database systems. There is growing interest in fundamentally different technologies due to the growing volume of unstructured data, the availability of storage and processing power, and the evolving analytic requirements.

As a popular alternative to traditional RDBMSs, NoSQL shows promise for a variety of modern applications.

What is a NoSQL Database?

In general terms, Databases, which store data in a format different from relational databases, are known as NoSQL databases. NoSQL stands for “not only SQL,” which pertains to the fact that either the database can store and manage data using “no SQL” at all or it can work in a combination that combines the flexibility of the newer approach (NoSQL) with the power of the traditional relational system (SQL).

Types of NoSQL Database

  1. Document Databases

  • The data in a document database is stored in JSON, BSON, or XML documents (not in Word documents or Google Docs, of course). Documents can be nested in a document database. For faster querying, certain elements can be indexed.

  • A document can be stored and retrieved in a form that is much closer to the data objects used in applications, which means less translation is required. When moving data between applications and storage, SQL data must often be assembled and disassembled.

  • Document databases are popular with developers because they have the flexibility to rework their document structures as needed to suit their application, shaping their data structures as their application requirements change over time. This flexibility speeds development because, in effect, data becomes like code and is under the control of developers. In SQL databases, intervention by database administrators may be required to change the structure of a database.

2. Key value stores

  • A key-value store is the simplest type of NoSQL database. Each data element is stored as a key-value pair consisting of an attribute name (or "key") and a value. A key-value store is essentially a relational database with only two columns: the key (such as "state") and the value (such as "Alaska").

  • Shopping carts, user preferences, and user profiles are examples of use cases.

3. Column-oriented databases

  • A column store is organized as a set of columns, while a relational database stores data in rows. If you want to run analytics on a small number of columns, you can read those columns directly without consuming memory. As columns are often of the same type, they benefit from more efficient compression, making reads even faster. Columnar databases can quickly aggregate the value of a given column (adding up the total sales for the year, for example). For example analytics.

  • Unfortunately, there is no free lunch, which means that while columnar databases are great for analytics, the way in which they write data makes it very difficult for them to be strongly consistent as writes of all the columns require multiple write events on disk. Relational databases don't suffer from this problem as row data is written contiguously to disk.

4. Graph Database

  • In graph databases, connections between data elements are captured and searched, overcoming the overhead associated with JOINing multiple tables.