When it comes to databases, the big question-mark is…SQL or NoSQL? Storing and managing your ever-expanding data with relational databases used to be the default approach. But depending on how fast you are scaling and the applications you are using, they may not be designed to handle your data sets. Now, more than ever, NoSQL databases can vastly help your organization. They store huge amounts of data that can be accessed by countless connected devices in parallel—without compromising performance.
In this article, we’ll define the NoSQL database, explore its various types, and delve into its strengths and weaknesses. Is it right for you and do you need one asap? Let’s find out!
What is a NoSQL Database?
NoSQL databases, also known as Not Only SQL databases, are highly scalable, non-relational databases that can handle structured, semi-structured, and unstructured data. Traditional relational databases store data in a tabular format with a well-defined data model and structure. In contrast, NoSQL databases operate in elastic environments where large volumes of various data types are processed concurrently across multiple systems. They are horizontally scalable, scaling out by adding multiple nodes instead of scaling up by augmenting the resources in an individual node.
Below is a comparison between NoSQL and relational database management system (RDBMS) databases:
|Data Model||Key-based data retrieval||Tables and schemas|
|Data Type||Structured, semi-structured, and unstructured||Structured|
Types of NoSQL Databases
NoSQL databases come in multiple forms which cater to different needs. These forms include:
Key-value databases generally operate using a simple data model which associates a unique key with its corresponding value. These databases are considered high performance because they store data in the memory, making them suitable for compute-intensive and read-heavy workloads. Examples of key-value databases include Redis, Memcached, Oracle Berkeley DB, and Riak.
Document databases are designed to provide flexibility and enhance performance, storing semi-structured data and the data’s description in a document format (typically an XML or JSON). Instead of storing frequently queried data in different tables, document databases store it in the same document. MongoDB & CouchDB are examples of document databases extensively used in cloud-based and big data applications.
As the name suggests, columnar databases store data in columns rather than in rows. These databases provide high data compression rates, thus saving disk space and leading to faster query execution. Cassandra is an open source, columnar database developed by Facebook and previously used in its Inbox search. Cassandra is known to have ‘masterless’ architecture because it treats all nodes the same—there is no master node. You can read more about Cassandra node-based architecture here.
Graph databases are collections of nodes and edges. Each node represents a record and each edge represents the connection between the nodes. Neo4j is a graph database widely used in real-time recommendation engines and social networking websites and applications.
Strengths & Weaknesses of NoSQL Databases
NoSQL databases offer multiple advantages, such as:
- Scalability: They support scaled-out architecture. Simply add an extra node to cater to the increased load on the system.
- Agility: They support flexible data modeling and schema-less architecture.
- Easy Integration: They offer open APIs which can help integrate multiple third-party platforms.
NoSQL databases don’t have many disadvantages and a large number of companies are using MongoDB and other variants to run their workloads. However, it is difficult to analyze data stored in NoSQL databases without programming expertise and not many business intelligence tools provide NoSQL database support.
NoSQL databases offer exciting features for the new generation of cloud-based and big data applications, including many that traditional RDBMS databases lack. Still, when choosing a database for your application, consider the pros and cons of these databases before making a final decision. In the next article of this three-part series, we will compare the two most popular NoSQL databases: MongoDB and Cassandra.
N2WS Announces Backup and Recovery Support for Amazon DynamoDB
Very recently, we announced the immediate availability of Cloud Protection Manager v2.3 which includes an onslaught of many powerful new features. Among these, are automated backup and recovery operations for Amazon DynamoDB, a fully managed (Serverless) NoSQL database service. You can now create policy/schedule-based backups for Amazon DynamoDB tables, along with existing AWS service support including Amazon EC2 instances, Amazon RDS, Amazon Aurora databases, and Amazon Redshift clusters. CPM is a fully-featured solution providing unparalleled backup and recovery for data held in the AWS cloud.