What Are NoSQL Databases and Why Enterprises Rely on NoSQL?

The business shift to digitization and customer experience has ushered enterprises into the digital revolution era. At the heart of the revolution are cloud technologies, mobile apps, big data applications, social and communication platforms, machine learning, and artificial intelligence. Running and maintaining modern applications has created a new set of technology requirements that can accommodate unprecedented scalability, speed, and data variability. Relational databases cannot meet growing requirements, and enterprises are turning to NoSQL (Not Only SQL) database (DB) technology designed for distributed data stores for huge-scale data needs.

NoSQL encompasses a wide range of database technologies that can store structured, semi-structured, and unstructured data. One of the key enterprise NoSQL features includes the ability to handle large volumes of data with high performance while maintaining flexibility in schema design. This makes NoSQL databases indispensable for building hi-tech applications.

But who uses NoSQL databases? Consider just a few examples of global businesses that are deploying NoSQL for their applications. From Twitter, Facebook, and Google, to Tesco, Ryanair, Marriott, Gannett, and other big and small enterprises, which accumulate terabytes of data every single day and rely on enterprise NoSQL databases for their big data and real-time web apps. These are prime examples of companies that use NoSQL databases to power their operations, enabling agility and scalability for dynamic, data-heavy applications.

The easiest way to understand what is a NoSQL database is to understand what it is not. Let’s start with the SQL understanding first. 

SQL stands for Structured Query Language. People call it S.Q.L. or sequel. In short, it is the name of a standard language for communicating with relational databases that store the data. It can pull, edit, add, search, update, and delete information in the database records.

As the name suggests, NoSQL is NOT a Structured Query Language, NOT use SQL to query the data, NOT follow strict schemas like relational models; it is NOT a replacement for an RDBMS. Instead, it uses documents with data types of descriptions and values to store data.

What are NoSQL databases used for? NoSQL databases are designed to be used across large, distributed systems. They are more scalable and much faster at handling large data loads than traditional relational databases. This core capability makes enterprise NoSQL an essential technology for organizations handling massive, dynamic data requirements. So, why use NoSQL? It provides superior scalability and flexibility, which relational databases simply cannot match when handling diverse and high-volume data sets.

As your application grows and you start to add new fields, your schema evolves as needed. Your database is scaled horizontally. So, if you need to build something quickly, NoSQL is an excellent way to go. 

RDBMS, on the other hand, scales by getting faster hardware and a larger memory. 

NoSQL databases have the following characteristics:

• Non-relational.
• NoSQL is schemaless.
• Most implement an aggregate pattern.
• It is running well on clusters.
• Open source/fully managed cloud.
• Have high scalability.
• Use distributed computing.
• Cost-effective.
• Simple API. Offers easy to use interfaces for storage and querying data provided.
• Able to process both unstructured and semi-structured data.
• No complex relationships, such as the ones between tables in an RDBMS.

NoSQL databases have emerged as the leading new data-storage technology. It is getting a lot of traction and hype these days, but it’s not that new of a thing in reality. 

The first database was engineered to run on a single server by Oracle in 1979. The only way to scale up these databases was to upgrade the servers – processors, memory, and storage. 

In 1998, Carlo Strozzi created a file system based database and used the term NoSQL for his lightweight, open-source relational database.

Google delivered the Bigtable exploration paper in 2006, and Amazon delivered the Dynamo research paper in 2007. These databases were built to meet another age of significant business necessities: Creating with deftness and working at any scale.

In 2009, the NoSQL term was re-surfaced when Eric Evans used it to name the current surge in non-relational databases.

NoSQL databases emerged in the era of mainframes and exponential development of web applications. When the cost of storage dramatically decreased, there was a need to create a complex data model to reduce data duplication. Developers were the primary cost of software development, so NoSQL databases were optimized for developer productivity.

NoSQL was engineered to meet a new generation of business requirements:

⇒ Data Storage: The digital data is measured in exabytes. One exabyte of information is equal to one billion gigabytes (GB). The amount of stored data added in 2006 was 161 exabytes. Just four years later, in 2010, the amount of data stored was almost 1,000 ExaBytes. 90% of the data on the internet has been created since 2016, according to an IBM Marketing Cloud study. In other words, there is a lot of data being stored in the world, and it’s just going to continue growing.

⇒ Interconnected Data: Major systems are built to be interconnected. The web fosters hyperlinks, pingbacks, and tags that tie things together. 

⇒ Complex Data Structure: NoSQL can handle hierarchical nested data structures easily. To do the same in SQL, you need multiple relational tables with all kinds of keys.

When compared to relational databases, NoSQL databases are often more scalable and deliver better performance. Relational databases store data in structured tables with rows and columns, making them flexible and easy to manage, especially in single-server environments. However, they face limitations when it comes to scaling in distributed systems.

As distributed systems leveraging cost-effective storage and processing power become more prevalent, the demand for high-speed performance and availability has significantly increased. 

NoSQL databases are better suited to meet these demands, offering greater efficiency and scalability in such contexts.

1. Simple to implement.
2. High scalability.
3. High availability.
4. Big data capability.
5. Store unstructured, semi-structured, or structured data with equal effect.
6. Enable easy updates to schemas and fields.
7. Is developer-friendly.
8. No single point of failure.
9. Easy replication.
10. Provides fast performance and horizontal scalability.
11. Often open-source and, therefore, lower cost. It may be an interesting choice for smaller enterprises with limited budgets.
12. Offers a flexible schema design that can easily be altered without downtime.

1. No standardization rules.
2. Limited query capabilities.
3. RDBMS databases are comparatively mature.
4. Staffing for NoSQL can be more costly.
5. It does not offer consistency when multiple transactions are performed simultaneously.
6. Difficult to maintain unique values as keys become difficult.

NoSQL databases operate by providing a schema-less or dynamic schema approach, making it easy to manage unstructured or semi-structured data. Unlike relational databases that rely on predefined table structures, NoSQL systems use flexible data models such as document-oriented, key-value pairs, column-family, or graph-based structures. This adaptability allows data storage to align with application-specific requirements. These databases are built on a distributed architecture, enabling them to process vast amounts of data across multiple nodes, ensuring scalability and resilience. Furthermore, they often prioritize eventual consistency, focusing on high availability and performance for modern, real-time applications.

The most important feature of a NoSQL database to consider is the data type it uses. Unlike SQL, which uses a relational model, NoSQL uses a variety of different models. They are categorized into four DB types: Key-value pair, Column-oriented, Graph-based, and Document-oriented. Let’s take a look at these four models, and explore how they’re different from one another.

⇒  Key-value databases 

The Key-value DB of NoSQL is used as a collection, associative arrays, etc. Key-value stores help to store schema-less data and heavy load. It allows horizontal scaling at scales that other types of databases cannot achieve. 

Examples of Key-value models: Redis, Dynamo, Azure CosmosDB, Riak. 

⇒  Document model 

Document-Oriented NoSQL DB stores and retrieves data as a Key-value pair, but the value part is stored as a document in JSON or XML formats. They are mostly used for CMS and content platforms, e-commerce applications, etc.

Examples Document originated DBMS systems: Amazon SimpleDB, CouchDB, MongoDB, Riak, Lotus Notes, MongoDB.

⇒  Graph model 

A Graph database stores entities and their relationships, represented as nodes and edges, respectively. Each node and edge is assigned a unique identifier, with edges defining the connections between the nodes. These databases are widely used in applications such as social networking, logistics, and spatial data.

Examples of Graph-based databases: Neo4J, Amazon Neptune, Infinite Graph, OrientDB, FlockDB.

⇒  Column-oriented Graph

Column-oriented databases work on columns and are based on Google’s BigTable paper. Column databases organize data in columns instead of rows. This structure allows for faster searches when querying only a few specific columns. These databases are commonly used in data warehouses, business intelligence tools, and CRM platforms.

Examples of Column-based NoSQL databases are Accumulo, Cassandra, Druid, HBase, Vertica.

Key-value and Document databases are similar.  In Key-value – we can say that the value is a document but the structure of the Document is opaque. In Document databases, we often have Document ID as a key, but the Document’s structure is often exposed and used for querying. None of the above-specified databases is better to solve all the problems; every category has its unique attributes and limitations.

NoSQL databases are tailored for handling massive volumes of diverse and rapidly changing data. They are ideal for powering real-time applications such as social media sites, e-commerce platforms, and video streaming services, where instant access to up-to-date information is critical. These databases also shine in big data projects and the Internet of Things (IoT) applications, making it easier to store and analyze massive, unstructured datasets. From powering recommendation engines to managing content in complex systems, NoSQL offers the flexibility to adapt to different needs. Industries such as healthcare and logistics also rely on NoSQL to organize critical information like patient records or supply chain data, all while keeping up with the demands of modern technology.

NoSQL databases are the best choice when dealing with large volumes of data that lack a fixed structure or when data requirements are constantly evolving. They are particularly useful for distributed systems that demand horizontal scalability and high availability. If your application requires handling real-time data, such as user sessions or live updates, NoSQL’s eventual consistency and fast write/read operations provide a significant advantage. Additionally, NoSQL is suitable for organizations that need to integrate multiple data types or formats without the constraints of a predefined schema. However, it’s essential to assess specific project needs to determine if NoSQL aligns with your requirements.

Popular SQL relational databases and RDBMSs are Oracle, IBM DB2, Sybase, MS SQL Server, Maria DB, PostgreSQL.

The major players in NoSQL have come about due to the companies that use them. Some of the biggest NoSQL technologies are:

DynamoDB organizes its data into partitions, supported by SSDs and replicated across three Availability Zones within a single AWS Region. The service automates partition management, requiring no manual effort from users.

To achieve an even distribution of data, DynamoDB recommends using a partition key with numerous distinct values relative to the table’s total item count. This ensures optimal performance and scalability.

Core building blocks and the essential DynamoDB components:

⇒  Tables. DynamoDB stores data in tables, which are collections that group related information. A Users table would hold data like user contact info, while an Orders table would contain customer order details. This structure is quite similar to the table concept in relational databases or the collection model in MongoDB.

⇒ Items. Every item in a table is made up of one or more attributes. In a Users table, an item could represent a user and contain attributes such as PersonID, LastName, and FirstName. In an Orders table, an item might represent a single order, with attributes like ProductID, Name, and Manager. In essence, items in DynamoDB are similar to rows or records in traditional databases, with attributes functioning like fields or columns.

⇒ Attributes. Each item is composed of one or more attributes. An attribute represents a basic data element that doesn’t require further breakdown. In a Users table, attributes might include PersonID, LastName, and FirstName, while in an Orders table, they could include ProductID, Name, and Manager. Essentially, attributes in DynamoDB function similarly to fields or columns found in traditional databases.

The database you chose for your project should meet its specific requirements. If your project involves structured data with complex relationships and requires consistency, a relational database may be suitable. For projects dealing with massive amounts of unstructured or frequently changing data, NoSQL databases provide better flexibility and scalability. Consider your team’s experience, potential data growth, and performance requirements while making your decision. The right database will not only support your current project, but will also scale as it grows.

The total cost of ownership of a NoSQL DB is a crucial consideration, and customers often overlook many factors impacting it.

The cost for NoSQL databases can often be minimal or even free, similar to some traditional relational databases like MySQL or MariaDB, which also offer free options. After initial costs, keeping up a NoSQL DB can be pricier, depending on its hosting solution. There are also a few hidden costs, including the added complexity of an endless choice of datastores, the inefficient use of hardware leads to server sprawl.

Consider that NoSQL specialists are less common than SQL specialists. Thus, staffing for NoSQL can be more costly.

1. NoSQL is a non-relational DB, that does not require a fixed schema, avoids joins, and is easy to scale.
2. NoSQL databases gained traction with major players like Google, Facebook, and Amazon, who deal with data on a massive scale.
3. Relational databases are expected to stay for many use cases.
4. Must use NoSQL for high scalability requirements applications.
5. NoSQL has some advantages because we precalculate data ahead of time – based on known access patterns.
6. RDBS has some tools – materialized views, aggregate tables, redundant data, and flat tables.
7. NoSQL removes size and speed limitations and makes this solution more natural.
8. If use NoSQL – expect to also use some relational engine for analytics and reporting.
9. If you use RDBMS, you can still add NoSQL for applications such as config, events, blob/document applications, messaging, and audits.
10. Four types of NoSQL Databases: Key-value, Column-oriented, Graphs based, Document-oriented.
11. NoSQL can handle structured, semi-structured, and unstructured data with equal effect.
12. NoSQL offers limited query capabilities.

The ability to scale, thereby taking advantage of commodity storage hardware in the cloud, makes NoSQL preferred for eCommerce and retail, media and entertainment, fin-tech, logistics, and healthcare business applications.

But what is the most essential is having a clear sense of what you’re trying to accomplish and selecting the database that will better suit your fundamental needs.

Every project is unique and needs careful technical analysis. Choosing an IT consulting partner is a wise investment to ensure the project’s success.

DevCom is focused on data-driven enterprise solutions, database design, and development, support, and maintenance. We cover the popular relational databases such as PostgreSQL, Microsoft SQL Server, as well as NoSQL platforms such as MongoDB, DynamoDB, CouchDB, etc. We are committed to making your databases reliable, scalable, maintainable, and secure.

Reach us at welcome@devcom.com to talk about your custom database development needs.