What are the 4 types of NoSQL databases?

Introduction to Types of NoSQL Databases

NoSQL is a non-relational database management system, which does not need to have a fixed schema.
NoSQL database system contains various database technologies that can manage structured, unstructured, semi-structured and polymorphic data.
We must use the relevant database to solve a specific set of problems as there are various solutions that are better than all of the others.

Different Types of NoSQL Databases

To understand the NoSQL database, let’s go through the following types,

1. Key-Value Stores

Based on distributed hash tables and Amazon’s Dynamo paper.
Created to handle massive amounts of data and store schema-free data.
Data insertion and retrieval are simple.
Stores data as a hash table where all keys are unique and the value can be a string, JSON, BLOB, etc.
Values can be stored against strings, lists, hashes sets and sorted sets as a key.
Collections, associative arrays, and dictionaries, etc. can be used as key-value stores.

Examples:

Use Cases:

Key-value data stores are ideal for storing session information, user profiles, comments, and recommendations of products.
Redis:
- Pinterest uses to store lists of images, galleries, users, boards, followers, and un-followers.
- Coinbase uses to authorize rate points and assurance the rightness of the Bitcoin transactions.
- Twitter uses to manage the Twitter timeline.
Memcached:
- Quora uses to maintain the cache of moderate, determined databases.

2. Document Stores

It is a collection of documents and a document is a collection of key-value type. All values or the data stored in documents can be accessed using the keys.
All documents are independent of each other.
Documents are schema-free and therefore are flexible and easy to modify.
Collections are used to store documents in order to group different kinds of data.
Documents may have nested documents, various different key-value pairs, or key-array pairs.

Examples of Document Databases:

MongoDB
Couchbase
RavenDB

Use Cases:

MongoDB:
- SEGA uses for managing game accounts.
- Aer Lingus uses to handle ticket management and internal applications.
- ly and Sourceforge use to manage data.
Couchbase:
- Cisco used to achieve greater scalability.

3. Column-Oriented

The databases came into the picture after Google’s research on the Google file system named BigTable distributed storage system.
Column-oriented databases basically designed to work on columns, every column is treated separately. All values are stored contiguously for each column.
Column-oriented databases stores data in column specific files with the same type.
In column-oriented databases, query processors operate on columns, it improves query performance as it can access specific column data. Also, it gives a performance on aggregation queries.
All data associated with the single column is ideal for compression because of the same type.
Column-oriented databases are designed like arrays with three-dimensional, the row identifier is the first dimension, a combination of a column identifier with family is the second and the timestamp is third.

Examples of Column Databases:

Apache Cassandra
Hadoop HBase
HyperTable

Use Cases:

The column-oriented database gives a highly scalable architecture. and high performance as they are fast for insertion and retrieval, They have been very popular among organizations working on internet of things, big data, and personalized content recommendations.
Cassandra:
- Outbrain uses to serve personalized content recommendations.
- Spotify uses to store metadata about artists, songs, etc. and user profile parameters for better personalization.
HBase:
- Facebook used for various services like search indexing and the “Nearby Friends” feature.

4. Graph Network

Graph network database stores data in a graph like structure.
Graph databases used for storing frequently changing data.
Graph databases are capable of representing any kind of data. It provides high accessibility as it uses indexes for lookups.
A graph database is a collection of one or more nodes and edges.
Each node represents an entity (such as a student or business) and each edge connects two nodes that show the relationship between them.
A unique identifier is used to define every node and edge and aware of its adjacent nodes.
The graph can be traversed in a depth-first or breadth-first fashion.starting from any arbitrary or start node.

Example of graph databases:

FlockDB
HyperGraphDB
Neo4j

Use Cases:

Graph databases are getting used when dealing with large datasets for establishing data relationships.
Graph databases offer fast query performance than relational databases, especially as data grows.
Neo4j:
- Walmart uses to provide relevant product promotions and recommendations.
- Medium uses it to build its social network to improve content personalization.
- Cisco uses to analyze customer support issues to anticipate bugs.

5. Hierarchical

Uses parent-child relationships or tree structures to store data.
In relational models, this is referred to as a one-to-many.
Location information is stored in Geospatial databases in the hierarchical.

Examples of Hierarchical Database:

Use Case:

Microsoft’s Windows registry and IBM’s IMS database are the best examples of hierarchical

6. Object-Oriented

These databases are fully NoSQL databases is a debatable topic yet. These databases are not similar to data models based on traditional RDBMS.
Allows the storage of data in the form of objects.

Examples of Object-Oriented Database:

Use Case:

Object-oriented databases are generally used in web-scale production or research purposes.

7. Triple Stores

Triple stores manage the data in the form of Subject-Predicate-Object.
The predicate is used for linking subject and object.
Triple Scores are a variety of network databases.

Examples of Triple Store Database:

Sesame
Jena
Virtuoso
Allegro Graph

Use Case:

Example: ‘Adam writes eduCBA blog.’ Here, Adam is the subject, while eduCBA blog is the object, here the predicate is the term ‘writes’ which links the subject and the object. In such scenarios, NoSQL is the best option as mapping it into SQL queries is quite difficult.

Conclusion

Seven types of NoSQL Database are Key-value stores, Document stores, Column-oriented, Graph Network, Hierarchical, Object-oriented and Triple Stores, etc.
We hope this guide helps you get an idea of which NoSQL database type to choose for your application or project. More supported data models within one database mean more versatility for you and to the end-user, in the way you should store your data.

What is NoSQL Database?

NoSQL stands for Not only SQL. NoSQL Databases are also known as non-relational databases that don’t require a fixed schema. Users can create documents with flexible schema and can scale up evenly without much of a stretch. NoSQL information bases are largely utilized for BigData and ongoing applications. NoSQL database processes information in a distributed manner and can oblige tremendous volumes of data.

Internet giants like Facebook, Google, Amazon use NoSQL databases heavily to deal with Terabytes of data daily.

As NoSQL Database uses distributed storage, it is easy to scale them up horizontally with commodity hardware. In the RDBMS world, the system tends to go slow when the volume of the data increases and could be tackled by scaling up the existing hardware. However, this process is expensive and inefficient. An alternative to this problem is distributing the data load to separate commodity hardware whenever the load increases.

Image Source: Guru99

Hevo Data, a No-code Data Pipeline helps to integrate data from 100+ sources to a Data Warehouse/destination of your choice to visualize it in your desired BI tool. Hevo is fully-managed and completely automates the process of not only loading data from your desired source but also transforming it into an analysis-ready form without having to write a single line of code.

Its fault-tolerant architecture ensures that the data is handled in a secure, consistent manner with zero data loss. It provides a consistent & reliable solution to manage data in real-time and always have analysis-ready data in your desired destination. It allows you to focus on key business needs and perform insightful analysis using a BI tool of your choice.

GET STARTED WITH HEVO FOR FREE

Check out what makes Hevo amazing:

Secure: Hevo has a fault-tolerant architecture that ensures that the data is handled in a secure, consistent manner with zero data loss.
Schema Management: Hevo takes away the tedious task of schema management & automatically detects schema of incoming data and maps it to the destination schema.
Minimal Learning: Hevo with its simple and interactive UI, is extremely simple for new customers to work on and perform operations.
Hevo Is Built To Scale: As the number of sources and the volume of your data grows, Hevo scales horizontally, handling millions of records per minute with very little latency.
Incremental Data Load: Hevo allows the transfer of data that has been modified in real-time. This ensures efficient utilization of bandwidth on both ends.
Live Support: The Hevo team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.
Live Monitoring: Hevo allows you to monitor the data flow and check where your data is at a particular point in time.

Simplify your data analysis with Hevo today!

History of NoSQL Databases

Handling SQL Databases are a time-consuming and costly task. Developers were the cost to the company rather than storage. Back in the late 2000s, NoSQL Databases emerged that changed the game of creating complex and difficult manage data models to avoid data duplication. NoSQL Databases are the perfect replacement for decreasing storage costs and increasing Developers’ costs. It optimizes the daily data management activities.

Due to the decline in prices of Data Storage, a sudden surge in the demand for storage and query increased. The data came in all shapes and sizes i.e., from structured and semi-structured to polymorphic data. Defining Schema for data for all the huge volumes of data in advance in SQL databases is next to impossible. That is where NoSQL Databases handled all the demands for storing unstructured data. It helped developers store data and provide them greater flexibility.

At that time Cloud Computing also came into action and the need to host applications and data on public Clouds increased. Developers wanted to distribute the data across multiple servers to make their applications and data available all the time anywhere. NoSQL Databases helped them manage all the data hassle-free.

Why should you use a NoSQL Database?

NoSQL databases serve as a great fit for various modern applications such as web, mobile, and gaming that need scalable, flexible, high-performance, and highly functional databases to offer great user experiences.

Scalability: NoSQL databases are usually designed to scale out by leveraging distributed clusters of hardware as opposed to scaling up by adding robust and expensive servers. Some cloud providers tackle these operations behind the scenes as a fully-managed service.
Flexibility: NoSQL databases usually offer flexible schemas that allow faster and more iterative development. The flexible data model makes NoSQL databases perfectly suited for unstructured and semi-structured data.
Highly Functional: NoSQL databases provide highly functional data types and APIs that are purposely built for each of their respective data models.
High-performance: NoSQL databases are optimized for particular access patterns and data models that allow higher performance as opposed to trying to accomplish similar functionality with relational databases.

How Does a NoSQL Database Work?

NoSQL databases leverage a variety of data models for managing and accessing data. These types of databases are optimized specifically for applications that need large data volume, flexible data models, and low latency, which can be achieved by relaxing some of the data consistency restrictions of other databases.

Here’s an example of modeling the schema for a simple book database:

In a relational database, a book record is often dissected (or normalized) and stored in separate tables, and relationships are defined by foreign and primary key constraints. In this instance, the Books table has columns for Book Title, ISBN, and Edition Number. The Authors table consists of the columns Author Name and AuthorID, and the Author-ISBN table consists of the columns ISBN and AuthorID. This relational model is designed specifically to allow the database to enforce referential integrity between tables within the database, normalized to decrease the redundancy, and generally optimized for storage.
Within a NoSQL database, a book record is generally stored as a JSON document. For every book, the item, Book Title, ISBN, Edition Number, Author ID, and Author Name are stored as attributes within a single document. In this model, the data is optimized for horizontal scalability and intuitive development.

Features of NoSQL Databases

NoSQL Databases offer numerous features over traditional databases. We have listed a few of the most popular features of NoSQL Databases.

1. Schemaless Tables

NoSQL Databases are schema-less and can store heterogeneous data from the same domain easily. Users can quickly load complex schemas and heterogeneous data in the same NoSQL documents or tables.

2. Non-Relational Structure

NoSQL Databases don’t rely on relational models, and it doesn’t store the data in flat fixed schemas. It also doesn’t support complex features like query language, integrity-joins, ACID operations, etc. The above-listed points make the NoSQL Database popular among the BigData and real-time fields.

3. Simple API Controls

NoSQL Databases offer easy-to-use API interfaces to allow low-level data manipulation. They are very well versed with REST endpoints.

4. Distributed Computing

NoSQL Databases offer distributed processing of queries along with auto-scaling and failover mechanisms.

Image Source: Guru99

Types of NoSQL Databases

There are four different categories of NoSQL Databases:

Document Databases
Key-Value Stores
Column-Oriented Databases
Graph Databases

Image Source: Guru99

Let’s discuss each of them in detail.

1. Document Databases

Document Databases use key-value pairs to store and retrieve data from the documents. A document is stored in the form of XML and JSON. A typical example of the document database is shown below:

Image Source: Guru99

The above figure shows that the Document database contains data in JSON (or XML) format and can contain varying schema. The Documents can be nested and indexed for faster querying.

Document databases allow developers to restructure their Documents based on their application requirements which may change over time. In contrast, in the RDBMS world, database administrators are required to restructure the database schemas.

Examples of Document databases are – MongoDB, OrientDB, Apache CouchDB, IBM Cloudant, CrateDB, BaseX, and many more.

2. Key-Value Stores

Key-value Stores are the simplest type of NoSQL Database. It uses keys and values to store the data. The attribute name is stored in ‘key’, whereas the values corresponding to that key will be held in ‘value’.

In Key-value store databases, the key can only be string, whereas the value can store string, JSON, XML, Blob, etc. Due to its behavior, it is capable of handling massive data and loads.

The use case of key-value stores mainly stores user preferences, user profiles, shopping carts, etc.

DynamoDB, Riak, Redis are a few famous examples of Key-value store NoSQL databases.

3. Column-Oriented Databases

Column-oriented databases store the data in a set of columns known as column families. That means that whenever a user wants to run queries for a smaller number of columns, they can read those columns directly without consuming memories corresponding to all data. The working of the Column-oriented database is based on the concept of the BigTable paper by Google. Below schematics shows how values are stored on Column-oriented databases:

Image Source: Guru99

HBase, Cassandra, HBase, Hypertable are NoSQL query examples of column-based databases.

4. Graph Databases

Graph databases form and store the relationship of the data. Each element/data is stored in a node, and that node is linked to another data/element. A typical example for Graph database use cases is Facebook. It holds the relationship between each user and their further connections.

Graph databases help search the connections between data elements and link one part to various parts directly or indirectly.

Image Source: Guru99

The Graph database can be used in social media, fraud detection, and knowledge graphs. Examples of Graph Databases are – Neo4J, Infinite Graph, OrientDB, FlockDB, etc.

Different NoSQL Databases

Here are the different types of NoSQL databases you can leverage for your requirements:

MongoDB
Cassandra
ElasticSearch
Amazon DynamoDB
HBase

MongoDB

MongoDB is one of the most widely known document-based databases that is capable of storing documents within JSON objects. You can leverage MongoDB if you are expecting a lot of reads and write operations from your application but you don’t care much about some of the data being lost in the server crash.

You can also use MongoDB when you are planning to integrate hundreds of different data sources since the document-based model of MongoDB serves as a great fit to provide a single unified view of your data. You can even use it to clickstream data and use it for customer behavioral analysis.

Cassandra

Cassandra is a popular distributed, open-source database system that was initially built by Facebook that is widely available and quite scalable. It can easily handle petabytes of data and thousands of concurrent requests per second.

You can leverage Cassandra when your use case needs more writing operations as compared to reads. Cassandra also comes in handy when you need a lesser number of aggregations and joins in your queries to the database or when you need more availability and consistency.

ElasticSearch

ElasticSearch is another distributed, open-source NoSQL database system that is highly consistent and scalable. It is also known as Analytics Engine to its users. You can easily leverage it to store, analyze, and search huge volumes of data.

If the full-text search is a part of your use case, ElasticSearch would be the best fit for your tech stack. It also allows you to search with fuzzy matching. ElasticSearch can also come in handy when storing logs data and analyzing it.

Amazon DynamoDB

Amazon DynamoDB is a highly scalable, key-value pair distributed database system developed by Amazon. It can easily tackle 10 trillion requests per day and more than 700 companies are leveraging Amazon DynamoDB as a part of their tech stack including Lyft, Snapchat, and Samsung.

If you are looking for a database that can handle various simple key-value pairs but those queries are quite large in number, then Amazon DynamoDB should be your go-to choice. If you are working with OLTP workload such as online banking or ticket booking where the data needs to be highly consistent, DynamoDB can come to the rescue.

HBase

HBase is another open-source, highly scalable distributed database system in this list. HBase was penned in Java and runs on top of the Hadoop Distributed File System (HDFS).

You can leverage HBase if you have at least a few petabytes of data to be processed. However, if your data volume is small, you won’t obtain the results you want. You can use HBase when you want to store real-time messages for billions of people or if your use case needs random access to the data.

When Should NoSQL be Used?

One should always know when to use a NoSQL Database. A few factors are listed below:

To manage huge volumes of data.
Managing modern applications paradigms like microservices and real-time Streaming.
Need to store structured and semi-structured data.
Fast-paced Agile development.
Need to scale-out architecture.

Difference between RDBMS and NoSQL

There are many differences between Relational Database Management Systems (RDBMS) and NoSQL Databases. The core difference is how the data is modeled in both of the Databases. A few key differences between RDBMS and NoSQL are listed below:

RDBMS is a relational Database, while NoSQL is a distributed database.
RDBMS is vertically scalable which means servers have to be added and power has to be increased which makes scalability through RDBMS Databases quite expensive. NoSQL Databases are horizontally scalable which means it needs to add more machines only.
RDBMS has a fixed schema that makes it possible to insert data in a uniform format. It makes the data redundant and helps get the primary and foreign keys to fit data in the tables. In NoSQL databases, there is no need to have a schema. One can add data in structured as well as semi-structured which makes them more flexible than RDBMS Databases.
RDBMS has stored procedures to manage and understand data whereas NoSQL Database does not have any stored procedures which makes it difficult for users to identify patterns.

NoSQL vs. SQL: What’s the difference?

Here are the primary differences between NoSQL and SQL:

Data Model: With NoSQL database systems, the data is modeled as tables with fixed columns and rows, as with a SQL database. As opposed to that, depending on the NoSQL database, data can be modeled as key-value pairs, JSON documents, or graphs with edges and nodes. Wide-column stores use the table and row concept, but the columns can be considered dynamic from row to row within a table.
API: For NoSQL databases, SQL isn’t needed as an API to the data in the database, although, various NoSQL databases provide a SQL-like query language. For SQL databases, SQL is typically the predominant interface to the data. Most NoSQL databases manage data integrity with an approach called BASE (Basically Available, Soft State with Eventual Consistency). Using BASE, data might be inconsistent for a little while, but database replication will eventually update all the copies of data to be consistent. The approach used by SQL databases is ACID. Using ACID, each transaction- when executed independently, in a consistent database state- with either finish, producing correct results, or terminate, with no effect.
Data Integrity: SQL and NoSQL databases adopt different approaches to protect the integrity of the data as it gets generated, updated, read, and deleted by users and applications.
Schema: The schema for a NoSQL database is pretty flexible, which means that there is no fixed structure to the data, data lengths, and types for data elements. You can store the data in a free-form, or schemaless manner. With SQL, the database of the schema is fixed, with rigid data lengths and types for each column, and every row needs to match the defined column structure and layout. For instance, if a column is defined as an integer, only integer data can be stored in the column and any attempt to do otherwise gets rejected by the DBMS.
Scalability: NoSQL databases usually implement horizontal scaling, also called scaling out. Scaling out involves adding more hardware to the system, generally, in the form of new commodity servers. Horizontal partitioning leverages sharding to bisect large databases into smaller pieces spread across multiple servers is frequently utilized in NoSQL systems. On the other hand, the SQL approach is vertical scaling, also called scaling up. You need additional resources to scale up, such as a more powerful CPU or supplementary memory, to tackle the additional workload or to enhance performance.

Advantages of NoSQL Databases

Let’s understand some of the advantages of NoSQL Databases:

NoSQL database is optimum for processing massive volume data with distributed processing.
NoSQL database supports failover mechanisms and ensures high availability.
NoSQL database provides easy replication along with horizontally scalable capability.
NoSQL database is capable of handling structured, semi-structured, and unstructured data.
NoSQL databases can be installed on commodity hardware and can form clusters for distributed processing.
NoSQL database offers flexible schema and can be changed at runtime without service downtime.

Disadvantages of NoSQL Databases

In the above sections, we have discussed a lot about NoSQL databases and their benefits. However, there are certain limitations to the NoSQL database, which we have to look upon. Below are the few listed limitations:

NoSQL databases have limited query capabilities as compared to RDBMS.
NoSQL databases don’t offer any RDBMS capabilities like consistency and ACID transactions.
Most of the NoSQL databases use key-value pairs to store the data. Hence it isn’t easy to maintain as the volume increases.
NoSQL databases are new to the markets and can be challenging for RDBMS programmers to switch to these technologies.
Most of the NoSQL databases are open source and are a restricted choice for enterprises.

Conclusion

In this blog post, we have discussed in-depth NoSQL databases, their types, and their advantages and disadvantages. NoSQL databases are getting popular among enterprises due to their exceptional properties.

Integrating and analyzing data from a huge set of diverse sources can be challenging, this is where Hevo comes into the picture. Hevo is a No-code Data Pipeline and has awesome 100+ pre-built integrations that you can choose from. Hevo can help you integrate data from multiple sources and load them into a destination to analyze real-time data with a BI tool. It will make your life easier and make data migration hassle-free. It is user-friendly, reliable, and secure. Check out the pricing details here.

VISIT OUR WEBSITE TO EXPLORE HEVO