Most Popular NoSQL Databases

There are many different NoSQL databases available, each with its own unique set of features and capabilities. Some of the most popular NoSQL databases include:

MongoDB

MongoDB is a popular NoSQL database that is based on the document store model, which means that data is stored in documents that are similar to JSON objects. This allows for the efficient representation and manipulation of complex data structures and relationships.

Cassandra

Cassandra is a NoSQL database that is based on the column store model, which means that data is organized into columns rather than rows. This allows for fast and efficient data retrieval, especially for large datasets.

Redis

Redis is an in-memory data store that is often used as a cache or message broker. It is known for its low latency and high performance, making it a popular choice for real-time applications and in-memory databases.

Couchbase

Couchbase is a document-based NoSQL database that is designed for high-performance and scalability. It is also known for its full-text search capabilities and its ability to handle unstructured data.

AWS DynamoDB

AWS DynamoDB is a fully managed NoSQL database service provided by Amazon Web Services (AWS). It is known for its scalability and performance, and it’s built to handle high-traffic, real-time applications.

Cosmos DB

Cosmos DB is Microsoft’s globally-distributed, multi-model database service. It’s very popular for its scalability, performance and multiple data models support ( Document, Key-value, Graph, Column-family)

Elasticsearch

Elasticsearch is a powerful search engine based on the Lucene library. It is a distributed, JSON-based search and analytics engine designed for handling large amounts of data.

HBase

HBase is also a NoSQL database that is based on the key-value store model, which means that data is organized as a set of keys and values. This allows for fast and efficient data access and retrieval, and is well suited for applications that require real-time data access.

These are some of the most popular NoSQL databases among developers and industry practitioners, and their popularity often reflects the specific needs and use cases that they are best suited for. It’s important to understand your own needs and requirements, and to test and evaluate the different options before choosing a specific database for your application.

Types of NoSQL Database

There are different types of NoSQL databases, including the following:

Document store

Document store NoSQL databases store data in documents that are similar to JSON objects. This allows for the efficient representation and manipulation of complex data structures and relationships. Examples of document store NoSQL databases include MongoDB and CouchDB.

Column store

Column store NoSQL databases store data in columns rather than rows. This allows for fast and efficient data retrieval, especially for large datasets. Examples of column store NoSQL databases include Cassandra and HBase.

Key-value store

Key-value store NoSQL databases store data as a set of keys and values. This allows for fast and efficient data access and retrieval, and is well suited for applications that require real-time data access. Examples of key-value store NoSQL databases include Redis and DynamoDB.

Graph database

Graph database store data as a network of nodes and edges, which allows for the efficient representation and manipulation of complex data relationships. Examples of graph databases include Neo4j and ArangoDB.

Wide-column store

Wide-column store NoSQL databases store data in columns, but the columns can be dynamically added or removed, which allows for more flexibility and scalability compared to traditional column store databases. Examples of wide-column store NoSQL databases include Bigtable and Apache HBase.

Advantages of NoSQL Database

Scalability and flexibility

One of the main advantages of NoSQL databases is that they are highly scalable and flexible, which means that they can easily handle large volumes of data and support a large number of users and applications. This is particularly useful for organizations that experience sudden spikes in traffic or data volume.

Improved performance

NoSQL databases can have improved performance compared to traditional relational databases, especially for large datasets or applications that require real-time data access. This is because NoSQL databases are optimized for fast and efficient data retrieval and manipulation, and are not limited by a fixed schema.

Support for unstructured and semi-structured data

NoSQL databases are well suited for handling unstructured and semi-structured data, which is data that does not have a fixed schema or data that is not easily organized into rows and columns. This is particularly useful for applications that require the integration of different data types and sources, such as multimedia data or data from multiple sources.

Ease of use and development

NoSQL databases are typically easier to use and develop as compared to traditional relational databases, especially for developers who are not familiar with SQL. This can reduce the time and resources required to develop and maintain applications that use a NoSQL database.

Cost savings

NoSQL databases can help organizations to reduce costs, since they do not require the upfront investment in hardware and infrastructure that traditional relational databases do. Additionally, organizations only pay for the resources they use, which can help to control costs and avoid overspending.

Disadvantages of NoSQL Database

Limited support for SQL

One of the main disadvantages of NoSQL databases is that they do not support SQL, which is the standardized and widely used language for managing and querying data in relational databases. This can limit their compatibility with other systems and applications that use SQL, and may require developers to learn and use specialized NoSQL query languages.

Limited support for ACID transactions

NoSQL databases typically do not support ACID (Atomicity, Consistency, Isolation, Durability) transactions, which are a set of properties that guarantee the integrity and consistency of data in a database. This can limit their ability to handle complex data manipulation operations, such as data aggregation or data mining.

Limited vendor support

NoSQL databases are not as widely used as traditional relational databases, which means that they may have limited vendor support and resources compared to other database models. This can make it more difficult to find support and expertise for NoSQL databases.

Potential vendor lock-in

Organizations that use a NoSQL database may be dependent on their database vendor, which can create vendor lock-in and limit their ability to switch to another database in the future.

Compatibility issues

NoSQL databases may not be compatible with other database models, which can limit their interoperability and integration with other systems and applications.

Use Cases of NoSQL Database

NoSQL databases are a popular choice for many modern applications because they offer several benefits over traditional relational databases.

So, when to use a NoSQL database? Here are common use cases for NoSQL databases:

1. Storing and processing large amounts of data, such as in the case of big data applications: NoSQL databases are designed to scale horizontally, which means they can easily handle large volumes of data without sacrificing performance.
2. Storing and managing unstructured data, such as documents, images, and videos: NoSQL databases are typically more flexible than relational databases, which makes them well-suited for handling unstructured data.
3. Building real-time, high-performance applications, such as mobile and web applications: NoSQL databases are generally faster and more efficient than relational databases, which makes them a good choice for applications that require quick response times.
4. Mobile and IoT applications: NoSQL databases are often used to store and process data from mobile and Internet of Things (IoT) devices, due to their ability to handle a high volume of read and write operations in real-time.
5. Enabling rapid development and deployment of applications: Because NoSQL databases are typically more flexible and scalable than relational databases, they can make it easier and faster to develop and deploy modern applications.
6. Supporting cloud-native architectures and applications: NoSQL databases are often used in cloud-based applications because they are designed to be distributed and scalable, which makes them well-suited for the dynamic nature of cloud environments.

Which is the Fastest NoSQL Database?

When it comes to performance, NoSQL databases have some inherent characteristics that make them particularly fast, such as horizontal scaling and distributed architecture. However, the specific performance of a NoSQL database will depend on a variety of factors, including the size and complexity of your data, the number of concurrent users and requests, and the specific implementation and configuration of the database.

With that said, some NoSQL databases are known for their high performance and are commonly used in high-traffic and high-performance applications. Some examples of NoSQL databases include:

• Redis: Redis is an in-memory data store that is often used as a cache or message broker. It is known for its low latency and high performance, making it a popular choice for real-time applications.
• Aerospike: Aerospike is a distributed NoSQL database that is optimized for high performance and low latency. It is designed for high-traffic, real-time applications and it’s known for his high performance and scalability.
• Cassandra: Cassandra is a highly-scalable, distributed NoSQL database that is optimized for read and write performance. It’s been used in high-performance applications that require low-latency data access, and it’s known for it’s linear scalability.
• MongoDB: MongoDB is a document-based NoSQL database that is known for its high performance and scalability. It also provides built-in sharding and automatic balancing of data, making it a good choice for high-traffic, real-time applications.

Keep in mind that the specific performance of a NoSQL database will depend on a variety of factors and it’s always recommended to test and validate the performance of the database under your specific use case, workload and data size.

It’s also worth to note that, performance is not the only factor to take into account when choosing a database, you should also consider the specific requirements of your use case, your expertise with the database and your team skills to manage and maintain the database.

More to Read

• Relational Database Benefits and Limitations
• Relational Vs Non Relational Database
• 13 Examples of Relational Database
• Relational Database Vs. Object-Oriented Database
• 9 Types of Databases
• Distributed Database
• Operational Database
• Personal Database
• Graph Databases
• Centralized Database

What is a Graph Database?

In a graph database, the data is stored as a set of vertices and edges, with each vertex representing an entity (such as a person or a business) and each edge representing a relationship between two vertices (such as a friendship or a business partnership). The graph structure allows for flexible and efficient querying, as it allows for traversing relationships between entities in various ways.

Graph databases are particularly useful for storing and querying data that has complex relationships, such as social networks, recommendation engines, and fraud detection systems. They are also often used in areas such as bioinformatics and supply chain management, where the data has a complex, interconnected structure.

Graph Database Use Cases

There are many use cases for graph databases, as they are particularly well-suited for storing and querying data that has complex relationships. Some common use cases for graph databases include:

Social networks

Graph databases can be used to store and query data about relationships between people, such as friendships, family relationships, and professional connections. This can be used to build social networking platforms, recommendation engines, and other applications.

Fraud detection

Graph databases can be used to identify patterns of fraudulent activity by analyzing the relationships between entities such as individuals, businesses, and transactions.

Recommendation engines

Graph databases can be used to store and query data about users and their interactions with products or content. This can be used to build recommendation engines that suggest products or content to users based on their interests and past behavior.

Supply chain management

Graph databases can be used to store and query data about the relationships between different entities in a supply chain, such as suppliers, manufacturers, and retailers. This can be used to optimize logistics and supply chain management.

Bioinformatics

Graph databases can be used to store and query data about the relationships between different biological entities, such as genes, proteins, and diseases. This can be used to study the relationships between different biological processes and to develop new drugs and treatments.

Graph Database List

Some popular graph databases are:

Neo4j

Neo4j is a widely used open-source graph database that is optimized for storing and querying large amounts of data. It is written in Java and supports ACID transactions, making it suitable for use in enterprise applications.

JanusGraph

JanusGraph is an open-source, distributed graph database that is built on top of Apache Cassandra and Elasticsearch. It is designed to handle large-scale graph data and is optimized for high performance and scalability.

Amazon Neptune

Amazon Neptune is a fully managed graph database service that is optimized for storing and querying graph data. It is designed to be easy to use and is backed by the reliability and security of the Amazon Web Services (AWS) cloud platform.

OrientDB

OrientDB is an open-source, multi-model database that supports graph, document, key-value, and object data models. It is designed to be scalable and efficient, and it supports ACID transactions andSQL-like query language.

ArangoDB

ArangoDB is an open-source, multi-model database that supports graph, document, and key-value data models. It is designed to be flexible and easy to use, and it supports ACID transactions and a powerful query language.

Graph Database Properties

Graph databases have several properties that make them unique and well-suited for storing and querying data that has complex relationships:

Flexible data model

Graph databases use a flexible data model that allows for the representation of complex relationships between entities. This makes it easy to store and query data that has many different types of relationships and connections.

Efficient querying

Graph databases are optimized for efficient querying of data, particularly when it comes to traversing relationships between entities. This makes it easy to find and retrieve data about specific entities and their relationships with other entities.

Scalability

Graph databases are designed to scale well as the size of the data increases. This makes them suitable for storing and querying large amounts of data.

ACID transactions

Many graph databases support ACID (Atomicity, Consistency, Isolation, Durability) transactions, which ensure that data is stored and accessed in a consistent and reliable manner. This is important for applications that require high levels of data integrity and reliability.

High performance

Graph databases are optimized for high performance and can handle a large number of queries and updates in real-time. This makes them suitable for use in high-traffic applications.

Graph Database Vs. Relational Database

Graph databases are based on graph theory and are designed to store and process complex relationships and connections between data. They are particularly well suited for data that is hierarchical, connected, or has complex relationships.

Relational databases are based on the relational model and are designed to store and process structured data. They are particularly well suited for data that can be organized into tables with well-defined relationships between the rows and columns.

There are a few key differences between graph databases and relational databases:

In general, graph databases and relational databases each have their own strengths and weaknesses, and the choice between the two will depend on the specific needs of your application.

More to read

• Relational Database Benefits and Limitations
• Relational Vs Non Relational Database
• 13 Examples of Relational Database
• Relational Database Vs. Object-Oriented Database
• 9 Types of Databases
• Distributed Database
• Operational Database
• Personal Database
• Centralized Database

It covers all technical fields to simplify and lessen the human burden which leads to make the better decisions. There are number of tools and techniques are used in this regards to make advancement in technology and save the time.

Today most of the companies use artificial intelligence to improve their working efficiency and progress. They use different intelligent techniques to make their business predictions.

What is intelligence?

Intelligence is the ability to get the knowledge and apply the same with the proficiency and skill for some result or outcome. The collections of information have the ability to learn quickly to solve the particular problems in recent times.

Read also: Artificial Intelligence Tutorial for Beginners

Purpose of Artificial Intelligence

There are some main purposes and features in which artificial intelligence is used in different fields or zones. These are the main purposes of artificial intelligence.

1. Improves decision making
2. Singularity
3. Machine learning
4. Business process optimization
5. Creative work in technologies
6. Provides financial services
7. Health care
8. Automotive
9. HR & Recruitment

All the above points are briefly discussed here.

1 – Improves decision making

The basic goal of artificial intelligence is to provide mechanism for decision making. This decision making is based on rare data as input data and will provide artificial intelligent result like human mind.

Artificial intelligence has the ability to make better decisions by automating the different physical and other tasks. These tasks can reduce the human labor and also saves the time.

2 – Singularity

The ultimate objective of artificial intelligence is to overtake the work of human being. In near future, the growth of technology will become uncontrollable that will result into massive changes in human life style. Besides their side effects, these intelligent technologies will make the work simpler and efficient.

It is also called technological singularity. You can read in detail about singularity here.

3 – Machine learning

The main difference between machine learning and artificial intelligence is that machine learning is mainly concerned with accuracy. Machine learning is the sub-field of artificial intelligence and it takes data to produce the output as it is more focused.

Machine learning has mainly four types, you can read it here.

4 types of machine learning

4 – Business process optimization

Business has the vital integrity in the economy of any country. The business process optimization is carried out by streamlining the work and removing the redundancies that ultimately results in improvement of the business.

The robotic process optimization is also used to minimize the daily routine work performed by the humans through different algorithms.

5 – Creative work in technologies

There are number of technologies that are used to simplify the workflow and are easy to integrate across the business. These technologies are very important and are playing an important role in different fields of life. Some examples are:

1. Virtual reality
2. Live streaming apps.
3. Predictive Analytics.
4. Drones.
5. Motion animation.

6 – Provide financial services

Artificial intelligence has played a huge role in financial services. It is used in fraud detection, risk management, asset management and insurance besides countless other sub-fields of financial services. There is almost no sub-field left without the use of artificial intelligence applications.

Similar to other fields the AI applications have saved a lot of time, human resources and made the results efficient.

7 – Health care

It the most important sector where the artificial intelligence has revolutionized the sector. A large number of healthcare institutions are using the artificial intelligence machines for better and fast diagnoses the diseases in the patients.

IBM Watson is the one of the known and best healthcare technology which is based on simple questionnaire and responds according to the disease like x-ray reading.

Virtual assistant devices, based on artificial intelligence, are widely used in healthcare sector.

8 – Automotive

Artificial intelligence has a huge impact on automotive industry. You will find it everywhere from car manufacturing industry to driver monitoring and driver recognition. There are artificial intelligence software available for driver monitoring. The software can make seat adjustment, mirror adjustment and even temperature adjustment.

The scenario of automotive industry has complete changed since the indulgence of artificial intelligence in the industry.

9 – HR & Recruitment

Artificial intelligence in HR & recruitment is to boost-up the speed and precision of decision making and make the selection more reliable and accurate.

If the recruiter is not using the artificial intelligence in recruitment process, then the business is losing a lot of resources such as time and money.

So, the automation of workflow for recruitment process and management of the staff artificial intelligence applications play a huge role and saves the resources.

Bottom Line

Artificial intelligence is used vastly in almost every field of the life. We have described only a few uses of artificial intelligence.

From the above discussion, we can easily say that the purpose of artificial intelligence is to provide software intelligent spectrum with local reasoning and interaction with humans to offer decision based supported results. It also provides valuable software and predictive tools for better results.

Being a core component of all modern software still it is not a replacement of humans, however, it saves time, resources and gives us many extra benefits which seems impossible with the routine labor.

More to read

• History of Artificial Intelligence
• How Artificial Intelligence Works
• 4 Types of Artificial Intelligence
• Artificial Intelligence and Robotics
• Artificial Intelligence Vs. Machine Learning
• Artificial Intelligence Vs. Human Intelligence
• Artificial Intelligence Vs. Data Science
• Artificial Intelligence Vs. Computer Science
• What Artificial Intelligence Cannot Do?
• How has Artificial Intelligence Impacted Society?
• What is the purpose of Artificial Intelligence?

Cluster is a group of data objects that are similar to one another within the same cluster, whereas, dissimilar to the objects in the other clusters.

Cluster analysis is a technique used to classify the data objects into relative groups called clusters.

Clustering is an unsupervised learning approach in which there are no predefined classes.

The basic aim of clustering is to group the related entities in a way that the entities within a group are alike to each other but the groups are dissimilar from each other.

In K-Means clustering, “K” defines the number of clusters. K-means Clustering, Hierarchical Clustering, and Density Based Spatial Clustering are more popular clustering algorithms.

Examples of Clustering Applications:

• Cluster analyses are used in marketing for the segmentation of customers based on the benefits obtained from the purchase of the merchandise and find out homogenous groups of the consumers.
• Cluster analyses are used for earthquake studies.
• Cluster analyses are used for city planning in order to find out the collection of houses according to their house type, worth and geographical locality.

Major Clustering Approaches:

Major clustering approaches are described as under: –

Partitioning Clustering

In this technique, datasets are subdivided into a set of k-groups (where k is the no. of groups, which is predefined by the analyst).

K-means is the well-known clustering technique in which each cluster is represented by the center of the data points belonging to the cluster.

K-medoids clustering is an alternative technique of K-means, which is less sensitive to outliers as compare to k-means.

K-means clustering method is also known as hard clustering as it produces partitions in which each observation belongs to only one cluster. 

Hierarchy Clustering

Hierarchy Clustering is used to identify the groups in the dataset but the analyst does not require to pre-specify the number of clusters to be generated.

The result obtained from this clustering is tree-based representation of the objects, which is recognized as a dendrogram. Furthermore, observations can also sub-divided into groups by slicing the dendrogram at the desired resemblance level.

Fuzzy Clustering

Fuzzy clustering is also known as soft clustering which permits one piece of data to belong to more than one cluster.

Fuzzy clustering is frequently used in pattern recognition. Fuzzy C-means clustering algorithm is commonly used worldwide.  

Density-based Clustering (DBSCAN)

DBSCAN stands for Density-based spatial clustering of applications with noise. It is a method that has been introduced by Ester et al. in 1996 that can be utilized to find out the clusters of any shape in a dataset having noise and outliers.

The main advantage of DBSCAN is that there is no need to specify the number of clusters to be generated by the user.

Grid-based Clustering

This clustering approach utilizes a multi-resolution grid data structure having high processing speed with a small amount of memory consumption.

Model-based Clustering:

In this clustering approach, it is assumed that the data is coming from a dispersal that is a combination of two or more clusters.

Model based clustering is utilized to resolve the issues that can arise in K-means or Fuzzy K-means algorithms.

Difference between Classification and Clustering

Process of applying K-mean Clustering

• Choose the number of clusters
• Specify the cluster seeds
• Assign each point to a centroid
• Adjust the centroid

Pros and Cons of Clustering

K-means

• Pros: It is simple to comprehend, work better on small as well as large datasets. This clustering technique is fast and efficient.
• Cons: There is a dire need to select the number of clusters

Hierarchical Clustering

• Pros: The ideal number of clusters can be acquired by the model itself.
• Cons: Hierarchical clustering is not suitable for large datasets.

K-Means Clustering Example (Python)

These are the steps to perform the example.

Import the relevant libraries.

Load the data

Now we load the data in .csv format in the same folder where clustering.ipynb file saved and also check the data what is inside the file. Look at this figure.

In order to map the data, we will create a new variable data_mapped which is equal to data.copy() and data_mapped[‘continent’] equal to data_mapped[continent].map and also Africa to 0, Asia to 1, Europe to 2, North America to 3 and South America to 4 as shown in this figure.

Further, we will select the features that we intend to utilize for clustering as below

In the above picture, we select three columns and left only one column i.e. country.

Perform K-Mean Clustering

In above span, we perform K-mean clustering with 5 clusters and the results shown in below figure.

Now we create a data frame i.e. data_with_clusters which is equal to data. Furthermore, we add an extra column i.e. Cluster which is equal to identified_clusters, as shown in figure

It is clear from the above picture that Angola, Burundi & Benin in cluster 0, Aruba, Anguilla, Antigua & Barb in cluster 1, Albania, Aland, Andorra, Austria & Belgium in cluster 2 and Afghanistan, United Arab Emirates & Azerbaijan in cluster 3.

Finally, we are going to plot a scatter plot in order to obtain a map of the real world. We will take the Longitude along the y-axis and Latitude along the x-axis.

These clusters are based on geographical location, therefore, the result is shown in this figure.