Trending March 2024 # Hbase Architecture: Use Cases, Components & Data Model # Suggested April 2024 # Top 12 Popular

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HBase Architecture and its Important Components

Below is a detailed architrecutre of HBase with components:

HBase Architecture Diagram

HBase architecture consists mainly of four components

HMaster

HRegionserver

HRegions

Zookeeper

HDFS

In this tutorial- you will learn,

HMaster

The following are important roles performed by HMaster in HBase.

Plays a vital role in terms of performance and maintaining nodes in the cluster.

HMaster provides admin performance and distributes services to different region servers.

HMaster assigns regions to region servers.

HMaster has the features like controlling load balancing and failover to handle the load over nodes present in the cluster.

When a client wants to change any schema and to change any Metadata operations, HMaster takes responsibility for these operations.

Some of the methods exposed by HMaster Interface are primarily Metadata oriented methods.

Table (createTable, removeTable, enable, disable)

ColumnFamily (add Column, modify Column)

Region (move, assign)

The client communicates in a bi-directional way with both HMaster and ZooKeeper. For read and write operations, it directly contacts with HRegion servers. HMaster assigns regions to region servers and in turn, check the health status of region servers.

In entire architecture, we have multiple region servers. Hlog present in region servers which are going to store all the log files.

HBase Region Servers

When HBase Region Server receives writes and read requests from the client, it assigns the request to a specific region, where the actual column family resides. However, the client can directly contact with HRegion servers, there is no need of HMaster mandatory permission to the client regarding communication with HRegion servers. The client requires HMaster help when operations related to metadata and schema changes are required.

HRegionServer is the Region Server implementation. It is responsible for serving and managing regions or data that is present in a distributed cluster. The region servers run on Data Nodes present in the Hadoop cluster.

HMaster can get into contact with multiple HRegion servers and performs the following functions.

Hosting and managing regions

Splitting regions automatically

Handling read and writes requests

Communicating with the client directly

HBase Regions

HRegions are the basic building elements of HBase cluster that consists of the distribution of tables and are comprised of Column families. It contains multiple stores, one for each column family. It consists of mainly two components, which are Memstore and Hfile.

ZooKeeper

HBase Zookeeper is a centralized monitoring server which maintains configuration information and provides distributed synchronization. Distributed synchronization is to access the distributed applications running across the cluster with the responsibility of providing coordination services between nodes. If the client wants to communicate with regions, the server’s client has to approach ZooKeeper first.

It is an open source project, and it provides so many important services.

Services provided by ZooKeeper

Maintains Configuration information

Provides distributed synchronization

Client Communication establishment with region servers

Provides ephemeral nodes for which represent different region servers

Master servers usability of ephemeral nodes for discovering available servers in the cluster

To track server failure and network partitions

Master and HBase slave nodes ( region servers) registered themselves with ZooKeeper. The client needs access to ZK(zookeeper) quorum configuration to connect with master and region servers.

During a failure of nodes that present in HBase cluster, ZKquoram will trigger error messages, and it starts to repair the failed nodes.

HDFS

HDFS is a Hadoop distributed File System, as the name implies it provides a distributed environment for the storage and it is a file system designed in a way to run on commodity hardware. It stores each file in multiple blocks and to maintain fault tolerance, the blocks are replicated across a Hadoop cluster.

HDFS provides a high degree of fault –tolerance and runs on cheap commodity hardware. By adding nodes to the cluster and performing processing & storing by using the cheap commodity hardware, it will give the client better results as compared to the existing one.

In here, the data stored in each block replicates into 3 nodes any in a case when any node goes down there will be no loss of data, it will have a proper backup recovery mechanism.

HDFS get in contact with the HBase components and stores a large amount of data in a distributed manner.

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Top 15 Components Of 8085 Architecture

Introduction to 8085 Architecture

Eighty-eighty-five is an 8-bit microprocessor created in 1971 by Intel. It requires less circuit and makes the computer system to be simpler and easy to be built. It uses 5-volt power supply and has depletion mode transistors. Hence, 8085 could be compared with 8080 derived CPU. Hence these can be used in the systems with CP/M operating system. It has a DIP package with 40 pins. There is a data bus in the processor to fully utilize the functions of pins. There is built in serial I/O and 5 interrupts so that 8085 has long life similar to the controller used.

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Architecture of 8085:

Components of 8085 Architecture

1. It consists of timing and control unit, accumulator, arithmetic and logic unit, general purpose register, program counter, stack pointer, temporary register, flag register, instruction register and decoder, controls, address buffer and address and data bus.

2. The timing and control unit provides proper signals to the microprocessor to perform functions in the system. We have control signals, status signals, DMA signals and RESET signals. This also controls the internal and external circuits in the system.

3. The accumulator is a register that performs all the arithmetic and logic operations in 8-bit processor. It connects the data bus and ALU unit of the processor.

4. ALU performs all the operations that includes arithmetic and logic operations such as addition, subtraction, multiplication, division, and the logical operations in the system.

5. General purpose registers used in the processor include B, C, D, E, H and L registers. Each register holds the data and also these could be made to work in pairs. Hence these can hold 16-bit data in the processor.

7. Stack pointer works like a stack with a 16-bit register. It performs push or pop operations and is either incremented or decremented by 2 in the register.

8. Temporary data of the operations in ALU is handled in a temporary register which is 8-bit.

9. The 8-bit register with 1-bit flip-flops is called Flag register. There are 5 flip-flops and it holds logic data from the accumulator register. The logic data can be 0 or 1. The five flip-flops are Sign, Zero, Auxiliary Carry, Parity and Carry.

10. Instruction register and decoder is also an 8-bit register where instructions are stored after taking it from the memory. Decoder encrypts the instruction stored in the register.

11. The signal is given to the microprocessor through the timing and control unit to do the operations. There are different time and control signals to perform the operations. They are control signals, status signals, DMA signals, and RESET signals.

13. Serial data communication is controlled using serial input data and serial output data.

14. Stack pointer and program counter load the data into the address buffer and data buffer so that it communicates with the CPU. The chips are connected here and CPU transfers data via these chips to the data buses.

15. Data to be stored is saved in the data bus and it transfers data to different address services.

Features of 8085 Architecture

Any 8-bit data could be processed, accepted, or provided in the microprocessor simultaneously. The power supply is a single 5-volt supply and operates on a 50% duty cycle.

The clock generator in the processor is internal that needs a tuned circuit, either LC or RC or crystal. The frequency is divided by 2 so that the clock signal is generated to synchronize external devices of the system.

3 MHz frequency can be used to operate the processor. The maximum frequency in which 8085 operates is 5 MHz

16 address lines are provided in the processor so that it can access 64 Kbytes of memory in the system. Also, 8 bit I/O addresses are provided to access 256 I/O ports.

The address bus and data bus in the processor is multiplexed so that the number of external pins can be reduced. External hardware is needed to separate the address and data lines in the processor. 74 instructions are supported in the processor with different address modes. The address modes are immediate, register, direct, indirect, and implied modes.

Advantages

The general-purpose electronic processing devices in the system to execute various tasks are called the microprocessors. All the logic and arithmetic operations are performed here and the results are stored in the registers. This helps the CPU to fetch information whenever needed in the system.

Data could be fetched and moved easily to various locations with the help of registers in the microprocessor.

Operands are delivered easily from the microprocessor and this is easy to do than to restore the operands from the memory. Program variables are stored easily in the registers and hence developers prefer to work with processors in the system.

Serial communication is provided with serial control and hardware interrupts are available to deliver urgent requests. It handles the interruptions in a skilled manner so that the process is kept on hold until the urgent requests are fulfilled. Control signals are available so that bus cycles are controlled. This rule out the chance of an external bus controller.

The system bus is shared with Direct Memory Access to transfer huge data from device to memory or vice versa.

Trainer kits are provided in the institutions to learn about microprocessors so that complete documentation is provided to the students regarding the microprocessors. Also, simulators are available for the execution of the codes in the graphical interface. Assembly language programming is added in the microprocessor course so that it helps the students.

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Top 10 Components Of Iot Architecture In 2023

In this article, we aim to examine the concept of IoT architecture, explain the difference between IoT ecosystem and IoT architecture, demonstrate its ten different components, and finally provide a real-life example for contextualization.

What is IoT architecture?

IoT architecture comprises several IoT building blocks connected to ensure that sensor-generated data is collected, transferred, stored, and processed in order for the actuators to perform their designated tasks.

What is the difference between IoT ecosystem and IoT architecture?

IoT ecosystem is the encompassing term attributed to the five general components of devices, communication protocols, the cloud, monitoring, and the end-user in the IoT system.

IoT architecture is the breakdown of the inner workings of these building blocks to make the ecosystem function.

What are the different elements of IoT architecture?

For the sake of brevity, we will only explore the ten most important parts of an IoT architecture.

1- Devices

IoT devices are equipped with sensors that gather the data, which will be transferred over a network. The sensors do not necessarily need to be physically attached to the equipment. In some instances, they are remotely positioned to gather data about the closest environment to the IoT device. Some examples of IoT devices include:

Temperature detectors

Smoke detectors

Cameras and CCTVs

2- Actuators

Actuators are devices that produce motions with the aim of carrying out preprogrammed tasks, for example:

Smart lights turning on or off

Smart locks opening or closing

Thermostat increasing or decreasing the temperature

3- Gateways 4- Cloud gateways 5- Data lake

A data lake is a data storage space that stores all sorts of structured and non-structured data such as images, videos, and audio, generated by IoT devices, which will then be filtered and cleaned to be sent to a data warehouse for further use.

6- Data warehouse

For meaningful insight, data should be extracted from the data lake to the data warehouse, either manually, or by using data warehouse automation tools. A data warehouse contains cleaned, filtered, and mostly structured information, which is all destined for further use.

7- Data analytics

Data analytics is the practice of finding trends and patterns within a data warehouse in order to gain actionable insights and make data-driven decisions about business processes. After having been laid out and visualized, data and IoT analytics tools help identify inefficiencies and work out ways to improve the IoT ecosystem.

8- Control applications

Previously, we mentioned how actuators make “actions” happen. Control applications are a medium which, through them, it’s possible to send out the relevant commands and alerts which will make actuators function. An example of a control application could be soil sensors signaling a dryness in the lawns, and consequently, the actuators turning on the sprinkles to start irrigation.

9- User applications

They are software components (e.g. smartphone apps) of an IoT system that allow users to control the functioning of the IoT network. User applications allow the user to send commands, turn the device on or off, or access other features.

10- Machine learning

Machine learning, if available, gives the opportunity to create more precise and efficient models for control applications. ML models pick up on patterns in order to predict future outcomes, processes, and behavior by making use of historical data that’s accumulated in the data warehouse. Once the applicability and efficiency of the new models are tested and approved by data analysts, new models are adopted.

What is a real-life example IoT architecture?

The sensors take relevant data, such as daylight or people’s movement. The lamps on the other end, are equipped with actuators to switch the light on and off. The data lake stores these raw data coming from the sensors, while a data warehouse houses the inhabitants’ behavior on various days of the week, energy costs, and more. All these data, through field and cloud gateways, are transferred to computing databases (on-premise or cloud).

The users have access to the user application through an app. The app allows them to see which lights are on and off, or it gives them the ability to pass on commands to the control applications. If there is a gap in algorithms, such as when the system mistakenly switches off the lights and the user has to switch it on manually, data analytics can help address these problems at its core.

When daylights get lower than the established threshold, it’s the control applications commanding the actuators to turn the lights on. At other times, if the lights are on power-saving mode and would only be turned on if a user walks past the lawn, it’s the cloud that receives the data of a passerby walking and after identification, alerts the actuators to turn the lights on. This makes sure that false alarms are detected and the power is conserved.

But the control application does not only function with already-established commands. By leveraging machine learning, algorithms would learn more about usage patterns and customize the functionality accordingly. For example, if the inhabitants leave home at 7 am and come back at 5 pm, after some time, the lights would turn off and on in between this interval autonomously. These smart adjustments would, furthermore, reduce the need for human intervention and make for seamless continuity.

For more on the internet of things

To learn more about the technical side of internet of things, read:

Finally, If you believe your business will benefit from an IoT solution, feel free to check our data-driven hub of IoT solutions and tools.

And we can guide you through the process:

He primarily writes about RPA and process automation, MSPs, Ordinal Inscriptions, IoT, and to jazz it up a bit, sometimes FinTech.

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Apache Kafka Use Cases And Installation Guide

This article was published as a part of the Data Science Blogathon.

Introduction

Today, we expect web applications to respond to user queries quickly, if not immediately. As applications cover more aspects of our daily lives, it is difficult to provide users with a quick response.

Source: kafka.apache.org

Caching is used to solve a wide variety of these problems, but applications require real-time data in many situations. In addition, we have data to be aggregated, enriched, or otherwise transformed for further consumption or further processing. In these cases, Kafka is helpful.

What is Apache Kafka?

It is an open-source platform that ingests and processes streaming data in real time. Streaming data is generated simultaneously by thousands of data sources every second. Apache Kafka uses a Subscribe and Publish model for reading and writing streams of records. Unlike other messaging systems, Kafka has built-in sharding, replication, higher throughput, and is more fault-tolerant, making it an ideal solution for processing large volumes of messages. More about Kafka integration

What is Apache Kafka Used For?

Kafka Use Cases are numerous and found in various industries such as financial services, manufacturing, retail, gaming, transportation and logistics, telecommunications, pharmaceuticals, life sciences, healthcare, automotive, insurance, and more. Kafka is used wherever large-scale streaming data is processed and used for reporting. Kafka use cases include event streaming, data integration and processing, business application development, and microservices. Kafka can be used in the cloud, multi-cloud and hybrid deployments. 6 Reasons to Automate Your Data Pipeline

Kafka Use Case 1: Tracking web activity

Kafka Use Case 2: Operational Metrics

Kafka can report operational metrics when used in operational data feeds. It also collects data from distributed applications to enable alerts and reports for operational metrics by creating centralized data sources of operations data.

Kafka Use Case 3: Aggregating Logs

Kafka collects logs from different services and makes them available in a standard format to multiple consumers. Kafka supports low-latency processing and multiple data sources, which is great for distributed data consumption.

Use Case 4: Stream Processing

How Does Apache Kafka Work?

It is known as an event streaming platform because you can:

• Publish, i.e., write event streams, and Subscribe, i.e., read event streams, including continuous import and export of data from other applications.

• Reliably store event streams for as long as you want.

• Processing of events stream when they occur or after.

Kafka is highly scalable, elastic, secure, and has a data-distributed publish-subscribe messaging system. The distributed system has servers and clients that work through the TCP network protocol. This TCP (Transmission Control Protocol) helps in transferring data packets from source to destination between processes, applications, and servers. A protocol establishes a connection before communication between two computing systems on a network occurs. Kafka can be deployed on virtual machines, bare hardware, on-premise containers, and in cloud environments.

Kafka’s Architecture – The 1000 Foot View

• Brokers (nodes or servers ) handle client requests for production, consumption, and metadata and enable data replication in clusters. Their several Brokers that can be more than one in a cluster.

• Zookeeper maintains cluster state, topic configuration, leader election, ACLs, broker lists, etc.

• Producer is an application that creates and delivers records to the broker.

• A consumer is a system that consumes records from a broker.

Kafka producers and consumers

Kafka Producers are essentially client applications that publish or write events to Kafka, while Kafka Consumers are systems that receive, read, and process those events. Kafka Producers and Kafka Consumers are completely separate, and they have no dependency. It is one important reason why Apache Kafka is so highly scalable. The ability to process events exactly once is one of Kafka’s guarantees.

Kafka’s themes

Kafka Records

Entries are event information that is stored in a topic as a record. Applications can connect and transfer the record to the topic. The data is durable and can be stored in the topic long until the specified retention period expires. Records can consist of different types of information – information about a web event such as a purchase transaction, social media feedback, or some data from a sensor-driven device. It can be an event that signals another event. These topic records can be processed and reprocessed by applications that connect to the Kafka system. Records can be described as byte arrays that store objects in any format. An individual record will have two mandatory attributes – key and value and two optional attributes – timestamp and header.

Apache Zookeeper and Kafka

Apache Zookeeper is software that monitors and maintains order in the Kafka system and acts as a centralized, distributed coordination service for the Kafka Cluster. It manages configuration and naming data and is responsible for synchronization across all distributed systems. Apache Zookeeper monitors Kafka cluster node states, Kafka messages, partitions, and topics, among other things. Apache Zookeeper allows multiple clients to read and write simultaneously, issue updates, and act as a shared registry in the system. Apache ZooKeeper is an integral part of distributed application development. It is used by HBase, Apache Hadoop, and other platforms for functions such as node coordination, leader election, configuration management, etc.

Use cases of Apache Zookeeper

Apache Zookeeper coordinates the Kafka Cluster. it needs Zookeeper to be installed before it can be used in production. This is necessary even if the system consists of a single broker, topic, and partition. Zookeeper has five use cases – administrator selection, cluster membership, topic configuration, access control lists (ACLs), and quota tracking. Here are some Apache Zookeeper use cases:

Apache Zookeeper chooses Kafka Controller.

A Kafka Controller is a broker or server that maintains a leader/follower relationship between partitions. Each Kafka cluster has only one driver. In the event of a node shutdown, the controller’s job is to ensure that other replicas take over as partition leaders to replace the partition leaders on the node being shut down.

Apache Zookeeper manages the topic configuration.

Zookeeper software keeps records of all topic configurations, including the list of topics, the number of topic partitions for each topic, overriding topic configurations, preferred leader nodes, and replica locations, among others.

The zookeeper Software maintains access control lists or ACLs

The Zookeeper software also maintains ACLs (Access Control Lists) for all topics. Details such as read/write permissions for each topic, list of consumer groups, group members, and the last offset each consumer group got from the partition are all available.

Installing Kafka – Several Steps Installing Apache Kafka on Windows OS

Prerequisites: Java must be installed before starting to install Kafka.

Installation – required files

To install Kafka, you will need to download the following files:

Install Apache ZooKeeper

A. Download and extract Apache ZooKeeper from the above link.

b. Go to the ZooKeeper configuration directory, and change the dataDir Path from “dataDir=/tmp/zookeeper” to “:zookeeper-3.6.3data” in the zoo_sample.cfg file. Please note that the name of the Zookeeper folder may vary depending on the downloaded version.

C. Set system environment variables, add new “ZOOKEEPER_HOME = C:zookeeper-3.6.3”.

d. Edit the system variable named Path and add;%ZOOKEEPER_HOME%bin;

E. Run – “zkserver” from cmd. Now ZooKeeper is up and running on the default port 2181, which can be changed in the zoo_sample.cfg file.

Install Kafka

A. Download and extract Apache Kafka from the above link.

b. In the Kafka configuration directory. replace the chúng tôi path from “log.dirs=/tmp/kafka-logs” to “C:kafka-3.0.0kafka-logs” in server.properties. Please note that the name of the Kafka folder may vary depending on the downloaded version.

C. In case ZooKeeper is running on a different computer, edit these server.properties __ Here, we will define the private IP of the server

listeners = PLAINTEXT://172.31.33.3:9092## Here, we need to define the public IP address of the server}

d. Add the below properties to server.properties

E. Kafka runs as default on port 9092, and it connects to ZooKeeper’s default port which is 2181.

Running Kafka server.

A. From Kafka installation directory C:kafka-3.0.0binwindows, open cmd and run the below command.

b. The Kafka Server is up and running, and it’s time to create new Kafka Topics to store messages.

Create Kafka Topics

A. Create a new topic as my_new_topic.

b. From C:kafka-3.0.0binwindows, open cmd and run the command below:

Commands For Installing Kafka

Kafka Connectors

How can Kafka be connected to external systems? Kafka Connect is a framework that connects databases, search indexes, file systems, and key-value stores to Kafka using ready-to-use components called Kafka Connectors. Kafka connectors deliver data from external systems to Kafka topics and from Kafka topics to external systems.

Kafka resource connectors

The Kafka Source Connector aggregates data from source systems such as databases, streams, or message brokers. The source connector can also collect metrics from application servers into Kafka topics for near-real-time stream processing.

Kafka sink connectors

The Kafka Sink Connector exports data from Kafka topics to other systems. These can be popular databases like Oracle, SQL Server, SAP or indexes like Elasticsearch, batch systems like Hadoop, cloud platforms like Snowflake, Amazon S3, Redshift, Azure Synapse, and ADLS Gen2, etc.

Conclusion

Kafka has numerous and found in various industries such as financial services, manufacturing, retail, gaming, transportation and logistics, telecommunications, pharmaceuticals, life sciences, healthcare, automotive, insurance, and more. Kafka is used wherever large-scale streaming data is processed and used for reporting.

It is an open-source platform that ingests and processes streaming data in real time. Streaming data is generated simultaneously by thousands of data sources every second.

The Kafka Source Connector aggregates data from source systems such as databases, streams, or message brokers. The source connector can also collect metrics from application servers into Kafka topics for near-real-time stream processing.

Zookeeper software keeps records of all topic configurations, including the list of topics, the number of topic partitions for each topic, overriding topic configurations, preferred leader nodes, and replica locations, among others.

The media shown in this article is not owned by Analytics Vidhya and is used at the Author’s discretion.

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Openai Whisper: Pricing, Features And Use Cases

A general purpose multilingual speech recognition system that lets users transcribe or translate audio files.

About Open AI Whisper

Whisper AI is an Open AI product that automatically recognizes speech and transcribes it. The tool is trained with a robust dataset of 680,000 hours of multilingual and multitask data from the web. It is trained using natural language and deep learning to interpret speeches in multiple languages. You can use Open AI Whisper to transcribe existing audio files, but it cannot record audio.

Whisper AI transcribes English and non-English audio with a high-level of accuracy. The tool also translates audio files into other languages. Whisper AI is trained with a large and diverse dataset and doesn’t focus specifically on a single language. It offers a zero-shot performance that makes 50% fewer errors compared to existing automatic speech recognition models.

Open AI Whisper Features

OpenAI Whisper is a powerful speech recognition tool. It offers several features to automate speech recognition and transcription. Some of its useful features include the following:

Whisper AI can translate and understand 100 languages.

It can identify the language of an audio file.

It offers API for developers to integrate Whisper AI features into other software.

Whisper AI offers offline access to users.

It can recognize speech in various accents despite background noise.

Open AI Whisper Use Case – Real-World Applications

Open AI Whisper can be used in every industry seeking speech recognition or translation services. Some real-life applications of this AI tool are as follows:

Translators can use Whisper AI to translate speech into other languages.

Transcribers can use Whisper AI to convert audio files into text.

Developers can use the API to create other powerful apps with Whisper AI functionality.

Open AI Whisper Pricing

Open AI Whisper is a free, open source model. You can access it using your Open AI credentials without paying a single penny. But the tool charges for API usage. Its API starts at $0.006 per 1000 tokens. It offers flexible pricing options, allowing users to pay as they use the credits.

FAQs

Does Open AI own Whisper AI?

Whisper AI is a product of Open AI. The tool was launched in 2023 for automatic speech recognition. However, it is still under development, so you may encounter frequent new updates while using the tool.

Which languages does Whisper AI support?

Whisper AI supports more than 100 languages. You can use it in English, and non-English languages like Telugu, Korean, Chinese, Russian, Romanian, Hungarian, Tamil, French, Portuguese, Italian, Japanese, German, Greek, etc.

Do I need to create a Whisper AI account?

To access Whisper AI, you need to use your Open AI account. If you don’t have an Open AI account, create one using the sign up button. After signing in, you can start using Whisper AI to recognize speeches.

Does Whisper AI record audio?

No, Whisper AI doesn’t record audio files. It only transcribes or translates existing audio files. You cannot record calls or other speech using Whisper AI for language identification or speech recognition purposes.

Which file formats are supported on Whisper AI?

Whisper AI supports audio files in m4a, mp3, webm, mp4, mpga, wav, and mpeg. The maximum file size supported is 250 MB.

Whisper AI can be used for speech recognition in multiple languages. The tool has a robust dataset trained with thousands of hours of speech. You can use it to transcribe audio files, identify languages, or translate speech.

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Top 10 Nft Use Cases In 2023

NFTs, which are digital assets represented by cryptographic tokens, are becoming more popular because of their numerous use cases. That’s represented by the global revenue rise in the NFT (non-fungible token) market is expected to be $1.6B by the end of 2023. That’s almost twice what it was in 2023. 

People might think of NFTs as digital images of the Bored Ape Yacht Club, fetching millions of dollars. But NFTs have grown to be value-adding assets to society in different industries by providing additional revenue streams and cost-saving mechanisms. 

In this article, we discuss the top 10 use cases of NFTs in 2023 in: 

Supply chain management 

Gaming 

Fashion

Finance, and more.

1. Music

NFTs can significantly change the music industry. Artists can leverage NFTs to:

Tokenize their songs and albums

Sell digital merchandise to create additional sources of income (limited digital assets)

Provide royalties to creators/artists/producers 

Combine them with additional physical benefits to encourage their fans to engage with their songs.

For example, Nas, a prominent American rapper, created

Figure 1: Nas’ 3-tier song tokens. Source: Royal

Gamma.io has an NFT marketplace based on the Stacks programming layer, which settles transactions onto the Bitcoin blockchain. It enables the trading and minting of various types of NFTs, such as music, digital collectibles, and different forms of art through the Bitcoin ecosystem.

2. Fashion

Fashion NFTs can be collected and worn as digital garments during occasions in virtual worlds, such as when visiting a friend, attending a party, or participating in a meeting. Fashion brands have already started experimenting with fashion NFTs. For instance:

Gucci sold a digital-only bag on Roblox (a metaverse space) for $4,115

Figure 2: Buyers of Gucci’s bag would be wearing this on the metaverse. Source: Inveres

Dolce & Gabbana creatively combined

3. Gaming 

NFT-based play-to-earn games have gained attraction over the last year. For instance, Axie Infinity, the top NFT gaming market leader, now has more than 2M active daily players. The NFT gaming market is growing at an exponential rate. If the current trend continues, the revenue generated is expected to reach $15B by 2027, up from the current $4B (Figure 3).

Figure 3: In-game NFT revenue is expected to be rising until 2027. Source: S&P

These NFTs usually concern in-game items such as avatars, skins, and weapons that can  be created on the blockchain for game-altering or cosmetic upgrades to the players. In-game NFTs are sold on various platforms, like NFT marketplaces, game-specific marketplaces, and occasionally through the game themselves. 

Some traditional gaming companies; however, have largely reacted negatively towards incorporating NFTs into their business model. For example, Steam, the largest PC game store,  has banned the use of NFTs and cryptocurrencies, citing instances of fraud and high volatility.7  Or Microsoft Gaming CEO voiced concerns in November 2023 about speculation and exploitation in the NFT market.

But not every major company has been skeptical of NFTs. Ubisoft, one of the biggest game developers, briefly launched its  NFT project Ghost Recon Wildlands, on Tezos. The NFTs represented cosmetic upgrades that gamers could use (Figure 4). The project was short-lived, after making only $400 and being scrapped. But it indicated that shifting dynamics of established gaming market players in embracing digital tokens.

Figure 4: Examples of the upgrades that players could purchase on Ghost Recon Wildlands. Image source: Forbes

4. Luxury goods

NFTs can act as a proof of authenticity for luxury goods by containing all relevant chúng tôi instance, counterfeits are common in the wine industry. Some wineries have started releasing NFTs to store the wines’ data, such as its date of harvest and bottling, grapes’ origin, authenticity certificate, etc. For instance, Château Angélus sold an NFT for $110,000, giving the owner one barrel of wine (equivalent to 30 bottles) and digital artwork (Figure 5).

Figure 5: A picture of a bell that would accompany the barrel of wine purchased. Source: OpenSea

5. Metaverse 

Metaverse is expected to impact almost every industry. And its market value is expected to reach a trillion dollars in the future. Metaverse is a combination of immersive virtual worlds where the user can replicate their “real” life actions and circumstances – like buying a house or wearing clothes – on there (Figure 6).

Figure 6: Screenshot of an avatar in what appears to be a habitable ice cave on the metaverse. Image source: NYT

Even though metaverse is still new, it’s soaring in valuation. In January of 2023, the real estate valuation in metaverse – calculated by summing up the land market cap of top platforms like Decentraland and NFT Worlds – was $1.4B. Moreover, the virtual real estate market is predicted to grow more than 31%CAGR by 2028. Virtual lands are bought and sold through NFTs. When someone buys a virtual land, the NFT representing the ownership of that parcel is transferred to the buyer’s wallet.

The largest metaverse land purchase was made in the Sandbox, one of the virtual worlds, for $4.3 million. Large companies from different industries, such as Gucci, PwC, Samsung, and JP Morgan have purchased lands on the metaverse. And interestingly, some real estate companies are incentivizing real estate sales in the physical world by promising digital twins on the metaverse. For example, the following is the digital replica of a seven-bedroom house put up for sale in Miami.

6. Supply chain

NFTs can be used to improve supply chain operations by storing metadata of products into the blockchain. In terms of security, blockchain technology in supply chain management prevents data elimination and manipulation. Logistics-wise, a use case of NFT is supply chain management is end-to-end tracking of goods from origin to destination. For example, Koinearth is a startup that creates enterprise NFTs that enable the tracking of physical goods and documents across the supply chain.

7. Ticket sales 

Popular event tickets tend to sell out  fast. And the rise of ticket bots is worsening the situation, as almost 40% of ticketing traffic is coming from ticket bots. And once tickets are sold out, they are then resold on secondary markets for profit. 17

This: 

Robs organizers of additional revenues. While the initial sale still generates income, organizers miss out on potential additional sales from merchandise, food, and beverages that would have been purchased by genuine attendees. Can lead to fraudulent activities. It is estimated that 12% of concert ticket buyers have been scammed (e.g. sold fake tickets).

Stops legitimate, interested customers from getting tickets.

NFT transactions on a blockchain will be on a public ledger, making secondary sales trackable and enabling rules-based validations before purchases. For example, eligible purchasers may be required to purchase a specific type of NFT or hold such an NFT. 

Therefore, NFTs can improve the ticket industry by: 

Removing the 3rd party ticket seller between the artists and fans.

Reducing scams due to transparency and authenticity verification provided by NFTs.

Non-transferable NFTs can prevent ticket reselling.

Due to the public nature of the blockchain and historical record-keeping, platforms can use smart contracts to establish rules to prevent bots and scammers by checking their transaction history.

A royalties system can be put in place that pays the artist, producer, or any party a percentage of subsequent sales of the ticket.

8. Asset tokens

NFTs can be used to tokenize physical assets, such as real estate, art work, and collectibles, creating a bridge between the physical and digital worlds. This process involves converting a tangible asset into a digital token on a blockchain, which represents the ownership, provenance, and other information related to the asset. 

For instance, the sporting goods giant Adidas sold 30,000 NFTs in 2023 which entitled the holders to physical goods, such as hoodies and tracksuits.

Benefit of asset tokenization include:

Fractional Ownership: NFTs can be used to break down a physical asset into smaller, more affordable fractions, allowing multiple individuals to own a portion of the asset. This increases liquidity, as it enables a larger number of people to invest in and trade these fractional shares.

Provenance and Authenticity: By tokenizing a physical asset, its ownership history, provenance, and authenticity can be securely stored on the blockchain. This can help combat fraud, counterfeiting, and theft, and also create trust among buyers and sellers in the secondary market.

Easier Transfer of Ownership: The tokenization of physical assets allows for the transfer of ownership to be executed digitally and efficiently, without the need for physical paperwork. NFTs can be easily bought, sold, or traded on digital platforms, reducing transaction times and costs.

Market Creation: The tokenization of physical assets can create new digital marketplaces where these assets can be bought, sold, or traded. This can lead to increased price discovery, market efficiency, and liquidity for these assets.

Cross-border Transactions: NFTs can facilitate cross-border transactions and investment in physical assets, as they are not bound by geographic limitations or borders. This can create a more inclusive and global market for tangible assets.

Digital Asset Management: Tokenizing physical assets allows for the creation of a digital record of the asset’s details, maintenance history, and other important information. This can simplify asset management processes and provide greater transparency to all parties involved.

Interoperability: By tokenizing physical assets on a blockchain, they can become more easily integrated with other digital assets and systems, allowing for new and innovative use cases.

9. Identity and credentials

NFTs can represent digital identities, certifications, or credentials, which can be easily verified and shared. This can be particularly useful in education, employment, or government services for validating skills, qualifications, or personal information.

10. Loans and financial instruments

Holders of blue chip NFTs, like valuable collections, real estate, or digital art pieces, can put them down as collateral and get loans and other available financial instruments. Once the collateral is approved and loan terms are agreed, the loan amount is transferred into the borrower’s digital wallet via smart contract execution. 

The borrower repays the loan according to the agreed-upon terms. Once the loan is fully repaid, including interest, the NFT collateral is returned to the borrower. If the borrower defaults on the loan, the lender can seize the NFT collateral and sell it to recover their funds.

Learn more about NFT loans and lending. 

This article was originally written by former AIMultiple industry analyst Arshia Mojtahedi and reviewed by Cem Dilmegani.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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