What’s New in Apache Kafka: Explore its Advantages and Use Cases in 2024

Imagine if someone kept a record of the market ups and downs for you. What if medical record management was taken care of by AI instead of a front desk worker? Think about the damages the country will face if the systems of NASA, NSA, or any other government service sector in any other country stop working or suddenly crash.

Don’t worry so much about it. We have a gift of technology just for that. An open-source, distributed streaming platform. Majorly designed for high-throughput and scalable data processing. It is the Apache Kafka. Undoubtedly, a very powerful tool for real-time management of complex data integration scenarios. It is often considered to be one of the best gifts of science and technology to the people. It is the stage which contains the solutions to nearly every tech problem. Need an app for finance management? Kafka has it. Want a shopping app? Kafka has it too. Need an app which can help you socialize well? Kafka has that too. You just say the word and Kafka gets it done for you.

Apache Kafka has proved to be majorly advantageous for easily handling high volumes of data. It provides a vast stage for the storage of multiple data formats. It is highly fault-resistant and supports multiple data protocols. It also proves to be immensely cost-effective in terms of investment by reducing the architectural cost. Apache Kafka has proven effective in drastically reducing downtime and enhancing efficiency and longevity. Concerning the users, it also reduces stockouts and encourages customer experiences by exposing the customers and clients to real-time inventory updates.

Many of the already existing features of Apache Kafka have been modified and innovated into extremely helpful and impactful remodelling. For this, we will have to know more about Apache Kafka and how this idea started taking over the world with its exceptional performance and unimaginable outcome. We will also get to know about its impact on technology and what more to expect from it in the future. The idea of such a powerful tool in itself is fascinating and quite challenging to make and provide to the world. However, we’ll know what alterations have been made to make it a huge success today.

 The key components of Apache Kafka are as follows:

• Topics: The place where data streams are organized and converted into logical channels or rooms.
• Partitions: The logical channels further divide into smaller, ordered segments.
• Producers: The section responsible for delivery of the recorded data.
• Consumers: The applications which are responsible for reading the given data.
• Brokers: The most important section including the servers. Also considered as the core of the Kafka cluster.
• ZooKeeper: The section responsible for coordination of the cluster configuration and its management.

However, if we look into the history of the making of the Apache Kafka, we come to witness that it was not made in one single go. There have been various improvements in its making. It is the result of those improvements which have given us an effortless and efficient Apache Kafka.

The numerous versions of Apache Kafka in use till now are distributed into two parts:

• Major versions

1. Kafka 0.7 (2011) – initial release
2. Kafka 0.8 (2012) – major scalability improvements
3. Kafka 0.9 (2015) – new consumer API, security features
4. Kafka 1.0 (2017) – improved performance and scalability
5. Kafka 2.0 (2018) – KSQL, Kafka Streams; improved security
6. Kafka 2.1 (2018) – improved performance and reliability
7. Kafka 2.2 (2019) – KRaft, Tiered storage
8. Kafka 2.3 (2019) – reliability and efficiency
9. Kafka 2.4 (2020) – enhanced security, KSQL improvement
10. Kafka 2.5 (2020) – performance improvements
11. Kafka 2.6 (2021) – enhanced security and KRaft improvements
12. Kafka 2.7 (2021) – improvements in functioning
13. Kafka 2.8 (2022) – security enhancement
14. Kafka 3.0 (2022) – (latest) major release with improved performance and reliability

• Minor versions

Each major version was often followed by a few minor versions which included improvements in bug fixing, performance, reliability, scalability and certain new features.

These minute improvements have led us to the latest version of Apache Kafka which is now the need of every company and industry whether big or small.

Along with these versions, Apache Kafka also has two release channels. They are:

• Stable channel: majorly recommended for production environments
• Development channel: place for the latest features and may include experimental changes.

Also, Apache Kafka versions follow the Apache software foundation’s support policy. This includes:

• Latest versions: full support and maintenance
• Previous version: support was limited to a few security fixes only
• Older versions: no support was given, only a few security fixes were provided

Apache Kafka is extremely versatile and scalable making it one of the first choices in various industries. It is a crucial component in modern data infrastructure. There are various Kafka tools which aid the smooth functioning of its system. Some of the popular ones include:

• For monitoring and management-

1. Kafka Manager: a web-based tool for cluster management
2. Kafka Tool: GUI-based tool used for topic inspection and consumer group management
3. Prometheus and Grafana: visualization and monitoring tools
4. Confluent Control Centre: combined monitoring and management stage

• For data integration-

1. Kafka Connect: integration of Kafka with external systems (official tool)
2. Confluent Hub: pre-built connectors for popular data sources
3. Apache NiFi: data flow tool for integrating external systems
4. Apache Beam: unified programming for data processing

• For stream processing-

1. Kafka Streams: java library for stream processing
2. Apache Flink: distributed stream processing engine
3. Apache Storm: real-time computation system
4. Confluent KSQL: streaming SQL engine

• For development and testing-

1. Kafka GUI: a visual tool for exploring Kafka topics
2. Kafka Cat: command-line tool for inspection
3. Kafka Console: command-line tool for the production and consumption of messages
4. Docker Kafka: super-contained Kafka environment

• For security and authentication-

1. Confluent Security: enterprise-grade security features
2. Kafka SSL/TLS: encryption and authentication of the given data
3. Kafka SASL: simple authentication and security layer
4. Apache Knox: basic gateway for winning Kafka access

• Some other important tools-

1. Kafka Eagle: monitoring and alerting in real-time
2. Burrow: consumer monitoring of Kafka
3. Kafka-Pixy: restful API for Kafka
4. Zipkin: distributed tracing system

These tools make it easier for Apache Kafka to build scalable, real-time data pipelines by simplifying the system’s development, deployment and management.

The key features of Apache Kafka which make it unique and accessible in its way include:

• Fault-Tolerant: replication of data ensures its availability across all nodes
• Scalable: handling high throughput and ensuring horizontal scaling
• Data-retention: data is retained for a considerable amount of time in Kafka
• Multi-subscriber: multiple consumers have the freedom to subscribe to the same topic
• Publish-Subscribe model: producers get to publish their data to topics while consumers subscribe the topics to get the data they have been looking for.
• Distributed architecture: designed to store bulky data across various nodes
• Real-time data processing: enables real-time data processing and event-driven architectures

These features have played an important role in making Kafka a much better option in terms of its reliability and capability for various companies and industries including:

• Finance and banking: Goldman Sachs, JPMorgan Chase, PayPal, American Express

Use cases-

1. Real-time transaction processing
2. Risk management
3. Market data analytics
4. Compliance reporting

• E-commerce and retail: Amazon, eBay, Target, Netflix

Use cases-

1. Real-time order processing
2. Inventory management
3. Recommendation engines
4. Customer behavior analytics

• Telecommunications: In Verizon, In AT&T, In T-Mobile, In Sprint

Use cases-

1. Call detail records
2. Network monitoring
3. Real-time billing
4. Customer experience analytics

• Healthcare: The UnitedHealth Group, The Kaiser Permanente, The Athenahealth, The Mayo Clinic

Use cases-

1. Storage of electronically produced health records
2. Medical imaging analysis
3. Patient data integration
4. Real-time health monitoring

• Technology and software: LinkedIn, Twitter, Uber, Microsoft

Use cases-

1. Real-time data integration
2. Event-driven architecture
3. Streaming analytics
4. Microservices communication

• Media and entertainment: Disney, Hulu, Viacom, Spotify

Use cases-

1. Real-time content delivery
2. User behavior analysis
3. Recommendation engines
4. Ad targeting

• Manufacturing and IoT: Siemens, Bosch, Cisco, Intel

Use cases-

1. Sensor data processing
2. Predictive maintenance
3. Quality control
4. Supply chain optimisation

• Government and public sector: NASA, National Security Agency(NSA), UK Government Digital Service, US Department of Defense

Use cases-

1. Data integration
2. Real-time analysis
3. Cybersecurity
4. Compliance reporting

These industries also help in improving Kafka’s performance in terms of operational efficiency, customer experiences and business growth. This idea reflects that it turns out to be a win-win situation for both the industries or companies using Kafka and Apache Kafka itself. Both the parties benefit along with the benefits of consumers as well.

Read More: What Is Web 5.0 & How Does It Differ From Previous Technologies?

 The major areas where Apache Kafka comes in very handy include:

• Event-Driven Architecture (EDA): it helps create scalable and event-driven systems
• Log Aggregation: helpful in the collection of log data from multiple systems and sources
• Streaming Analytics: aids the analysis of real-time streams
• Real-time Data Integration: helps in amalgamation of data from different applications
• IoT Data Processing: handling of IoT sensor data in large volumes
• Messaging: enables real-time communication between various applications
• Real-time notifications: delivery of messages and notifications based on real-life or recent events
• Microservices communication: allowing communication between microservices
• Audit trails: creation of auditable logs of system events
• Data ingestion: encircling the data into the data lakes, warehouses and databases

Kafka offers numerous benefits to its users making it a trusted source for them to rely on. Some of the benefits include:

Scalability and Performance-

• Scalable architecture for large-scale data streams
• High-throughput data processing
• Handling of bulky data with low latency

Flexibility and Integration-

• Supports multiple data formats
• Integration with various data sources and sinks
• Easily compatible with diverse programming languages

Security and Governance-

• Supports SSL/TLS encryption and authentication
• Access control lists(ACLs) for secure data access
• Auditing and logging capabilities

Cost-effective and Efficient-

• Open-source software reduces licensing costs
• Scalable architecture minimizes hardware requirements
• Efficient data processing reduces operational costs

Business benefits-

• Improved client experience through real-time engagement
• Enhanced operational efficiency through automation
• Increased revenue through data-driven decision-making
• Competitive advantage through innovative data processing
• Reduced risk through improved compliance and governance

Technical benefits-

• Simplifies data pipeline management
• Improved data quality and reliability
• Enhanced scalability
• Real-time data analysis
• Flexible integration with diverse systems and technologies

Client-specific benefits-

• Way more improved data quality
• Much enhanced collaboration
• Quicker Time-to-market
• Increased transparency
• Real-time visibility
• Improved decision making

Use case-specific benefits –

• IoT data processing
• Financial transactions
• E-commerce
• Medi field
• Cybersecurity

ROI(Return On Investment)-

• Reduced infrastructure cost
• Improved productivity
• Increased revenue through real-time decision-making
• Enhanced customer experience and content
• Competitive advantage

Other benefits-

• Simplification of data for the clients and consumers
• Improved data quality and consistency
• Real-time insights into business
• Allows microservices architecture
• Automatic failover and recovery
• Data durability through replication factor
• Facilitates event sourcing

What’s new in the Apache Kafka 2024?

Kafka is now brand new with the latest modifications for the users in 2024. The modifications include:

• Kafka 3.0

The most important release with improved scalability and security

• Tiered storage

Improved, efficient and effective storage of data

• Kafka connect 2.0

Super enhanced data integration and decentralization

• Kafka streams 2.0

Heavily improved data streaming with the least amount of latency

• Kraft(Kafka Raft)

Simplification of cluster management with raft consensus algorithm.

• TLS 1.3 support

Improved encryption for data transfer and vice-versa.

• OAuth 2.0 support

Standardised authentication and authorisation.

• Access control which is role-based

Superfine and minute access control over the content through RBAC.

• Open Telemetry Integration

Monitoring and tracing in a combined form.

• Serverless Kafka

Kafka runs without the management of the infrastructure.

• Edge computing

Kafka for IoT and edge computing use cases.

• The blend of Artificial Intelligence with Machine Learning

Kafka is used for data processing of AI and ML.

• Better resource utilisation

Kafka ensures much-improved memory management.

Along with these advances in Kafka, there are also some brand new launches in the use cases as of 2024. These include:

• New versions of real-time data processing

1. IoT sensor data processing: Kafka can now process sensor data from industrial equipment, vehicles and smart homes
2. Financial transaction processing: Kafka has enabled real-time transactions and further processing
3. E-commerce and order processing: Kafka has features which now have aligned order processing, inventory management and shipping

• Data Integration

1. Data Lakes: Kafka processes and stores data in the data lakes for further analysis
2. Cloud data integration: Kafka has integrated data across various cloud platforms such as AWS, GCP Azure etc
3. Data warehousing: Kafka majorly combines data from numerous sources into data warehouses

• Streaming analytics

1. Real-time analysis: Kafka has now enabled real-time analysis of user behaviours, market trends etc
2. Predictive maintenance: Kafka enables the processing of sensor data for predictive maintenance
3. Fraud detection: Kafka has improved its fraud detection system by alerting clients against fraudulent transactions in real-time

• The now-emerging use cases

1. Cloud-native applications: Kafka has now been supporting cloud-native applications and various microservices
2. Serverless architecture: Kafka ensures serverless data processing and event-driven architecture
3. Edge computing: Kafka now processes data at the edge for IoT and autonomous vehicles

Companies that use Apache Kafka:

• LinkedIn
• Twitter
• Uber
• Airbnb
• Netflix
• Goldman Sachs
• JP Morgan Chase
• American Express
• Walmart
• Target

These companies have benefitted by using Apache Kafka in various ways. The real-time analysis has played a significant role in hyping the demand for Apache Kafka. These companies look for certain features which include: Scalability, reliability, authentication and various other features which can track user behaviour and patterns. Apache Kafka has that in-store in it. It is a boon for companies which have entrepreneurs and working people as its target.

Let us take a look at a few of these client-specific successes-

• LinkedIn: much-improved data processing scalability
• Twitter: way more enhanced real-time analysis
• Uber: better data integration and scalability
• Airbnb: enhancement of real-time pricing and availability
• Netflix: new and advanced content recommendation engine

These are just a few to name. A much bigger aspect of Apache Kafka lies in the inter-related field where it helps as a medium.

This is all from the side of the producers. However, on the bigger side of the plate, we have equally important users. The users know the best and the worst of our products and their working. Hence, their benefits come first.

Various users benefit from Apache Kafka including:

• Developers

1. Simplification of data integration: Kafka is responsible for connecting various data sources and systems
2. Reliable data delivery: Kafka ensures guaranteed notification delivery
3. Flexibility in data processing: Kafka supports multiple data formats and protocols

• Data scientists/ analysts

1. Real-time insights: Kafka ensures real insights and aids decision-making
2. Data quality improvement: Kafka ensures data consistency and accuracy
3. Advanced analytics: Kafka supports machine learning and predictive analysis

• Business users

1. Enhanced customer services: Kafka supports personalized experiences through real-time data
2. Competitive advantage: Kafka enables businesses to stay ahead with real-time insights
3. Cost savings: Kafka reduces infrastructure with the help of scalable architecture

• Operational Teams

1. Improved security: Kafka guarantees secure data transfer and vice-versa
2. Efficient resource utilization: Kafka ensures optimum utilization of resources
3. Simplified monitoring: Kafka supports real-time monitoring and alerting

• Organizations

1. Reduced rate of risks: Kafka ensures secure data transmission and distribution
2. Customer engagement: Kafka’s support for personalized experiences aids customer engagement
3. Digital transformation: Kafka ensures real-time data-driven decision-making

• Industries

1. Telecommunications: real-time network monitoring
2. Manufacturing: predictive maintenance along with quality control
3. Media: real-time information sharing

Well, the customers who invest in Strivemindz get the benefits of Apache Kafka along with the extra advantages of Strivemindz. Like Apache Kafka, Strivemindz also makes your development process more agile. Using Apache Kafka in Strivemindz streamlines the deployment, configuration and management of Apache Kafka. The scalability of Strivemindz is an absolute natural complement to Kafka. Strivemindz offers Apache Kafka portability across infrastructure providers and various operating systems. With the benefits of Strivemindz, Apache Kafka clusters can span across on-site and public, private or hybrid clouds and use different operating systems.

We at Strivemindz, focus on training and enhancement of skills in various disciplines including technology, leadership and performance. Our areas of expertise include:

• Digital Information
• Data Science
• Analytics
• Cyber security
• Project management

Our major targeted audience often includes:

• Professionals
• Entrepreneurs
• Managers and leaders
• Students
• Organisations and teams

We primarily focus on the demands of the consumers. We know that only our consumers who are utilising our resources and services can give the group report and desired feedback. We, therefore, aim to get to every consumer and extract their feedback. This is what makes us unique in our way. Here at Strivemindz, we have set our goals concerning Apache Kafka. This includes:

Training and Development goals –

• Development of skills in terms of designing, implementation and management of the Kafka clusters
• Professional training of individuals in Kafka data integration, processing and analytics
• Making the individuals understand the workings of Kafka security, monitoring and troubleshooting
• Fostering expertise in Kafka-based real-time data processing
• Providing in-depth knowledge of Kafka architecture, its components and the ecosystem

Consulting and Implementation goals-

• Assisting organisations in designing Kafka-based solutions
• Development and integration of Kafka-based data pipelines for real-time data processing
• Ensuring an aligned and seamless blending of the existing systems and technologies

Research and Development goals-

• Exploration of the now emerging trends and technologies in the present Kafka ecosystem
• Development of innovative and flexible solutions with the help of Kafka, AI, ML and IoT
• Optimisation of Kafka configurations for only the desired use cases
• Creation of customised Kafka connectors
• Development of industry-specific Kafka solutions

Business goals-

• Establishment of Strivemindz as a trusted Kafka training and consulting partner
• Expansion of the client base across the globe
• Increased generation of revenue with the help of extensive training and various customer-friendly services
• Building various strategic partnerships with different companies for an improved supply of services to the customers
• Enhancement of the ultimate brand visibility in the Kafka ecosystem

Strivemindz ensures its clients that it will give them the best Apache Kafka experience and aims to be the leading provider of Kafka and its services shortly. We promise you the best of the products with the most affordable and amazing deals.