#apachekafka

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Getting old is interesting. I've always been a middle ground between sports (skateboarding, surfing, jiu-jitsu) and an avid student. Over time, it's clear that you can't keep trying the same tricks, just as you can't study the same way.

Today, as I'm writing an article about event-driven architectures, I realize that final exams in college were much easier, just like it's not as easy to jump the same stairs on a skateboard as I did when I was 18. In the image, you can see my favorite note-taking app, Obsidian, and my Neovim terminal. I'm diving deep into Java, and for that, I'm taking a Spring Boot bootcamp offered by Claro through the DIO Innovation One platform.

[feathersoft-info]

Apache Kafka Developers & Consulting Partner | Powering Real-Time Data Streams

In today's fast-paced digital landscape, the ability to process and analyze data in real-time is crucial for businesses seeking to gain a competitive edge. Apache Kafka, an open-source stream-processing platform, has emerged as a leading solution for handling real-time data feeds, enabling organizations to build robust, scalable, and high-throughput systems. Whether you're a startup looking to manage massive data streams or an enterprise aiming to enhance your data processing capabilities, partnering with experienced Apache Kafka developers and consulting experts can make all the difference.

Why Apache Kafka?

Apache Kafka is designed to handle large volumes of data in real-time. It acts as a central hub that streams data between various systems, ensuring that information flows seamlessly and efficiently across an organization. With its distributed architecture, Kafka provides fault-tolerance, scalability, and durability, making it an ideal choice for mission-critical applications.

Businesses across industries are leveraging Kafka for use cases such as:

Real-Time Analytics: By capturing and processing data as it arrives, businesses can gain insights and make decisions on the fly, enhancing their responsiveness and competitiveness.

Event-Driven Architectures: Kafka enables the creation of event-driven systems where data-driven events trigger specific actions, automating processes and reducing latency.

Data Integration: Kafka serves as a bridge between different data systems, ensuring seamless data flow and integration across the enterprise.

The Role of Apache Kafka Developers

Expert Apache Kafka developers bring a wealth of experience in building and optimizing Kafka-based systems. They possess deep knowledge of Kafka's core components, such as producers, consumers, and brokers, and understand how to configure and tune these elements for maximum performance. Whether you're setting up a new Kafka cluster, integrating Kafka with other systems, or optimizing an existing setup, skilled developers can ensure that your Kafka deployment meets your business objectives.

Key responsibilities of Apache Kafka developers include:

Kafka Cluster Setup and Management: Designing and deploying Kafka clusters tailored to your specific needs, ensuring scalability, fault-tolerance, and optimal performance.

Data Pipeline Development: Building robust data pipelines that efficiently stream data from various sources into Kafka, ensuring data integrity and consistency.

Performance Optimization: Fine-tuning Kafka configurations to achieve high throughput, low latency, and efficient resource utilization.

Monitoring and Troubleshooting: Implementing monitoring solutions to track Kafka's performance and swiftly addressing any issues that arise.

Why Partner with an Apache Kafka Consulting Expert?

While Apache Kafka is a powerful tool, its complexity can pose challenges for organizations lacking in-house expertise. This is where partnering with an Apache Kafka consulting expert, like Feathersoft Inc Solution, can be invaluable. A consulting partner brings a deep understanding of Kafka's intricacies and can provide tailored solutions that align with your business goals.

By working with a consulting partner, you can benefit from:

Custom Solutions: Consulting experts analyze your specific requirements and design Kafka solutions that are tailored to your unique business needs.

Best Practices: Leverage industry best practices to ensure your Kafka deployment is secure, scalable, and efficient.

Training and Support: Empower your team with the knowledge and skills needed to manage and maintain Kafka systems through comprehensive training and ongoing support.

Cost Efficiency: Optimize your Kafka investment by avoiding common pitfalls and ensuring that your deployment is cost-effective and aligned with your budget.

Conclusion

Apache Kafka has revolutionized the way businesses handle real-time data, offering unparalleled scalability, reliability, and speed. However, unlocking the full potential of Kafka requires specialized expertise. Whether you're just starting with Kafka or looking to optimize an existing deployment, partnering with experienced Apache Kafka developers and a consulting partner like Feathersoft Inc Solution can help you achieve your goals. With the right guidance and support, you can harness the power of Kafka to drive innovation, streamline operations, and stay ahead of the competition.

[govindhtech]

Get Started with IBM Event Automation on AWS Marketplace

Businesses can expedite their event-driven initiatives, no matter where they are in their journey, with IBM Event Automation, a completely composable solution. Unlocking the value of events requires an event-driven architecture, which is made possible by the event streams, event processing capabilities, and event endpoint management.

IBM Event Automation

The volume of data generated in an organisation is increasing at an exponential rate due to the hundreds of events that take place there. Imagine being able to access these continuous information streams, make connections between seemingly unrelated occurrences, and identify emerging patterns, client problems, or threats from competitors as they arise.

IBM Event Automation has the following features:

Event Streams: Use enterprise-grade Apache Kafka to gather and disseminate raw streams of business events occurring in real time. With a single, integrated interface, manage your Apache Kafka deployments, balance workloads, browse messages, and keep an eye on important metrics. Utilise the Kafka Connect framework to link easily with hundreds of popular endpoints, such as IBM MQ, SAP, and more.

Event Endpoint Management: Enable control and governance while encouraging the sharing and repurposing of your event sources. With ease, describe and record your events using the AsyncAPI specification, which is available as open source. Provide a self-service event source catalogue that users can peruse, use, and distribute. Using an event gateway, enforce runtime restrictions to protect and manage access to anything that can talk the Kafka protocol.

Event Processing: Create and test stream processing flows in real time using an easy-to-use authoring canvas by utilising the power of Apache Flink. At each stage, receive support and validation as you filter, collect, alter, and connect streams of events. Give business and IT users the tools they need to create business scenarios, recognise them when they happen, and take immediate action.

Information on Prices

Below are the total costs for these different subscription durations. Additional taxes or fees may apply.UnitsDescription12 MONTHSIBM Event Automation VPCsEntitlement to IBM Event Automation VPCs$3,396

Information on Usage

Fulfilment Options

SaaS, or software as a service

A software distribution paradigm known as “software as a service” involves the vendor hosting and managing the program online. Consumers do not own the underlying infrastructure; they only pay to use the software. Customers will pay for consumption through their AWS payment when using SaaS Contracts.

End-User License Contract

You accept the terms and conditions stated in the product’s End User License Agreement (EULA) by subscribing to this product.

Supporting Details

Software: IBM Event Automation

A benefit of IBM Event Automation is having access to IBM Support, which is offered everywhere, around-the-clock, every day of the year. Use this link to reach IBM Support: http://www.ibm.com/mysupport

Refund Guidelines

No reimbursements without IBM’s consent

You may now get IBM Event Automation on the AWS Marketplace. This represents a major advancement in deployment flexibility and accessibility for global enterprise clients. As of right now, companies can use their current AWS Marketplace Enterprise Discount Program (EDP) funds to purchase IBM Event Automation as self-managed software. This makes it possible for companies to purchase subscription licenses using the bring your own license (BYOL) strategy.

This breakthrough creates new opportunities for enterprises looking for seamless integration of sophisticated event-driven architecture capabilities into their AWS infrastructures. IBM ensures that customers can effortlessly build and maintain event-driven software capabilities while using AWS’s massive infrastructure and worldwide reach by listing IBM Event Automation on the AWS Marketplace.

With open plans available in more than 80 countries, IBM Event Automation is accessible worldwide. Clients can still seek access to IBM Event Automation by contacting their local IBM representative, even if they do not reside in one of the 80 mentioned countries. Because of its widespread availability, companies of all shapes and sizes can take use of its potential to successfully optimise their event management procedures. Clients can maximise their AWS budget under the BYOL subscription model by utilising their current IBM Event Automation licenses. This strategy aligns with cost optimisation initiatives and lowers upfront expenditures.

The ability to seamlessly integrate with other AWS services is made possible by hosting on the AWS Marketplace. This improves operational efficiency and gives clients access to a vast cloud ecosystem for their event-led integration requirements. AWS container environments, such Red Hat OpenShift Service on AWS (ROSA) and Amazon Elastic Kubernetes Service (EKS), can be deployed using IBM Event Automation. This containerised method is ideal for contemporary cloud-native designs since it ensures scalability, flexibility, and economical resource usage.

Customers can purchase IBM Event Automation on AWS Marketplace and install it directly in their own AWS environment, giving them more control over deployment details and customisation. Businesses planning to use the AWS Marketplace to implement IBM Event Automation should take into account a number of operational factors:

Infrastructure readiness refers to making sure the AWS environment satisfies the required security and performance standards and is ready to host containerised applications.

Licensing management: Controlling license usage to maximise cost-effectiveness, adhere to AWS Marketplace standards, and comply with IBM’s BYOL terms.

Integration and support: To expedite integration and address any operational obstacles, take advantage of IBM’s experience and AWS support services.

To sum up, the inclusion of IBM Event Automation in the AWS Marketplace is a calculated step towards providing businesses with advanced event management tools for their AWS setups. Organisations can improve cost optimisation, scalability, and operational efficiency by implementing containerised deployment choices and implementing a BYOL subscription model. Additionally, these strategies can facilitate smooth integration with AWS services. Businesses’ growing need for effective event-driven operations may be supported by IBM Event Automation on AWS Marketplace as they continue to embrace digital transformation and cloud adoption.

Read more on govindhtech.com

[algo2ace]

🚀 Exploring Kafka: Scenario-Based Questions 📊

Dear community, As Kafka continues to shape modern data architectures, it's crucial for professionals to delve into scenario-based questions to deepen their understanding and application. Whether you're a seasoned Kafka developer or just starting out, here are some key scenarios to ponder: 1️⃣ **Scaling Challenges**: How would you design a Kafka cluster to handle a sudden surge in incoming data without compromising latency? 2️⃣ **Fault Tolerance**: Describe the steps you would take to ensure high availability in a Kafka setup, considering both hardware and software failures. 3️⃣ **Performance Tuning**: What metrics would you monitor to optimize Kafka producer and consumer performance in a high-throughput environment? 4️⃣ **Security Measures**: How do you secure Kafka clusters against unauthorized access and data breaches? What are some best practices? 5️⃣ **Integration with Ecosystem**: Discuss a real-world scenario where Kafka is integrated with other technologies like Spark, Hadoop, or Elasticsearch. What challenges did you face and how did you overcome them? Follow : https://algo2ace.com/kafka-stream-scenario-based-interview-questions/

#Kafka #BigData #DataEngineering #TechQuestions #ApacheKafka #BigData #Interview

[sandipanks]

Reinvent your Business with Apache Kafka Distributed Streaming Platform

[agexplorers]

Be studious in your profession, and you will be learned. Be industrious and frugal, and you will be experienced...... Meet the new Confluent Administrator for Apache Kafka 🌐🔱🥇🏆🌐 @gauravsolanki84 👏🏼👏🏼👏🏼🤩 . . . . . . . #saturdayspecial #knowledgeispower💫 #leadbyexample #hardworkersonly #l4l #keepflying #thenewleader #directorships #likeforlikes #leadtheroles #globalleads #kafkaworld #apachekafka #confluentcertification #followyour❤️ #agexplorers #getsetgo #goglobal #panearthteams #administratorleaders (at Watford, United Kingdom) https://www.instagram.com/p/CJ1V1s2n_mC/?igshid=1nbys9rqpins0

[oscin]

5 Things about Event-Driven #API's and #ApacheKafka http://bit.ly/2sAdXgB APIs are becoming the crux of any digital business today. They provide a multitude of internal and external uses, including making B2B connections and linking building blocks for low-code application development and event-driven thinking. Digital business can’t exist without event-driven thinking. There are real benefits to developing event-driven apps and architecture—to provide a more responsive and scalable customer experience. Your digital business requires new thinking. New tools are required to adopt event-driven architecture. This includes being able to implement tools such as Kafka, Project Flogo®, and event-driven APIs. That said, if you’re not adopting event-driven APIs, you’re leaving revenue, innovation, and customer engagement opportunities on the table. Here are five things to know about how Kafka and event-driven APIs that can benefit your business. #Fintech #Insurtech #Wealthtech #OpenBanking #PSD2 #payment #Cybersecurity (hier: Zürich, Switzerland) https://www.instagram.com/p/B7L97HOA8iX/?igshid=1x8bjistwsd7e

[freshcodeit-blog]

Introduction to message brokers. Part 1: Apache Kafka vs RabbitMQ

The growing amount of equipment, connected to the Net has led to a new term, Internet of things (or IoT). It came from the machine to machine communication and means a set of devices that are able to interact with each other. The necessity of improving system integration caused the development of message brokers, that are especially important for data analytics and business intelligence. In this article, we will look at 2 big data tools: Apache Kafka and RabbitMQ.

Why did message brokers appear?

Can you imagine the current amount of data in the world? Nowadays, about 12 billion “smart” machines are connected to the Internet. Considering about 7 billion people on the planet, we have almost one-and-a-half device per person. By 2020, their number will significantly increase to 200 billion, or even more. With technological development, building of “smart” houses and other automatic systems, our everyday life becomes more and more digitized.

Message broker use case

As a result of this digitization, software developers face the problem of successful data exchange. Imagine you have your own application. For example, it’s an online store. So, you permanently work in your technological scope, and one day you need to make the application interact with another one. In previous times, you would use simple “in points” of the machine to machine communication. But nowadays we have special message brokers. They make the process of data exchange simple and reliable. These tools use different protocols that determine the message format. The protocols show how the message should be transmitted, processed, and consumed.

Messaging in a nutshell

Wikipedia asserts that a message broker “translates a message from the formal messaging protocol of the sender to the formal messaging protocol of the receiver”.

Programs like this are essential parts of computer networks. They ensure transmitting of information from point A to point B.

When a message broker is needed?

If you want to control data feeds. For example, the number of registrations in any system.

When the task is to put data to several applications and avoid direct usage of their API.

The necessity to complete processes in a defined order like a transactional system.

So, we can say that message brokers can do 4 important things:

divide the publisher and consumer

store the messages

route messages

check and organize messages

There are self-deployed and cloud-based messaging tools. In this article, I will share my experience of working with the first type.

Message broker Apache Kafka

Pricing: free

Official website: https://kafka.apache.org/

Useful resources: documentation, books

Pros:

Multi-tenancy

Easy to pick up

Powerful event streaming platform

Fault-tolerance and reliable solution

Good scalability

Free community distributed product

Suitable for real-time processing

Excellent for big data projects

Cons:

Lack of ready to use elements

The absence of complete monitoring set

Dependency on Apache Zookeeper

No routing

Issues with an increasing number of messages

What do Netflix, eBay, Uber, The New York Times, PayPal and Pinterest have in common?  All these great enterprises have used or are using the world’s most popular message broker, Apache Kafka.

THE STORY OF KAFKA DEVELOPMENT

With numerous advantages for real-time processing and big data projects, this asynchronous messaging technology has conquered the world. How did it start?

In 2010 LinkedIn engineers faced the problem of integration huge amounts of data from their infrastructure into a lambda architecture. It also included Hadoop and real-time event processing systems.

As for traditional message brokers, they didn’t satisfy Linkedin needs. These solutions were too heavy and slow. So, the engineering team has developed the scalable and fault-tolerant messaging system without lots of bells and whistles. The new queue manager has quickly transformed into a full-fledged event streaming platform.

APACHE KAFKA CAPABILITIES

The technology has become popular largely due to its compatibility. Let’s see. We can use Apache Kafka with a wide range of systems. They are:

web and desktop custom applications

microservices, monitoring and analytical systems

any needed sinks or sources

NoSQL, Oracle, Hadoop, SFDC

With the help of Apache Kafka, you can successfully create data-driven applications and manage complicated back-end systems. The picture below shows 3 main capabilities of this queue manager.

 As you can see, Apache Kafka is able to:

publish and subscribe to streams of records with excellent scalability and performance, which makes it suitable for company-wide use.

durably store the streams, distributing data across multiple nodes for a highly available deployment.

process data streams as they arrive, allowing you aggregating, creating windowing parameters, performing joins of data within a stream, etc.

APACHE KAFKA KEY TERMS AND CONCEPTS

First of all, you should know about the abstraction of a distributed commit log. This confusing term is crucial for the message broker. Many web developers used to think about "logs" in the context of a login feature. But Apache Kafka is based on the log data structure. This means a log is a time-ordered, append-only sequence of data inserts. As for other concepts, they are:

topics (the stored streams of records)

records (they include a key, a value, and a timestamp)

APIs (Producer API, Consumer API,  Streams API, Connector API)

The interaction of the clients and the servers are implemented with easy to use and effective TCP protocol. It’s language agnostic standard. So, the client can be written in any language that you want.

KAFKA WORKING PRINCIPLE

There are 2 main patterns of messaging:

queuing

publish-subscribe

Both of them have some pros and cons. The advantage of the first pattern is the opportunity to easily scale the processing. On the other hand, queues aren't multi-subscriber. The second model provides the possibility to broadcast data to multiple consumer groups. At the same time, scaling is more difficult in this case.

Apache Kafka magically combines these 2 ways of data processing, getting benefits of both of them. It should be mentioned that this queue manager provides better ordering guarantees than a traditional message broker.

KAFKA PECULIARITIES

Combining the functions of messaging, storage, and processing, Kafka isn’t a common message broker. It’s a powerful event streaming platform capable of handling trillions of messages a day. Kafka is useful both for storing and processing historical data from the past and for real-time work. You can use it for creating streaming applications, as well as for streaming data pipelines.

If you want to follow the steps of Kafka users, you should be mindful of some nuances:

the messages don’t have separate IDs (they are addressed by their offset in the log)

the system doesn’t check the consumers of each topic or message

Kafka doesn’t maintain any indexes and doesn’t allow random access (it just delivers the messages in order, starting with the offset)

the system doesn’t have deletes and doesn’t buffer the messages in userspace (but there are various configurable storage strategies)

CONCLUSION

Being a perfect open-source solution for real-time statistics and big data projects, this message broker has some weaknesses. The thing is it requires you to work a lot. You will feel a lack of plugins and other things that can be simply reused in your code.

I recommend you to use this multiple publish/subscribe and queueing tool, when you need to optimize processing really big amounts of data ( 100 000 messages per second and more). In this case, Apache Kafka will satisfy your needs.

Message broker RabbitMQ

Pricing: free

Official website: https://www.rabbitmq.com

Useful resources: tools, best practices

Pros:

Suitable for many programming languages and messaging protocols

Can be used on different operating systems and cloud environments

Simple to start using and to deploy

Gives an opportunity to use various developer tools

Modern in-built user interface

Offers clustering and is very good at it

Scales to around 500,000+ messages per second

Cons:

Non-transactional (by default)

Needs Erlang

Minimal configuration that can be done through code

Issues with processing big amounts of data

The next very popular solution is written in the Erlang. As it’s a simple, general-purpose, functional programming language, consisted of many ready to use components, this software doesn’t require lots of manual work. RabbitMQ is known as a “traditional” message broker, which is suitable for a wide range of projects. It is successfully used both for development of new startups and notable enterprises.

The software is built on the Open Telecom Platform framework for clustering and failover. You can find many client libraries for using the queue manager, written on all major programming languages.

THE STORY OF RABBITMQ DEVELOPMENT

One of the oldest open source message brokers can be used with various protocols. Many web developers like this software, because of its useful features, libraries, development tools, and instructions.

 In 2007, Rabbit Technologies Ltd. had developed the system, which originally implemented AMQP. It’s an open wire protocol for messaging with complex routing features. AMQP ensured cross-language flexibility of using message broking solutions outside the Java ecosystem. In fact, RabbitMQ perfectly works with Java, Spring, .NET, PHP, Python, Ruby, JavaScript, Go, Elixir, Objective-C, Swift and many other technologies. The numerous plugins and libraries are the main advantage of the software. 

RABBITMQ CAPABILITIES

Created as a message broker for general usage, RabbitMQ is based on the pub-sub communication pattern. The messaging process can be either synchronous or asynchronous, as you prefer. So, the main features of the message broker are:

Support of numerous protocols and message queuing, changeable routing to queues, different types of exchange.

Clustering deployment ensures perfect availability and throughput. The software can be used across various zones and regions.

The possibilities to use Puppet, BOSH, Chef and Docker for deployment. Compatibility with the most popular modern programming languages.

The opportunity of simple deployment in both private and public clouds.  

Pluggable authentication, support of  TLS and LDAP, authorization.

Many of the proposed tools can be used for continuous integration, operational metrics, and work with other enterprise systems.

RABBITMQ WORKING PRINCIPLE

Being a broker-centric program, RabbitMQ gives guarantees between producers and consumers. If you choose this software, you should use transient messages, rather than durable.

The program uses the broker to check the state of a message and verify whether the delivery was successfully completed. The message broker presumes that consumers are usually online.

As for the message ordering, the consumers will get the message in the published order itself. The order of publishing is managed consistently.

RABBITMQ PECULIARITIES

The main advantage of this message broker is the perfect set of plugins, combined with nice scalability. Many web developers enjoy clear documentation and well-defined rules, as well as the possibility of working with various message exchange models. In fact, RabbitMQ is suitable for 3 of them:

Direct exchange model (individual exchange of topic one be one)

Topic exchange model (each consumer gets a message which is sent to a specific topic)

Fanout exchange model (all consumers connected to queues get the message).

Here you can see the gap between Kafka and RabbitMQ. If a consumer isn’t connected to a fanout exchange in RabbitMQ, the message will be lost. At the same time, Kafka allows avoiding this, because any consumer can read any message.

CONCLUSION

As for me, I like RabbitMQ due to the opportunity to use many plugins. They save time and speed-up work. You can easily adjust filters, priorities, message ordering, etc. Just like Kafka, RabbitMQ requires you to deploy and manage the software. But it has convenient in-built UI and allows using SSL for better security. As for abilities to cope with big data loads, here RabbitMQ is inferior to Kafka.

To sum up, both Apache Kafka and RabbitMQ truly worth the attention of skillful software developers. I hope, my article will help you find suitable big data technologies for your project. If you still have any questions, you are welcome to contact Freshcode specialists. In the next review we will compare other powerful messaging tools, ActiveMQ and Redis Pub/Sub.

The original article Introduction to message brokers. Part 1: Apache Kafka vs RabbitMQ was published at freshcodeit.com.

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Today I have an merge conflict plus a deploy error in my link in bio app which is deployed in Fly io. Not big deal, just came out that was auth error in deployment, but about the merge conflict was a little bit more than a few lines.

[govindhtech]

New Amazon EC2 C7i & C7i-flex Instances: Power & Flexibility

Amazon EC2 C7i and C7i-flex instances

Utilizing 4th Generation Intel Xeon Scalable Processors, compute-optimized instances The C7i-flex and EC2 C7i instances on Amazon Elastic Compute Cloud (Amazon EC2) are next-generation compute-optimized instances with a 2:1 RAM to vCPU ratio. They are powered by specialized 4th Generation Intel Xeon Scalable processors (code called Sapphire Rapids).

These unique processor-powered EC2 instances are exclusive to AWS and deliver up to 15% greater performance than equivalent Intel processors used by other cloud providers. They are the best performing comparable Intel processors in the cloud.

For most compute-intensive tasks, the simplest approach to obtain price performance gains is with C7i-flex instances. When compared to C6i instances, they provide price performance that is up to 19% better. C7i-flex instances are an excellent initial option for applications that don’t use all of the computing resources because they come in the most common sizes, ranging from large to 8xlarge, with up to 32 vCPUs and 64 GiB of RAM.

The most popular compute-intensive workloads, such as web and application servers, databases, caches, Apache Kafka, and Elasticsearch, may be effortlessly operated on C7i-flex instances.

For workloads requiring bigger instance sizes (up to 192 vCPUs and 384 GiB memory) or persistently high CPU utilization, EC2 C7i instances offer advantages in terms of pricing and performance. Batch processing, distributed analytics, high performance computing (HPC), ad serving, highly scalable multiplayer gaming, and video encoding are among the workloads that EC2 C7i instances excel at. In comparison to C6i instances, C7i instances offer up to 15% better pricing performance.

Utilizing new Amazon EC2 Flex instances, reduce costs

A lot of users don’t use an EC2 instance’s entire compute capacity. As a result, those clients are paying for services that they do not require. For most compute-intensive tasks, the simplest option to obtain enhanced pricing performance is with Amazon EC2 C7i-flex instances. The majority of the time, Amazon EC2 Flex instances can scale up to full computing performance, making optimal use of compute resources. The goal of flex instances is to maximize both performance and cost.

Advantages

Reduced expenses

For most compute-intensive tasks, the simplest approach to optimize costs is with C7i-flex instances. When compared to C6i instances, they provide price performance that is up to 19% better. In terms of price performance, EC2 C7i instances outperform C6i instances by 15%. Additional larger instance sizes offered by C7i allow for consolidation and the execution of workloads that are more demanding and greater in size.

Adaptability and discretion

The most extensive and varied range of EC2 instances available on AWS is now enhanced by C7i-flex and EC2 C7i instances. Large to 8xlarge are the five most popular sizes offered by C7i-flex. Eleven sizes (two bare-metal sizes, c7i.metal-24xl and c7i.metal-48xl) with different vCPU, memory, networking, and storage capacities are offered by C7i.

Optimum efficiency using resources

Built on the AWS Nitro System, which consists of a lightweight hypervisor combined with specialized hardware, are the C7i-flex and EC2 C7i instances. Nitro improves overall performance and security by providing your instances with nearly all of the host hardware’s computation and memory resources. When it comes to workloads, EC2 instances based on the Nitro System can give throughput performance that is over 15% higher than other cloud providers using the same CPU.

Features

Driven by Intel Xeon Scalable Processors of the 4th Generation

Custom 4th Generation Intel Xeon Scalable processors with an all-core turbo frequency of 3.2 GHz (max core turbo frequency of 3.8 GHz) power the C7i-flex and EC2 C7i instances. These specialized CPUs provide the best performance among similar Intel processors and are exclusively offered on AWS. Support for Intel Total Memory Encryption (TME) always-on memory encryption is included in both cases.

Superior performance interfaces

Compared to C6i instances, DDR5 memory, which is used by C7i-flex and EC2 C7i instances, offers more bandwidth. Up to 12.5 Gbps of networking bandwidth and up to 10 Gbps of bandwidth for Amazon Elastic Block Store (Amazon EBS) are supported by C7i-flex instances.

Up to 50 Gbps of networking bandwidth and 40 Gbps of bandwidth to Amazon EBS are supported by EC2 C7i instances. Furthermore, EC2 C7i allows you to attach up to 128 EBS volumes to an instance, as opposed to C6i’s limit of 28 EBS volume attachments. Elastic Fabric Adapter (EFA) in the metal-48xl and 48xlarge sizes is also supported by EC2 C7i instances.

Fresh accelerators

There are four new integrated accelerators in Intel Xeon Scalable processors of the 4th generation. For applications like CPU-based machine learning, Advance Matrix Extensions (AMX), which are available on both C7i-flex and EC2 C7i instances, speed up matrix multiplication operations.

Available exclusively on EC2 C7i bare metal sizes, Data Streaming Accelerator (DSA), In-Memory Analytics Accelerator (IAA), and Quick Assist Technology (QAT) facilitate effective data offloading and acceleration, enhancing performance for databases, encryption and compression, and queue management workloads.

Constructed Using the Nitro Framework

The AWS Nitro System may be put together in a variety of ways, giving AWS the flexibility to quickly and flexibly construct EC2 instance types with an ever-expanding range of networking, storage, compute, and memory capabilities. The overall performance of the system is improved by Nitro Cards, which offload and speed I/O for functions.

The great majority of apps do not constantly operate at 100% CPU utilization. Consider a web application as an example. It seldom ever uses a server’s compute at full capacity, usually alternating between times of high and low demand.Image credit to AWS

Using the Amazon EC2 M7i-flex instances, first introduced in August, is one simple and affordable approach to run such workloads. With the extra benefit of providing you with better price/performance if you don’t always need full compute capacity, these less expensive versions of the Amazon EC2 M7i instances offer the same next-generation specs for general purpose computing in the most popular sizes. They are therefore an excellent first option if you want to lower your operating costs without sacrificing performance standards.

Because customers responded so favourably to this flexibility, they are now providing Amazon EC2 C7i-flex instances, which offer comparable price/performance benefits as well as lower prices for applications that require a lot of computation. These are less expensive versions of the Amazon EC2 C7i instances that provide a minimum CPU performance with a 95% capacity to scale up to the maximum compute performance.

Which is better, C7i-flex or C7i?

The compute-optimized C7i-flex and C7i instances are driven by exclusive 4th Generation Intel Xeon Scalable processors that are exclusively offered by Amazon Web Services (AWS). Compared to comparable x86-based Intel CPUs utilized by other cloud providers, they offer up to 15% higher performance.

They are perfect for running applications including web and application servers, databases, caches, Apache Kafka, and Elasticsearch. They both use DDR5 memory and have a 2:1 memory to vCPU ratio.

Why then would you choose to use one over the other? Here are three factors to take into account while choosing the best option for you.

Pattern of usage

When you don’t need to use all of the computational resources, EC2 flex instances are an excellent choice.

An efficient use of compute resources can result in five percent lower pricing and five percent better price performance. For compute-intensive workloads, C7i-flex instances should be the first option because they are generally a fantastic fit for the majority of applications.

Instead, you ought to use EC2 C7i instances if your application necessitates constant high CPU consumption. Workloads include batch processing, distributed analytics, ad serving, high performance computing (HPC), highly scalable multiplayer gaming, and video encoding are perhaps better suited for them.

Sizes of instances

C7i-flex instances come in the most popular sizes, up to a maximum of 8xlarge, and are utilized by most workloads.

The huge C7i instances, which come in 12xlarge, 16xlarge, 24xlarge, 48xlarge, and two bare metal alternatives with metal-24xl and metal-48xl sizes, are worth looking into if you require greater specs.

Bandwidth on a network

Depending on your needs, you might need to use one of the larger C7i instances because larger capacities also offer higher network and Amazon Elastic Block Store (Amazon EBS) bandwidths. With a network bandwidth of up to 12.5 Gbps and an Amazon Elastic Block Store (Amazon EBS) capacity of up to 10 Gbps, C7i-flex instances should be adequate for the majority of workloads.

Knowledgeable Regions: To find out whether C7i-flex instances are offered in the regions of your choice, see AWS Services by Region.

Purchase options: On-Demand, Savings Plan, Reserved Instance, and Spot form are available for C7i-Flex and EC2 C7i instances. Additionally, dedicated hosts and dedicated instances for C7i are offered.

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