LOUISVILLE, Colo., Oct. 31, 2023 (GLOBE NEWSWIRE) -- JumpCloud Inc. today announced it has been named the winner for the Hot Company SMB Cybersecurity award from Cyber Defense Magazine (CDM), the industry’s leading electronic information security magazine.

JumpCloud Wins Coveted Top InfoSec Innovator Award for 2023

Named Hot Company SMB Cybersecurity in Cyber Defense Magazine’s 11th Annual InfoSec Awards During CyberDefenseCon 2023LOUISVILLE, Colo., Oct. 31, 2023 (GLOBE NEWSWIRE) --  Inc. today announced it has been named the winner for the Hot Company SMB Cybersecurity award from Cyber Defense Magazine (CDM),

Executive positions have been filled at several prominent face biometrics providers, with Facephi, Mitek and ID.me announcing hires.

C-level moves at Facephi, Plaid, Prove, JumpCloud, new VPs for Mitek and ID.me

After moving on from lockdowns and supply chain shortages, small and medium-sized enterprises (SMEs) now face entirely new challenges: layoffs, recession fears, and a different kind of economic uncertainty.

Flexibility and adaptability: Preparing for what’s next in SME IT

Biometrics are preferred but old-school passwords persist as the dominant form of authentication says a new FIDO Alliance survey.

FIDO Alliance survey shows biometrics as the preferred consumer log-in method

Explore the newest ATO data from Sift’s Q3 Digital Trust & Safety Index, learn about Sift’s latest product integrations and upgrades, and read about the company’s recent awards and recognition. The post Digital Trust & Safety Roundup: Rising ATO,

Digital Trust & Safety Roundup: Rising ATO, new product updates, and Sift recognition

Artificial intelligence (AI) makes cybercriminals smarter. But AI makes us smarter, too – when we enact the policies that can foil it. As security professionals, we know that we can’t protect against everything. Some detection methods are noisy or ...

Taking Stock of Identity Solutions in the Age of AI

JumpCloud Releases JumpCloud Go™ for Phishing-Resistant Passwordless Authentication and More in Newest Release

JumpCloud Inc. today updated its Open Directory platform to include support for a passwordless login capability, dubbed JumpCloud Go, that leverages biometric authentication tools embedded in operating systems running on endpoints to further ensure security.

JumpCloud Adds Passwordless Authentication to Open Directory Platform

JumpCloud Inc. today announced a slate of new features to its open directory platform. Today’s release enhances organizations’ security capabilities and improves identity and device management for users,

JumpCloud

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Since GDPR came into effect in 2018, the world has slowly shifted from perceiving cybersecurity as a mere burden that most organizations dismiss, to viewing it as a business vector that should be carefully analyzed, planned, monitored, and above all, receive managerial attention and dedicated resources. However, the shift from the first described “mode” to the second has been gradual, especially among organizations that did not have more than a slim handful of privacy regulations to which they were obligated to adhere. In such circumstances, this shift has been too slow and only mildly affected the managerial prioritization or the allocation of resources. While the awareness of organizations toward cybersecurity escalated dramatically, its practical application is still lagging. 2024 will leave significantly fewer organizations that could still claim to have this mere handful of regulations to follow. This will derive the urgency to quickly adapt, or otherwise, be at risk of breaches or be left outside of business opportunities due to cybersecurity implementation gaps. Continuing last year's trend of increased regulation frameworks, more regulations have been developed and published, depicting 2024 as another fruitful year in cyber and privacy guidance, with some notable themes and trends already taking shape:. 1. Regulatory Onslaught: AI and ESG Take Center Stage To say that the cyber and vendor security regulatory landscape is expanding is a mere understatement. For one, the is set to control and reduce AI-associated risks within the European region, underscoring a giant leap as it becomes the world’s first comprehensive AI law. EU AI Act Secondly, has increased public awareness of the frameworks due to increasing customers' support for environmental conservation, human rights, and ethical business operations. In the next few years, companies are more likely to prioritize building relationships with suppliers who align with their internal values. However, there is still no industry-wide standard for evaluating ESG performance, despite the greater attention being paid to ESG considerations. Third-Party Risk Management (TPRM) Environmental, Social, and Governance (ESG) Other regulatory frameworks surging include , which unifies guidance for all EU members, on response and recovery from all types of ICT-related disruptions and threats; which will improve the ability to incorporate trustworthiness considerations into the design, development, use, and evaluation of AI products, services, and systems; which puts forward guidelines for cybersecurity risk management, governance and incident reporting by public companies (US); which was designed to present tiered security levels for contractors for US Government vendors, and which has been drafted by the German Federal Ministry of Labor and Social Affairs (BMAS) to regulate Corporate Due Diligence Obligations in Supply Chains that are operating with connection to German entities. DORA (Digital Operational Resilience Act) NIST’s AI RMF (Risk Management Framework) SEC Disclosure Rules (by The Securities and Exchange Commission) CMMC (Cybersecurity Maturity Model Certification) The German Supply Chain Act (GSA) 2. Board Engagement: A New Paradigm for Cybersecurity and ESG As the importance of cybersecurity continues, supervising it will no longer be exclusive to designated teams. Moving forward, boards will be more involved, covering cybersecurity measures and broader ESG aspects. Today, the connection between cybersecurity and ESG is becoming more apparent, and board members now recognize that sustainable business practices require a comprehensive strategy. Boards must incorporate social responsibility, environmental impact, and transparency in governance into their cybersecurity plans as demand on corporations to do so grows. This all-encompassing strategy recognizes the wider influence of cybersecurity on the standing, reliability, and long-term viability of the company. Boards now have to consider the ramifications of this paradigm change for the organization's social effect, ethical standing, and compliance with changing regulatory frameworks, in addition to the technical nuances of cybersecurity. The inclusion of ESG and cybersecurity on the board's agenda indicates that organizational risk management has matured and is now in line with wider society's expectations for security procedures. 3. Generative AI: Stakes and Emerging Risks Generative AI increases productivity, automates documentation, and simplifies several procedures. Simply put, it is becoming a need for businesses to remain competitive as they incorporate it into their operations more and more. However, this integration has its own set of concerns, some of which have long existed but continuously remain as pressing issues. The autonomous generation of information, designs, and innovations by Generative AI systems raises concerns regarding intellectual property (IP). Protecting confidential data becomes crucial, necessitating the implementation of strong safeguards by enterprises to preserve their intellectual property. When massive volumes of sensitive data are processed by Generative AI systems, data integrity becomes even more important. As a result, ensuring the security and privacy of this data becomes an insurmountable task, requiring more extensive encryption, access controls, and monitoring systems. The risks associated with Generative AI aren’t brand new. However, the scale and complexity of AI-driven systems demand a more vigilant culture for organizations to proactively address these challenges. Through this, the full potential of Generative AI can be unlocked without compromising company data. 4. Persistent Threat: Data Breaches and Ecosystem Impact Make no mistake: data breaches will always be present despite advancements in cybersecurity. Even the biggest companies, frequently regarded as security heavyweights, haven’t gotten their immunity badge yet. As a result, repercussions impacting the whole ecosystem will likely continue as corporate activities are interdependent. We’ve seen some of the biggest data breaches last year alone. In March 2023, the ChatGPT breach led to the data exposure of . It contained the last four credit card digits, the expiration date, the email address, the payment address, and the first and last names of the current user. Other breaches that came months later include the MOVEit, JumpCloud, and T-Mobile, just to name a few. 1.2% of subscribers This pattern is unlikely to change in 2024 as threat actors evolve alongside the advancement of cybersecurity tools. The ongoing occurrence of data breaches emphasizes the necessity of continuous watchfulness, flexible security tactics, and industry-wide cooperation to strengthen the group's defense against cyberattacks. Establishing effective incident response and recovery procedures must take precedence over only preventing breaches for organizations. Just as important as preventing a breach in the first place is the capacity to quickly and efficiently lessen its effects. Nowadays, cybersecurity is a strategic necessity for preserving stakeholder confidence and protecting organizational resilience, not only a defensive tactic. 5. Rise of BISO: Bridging Business and Security A notable development in the organizational hierarchy is the rise of the This role serves as a bridge between business strategy and cybersecurity, acknowledging the intrinsic link between these two domains. The BISO is tasked with aligning security objectives with broader business goals, ensuring that cybersecurity measures contribute to, rather than hinder, organizational success. Business Information Security Officer (BISO). As vendor security becomes a core component of corporate strategy, the BISO is essential in helping the board, executive leadership, and the cybersecurity team communicate with each other. This integration ensures that organizational goals are smoothly integrated into security activities, rather than being isolated from the larger business strategy. The duties of the BISO go beyond technical proficiency to encompass a thorough comprehension of business operations, risk tolerance, and regulatory environment. Organizations can take proactive actions to address cybersecurity issues and leverage security measures as drivers of business resilience and innovation when they are strategically aligned. Conclusion The arrival of AI in cybersecurity represents a generational opportunity, one that must be approached with both vigilance and responsible stewardship. This era is unparalleled in its readiness to harness such a technological leap. Whether it is protecting against ongoing data breaches,, increased and active board participation, legal developments, or the rise of the BISO role, all these aspects can be supported, guided, and infrastructurally supported by Generative AI to ensure a more robust company posture to Vendor Security and TPRM as a whole. Indeed, TPRM has long been recognized as a formidable logistical challenge awaiting a solution within the technological domain. It is time we leverage the sophisticated technology at our disposal to address this challenge effectively, ushering in a new era of robust and resilient cybersecurity infrastructure.

The Future of Vendor Security: 5 Trends to Watch in 2024

Secrets in version control systems (VCS) like git is the current state of the world . Once source code enters a git repository, it can organically spread into multiple locations. This includes any secrets that may be included within. But why then are secrets in git repositories so common. despite widely being recognized as a bad practice Why secrets end up in git A seasoned developer may be scratching their heads wondering why anyone may put secrets inside a git repository. But the fact is, secrets inside git repositories is the current state of the world. Previously we have discussed why it is common to choose the path of least resistance when it comes to accessing and distributing secrets. , so it makes sense, at least from a convenience point of view, that secrets are stored inside a private git repository to make distribution and access easy. Git acts as the central point of truth for a project But storing secrets like this is playing with fire, it only takes a very small incident to get burnt. In addition to intentionally storing secrets in git, . Secrets may be hardcoded into source code, stored as text file, shared over Slack or buried inside a debug application log. In addition, developers can be in large distributed teams with access to a plethora of secrets while being faced with reduced release cycles and an ever growing number of technologies to master. when secrets are not managed properly, it is very easy to lose track of them Why secrets in git are dangerous Source code, we have to remember, is very leaky. Code is copied and transferred everywhere. Git is designed in a way that allows, even promotes, code to be freely distributed. Projects can be cloned onto multiple machines, forked into new projects,  distributed to customers, made public so on and so forth. Each time it’s duplicated on git, the entire history of that project is also duplicated. Why storing secrets in public repositories is bad will be obvious. They are freely available to everyone on the internet and it is very easy to monitor public repositories, GitHub has a public API to fetch all public commits for example. But what about private git repositories? Private repositories don’t publish your source code to the internet openly, but it doesn’t have adequate protection to store such sensitive information either. Imagine if there was a plain text file with all your credit card numbers within it, you hopefully wouldn’t put this into the companies git repository, secrets are just as sensitive. A few things to consider when storing secrets in private repositories: Everyone in the organization with access to the repo has access to the secrets within (one compromised account can provide an attacker access to a trove of secrets).Repositories can be cloned onto multiple machines or forked into new projects.Private repositories can be made public which can have secrets buried in the git history. Another important consideration is that code removed from a git repository is never actually gone. Git keeps track of all changes that are made. Code that is removed - or more technically correct: code that is committed over - still exists within the git history. Interestingly enough, code is removed from a project at near equal volume that is added. This means that the code within repositories are much deeper than the first layer and secrets could be buried deep within the git history under a mass of commits that have been long forgotten. git contributions graph https://github.com/hashicorp/vault/graphs/code-frequency Comment: The contributions graph that you see above from HashiCorp Vault repository is a typical view of a project's history. The regularity you find in project contribution graphs is both surprising and interesting (check out some projects graphs, it seems to be a rule of nature). Real world examples: recent data breaches Secrets being leaked into public places happens with surprising regularity. If you perform a search on GitHub for the commit message , you will find thousands of results. And that's just within public repositories. ‘removed aws key’ https://github.com/search?q=removed+aws+key&type=Commits GitGuardian detects over 3,000 leaked secrets each day within public GitHub alone, there are thousands of examples for this but below are a couple recent or noteworthy examples. Publicly disclosed examples of recent data breaches through leaked credentials. Starbucks Data Breach - January 2020 JumpCloud API key found in GitHub repository Equifax Data Breach - April 2020 leaked secrets in personal GitHub account granted access to sensitive data for equifax customers Uber Data Breach - October 2016 Poor password hygiene allowed intruders to access Uber’s Amazon S3 Datastore using an AWS access key posted in a private GitHub repository. If this seems like an issue for only large companies to worry about, it’s not. Attackers are constantly exploiting personal services through secret keys too. In one example, . bad actors scanned GitHub for AWS keys and used them to mine cryptocurrency, leaving developers with thousands of dollars in debt Detecting secrets in reviews (or not) One great advantage of git is to be able to quickly and clearly see changes made and compare previous and proposed code states. It’s therefore common to believe that if secrets are leaked in source code, they of course will be detected within a code review or in a pull request. Code reviews are great for detecting logic flaws, maintaining good coding practices and keeping code quality high. But they are not adequate protection for detecting secrets. This is because reviews generally only consider the net difference between the current and proposed state. Not the entire history of a branch. Branches are commonly cleaned before being merged into the master branch, temporary code is added then deleted, unnecessary files added then removed.. But now these files, which are high risk candidates for containing secrets, are not visible to the reviewer (unless they want to go through the entire history of a branch). Let's walk through the example above. While this is over simplified it tells a familiar story. Commit B a file named main.py is added. A new branch is created to add a new function to main.py in commit C, this feature uses an API key so to save time for testing this is hardcoded. Once the feature is working the hardcoded API key is replaced with an env variable and the file is cleaned. Finally a pull request is made and accepted because the reviewer looks at the net difference between commit B and D, ignoring commit C. Now undetected secrets are buried in the git history of the project. While this scenario is very basic, add in hundreds of commits and files between master and a development branch and you can see how easy it is to miss secrets in code reviews. Using automated detection to find secrets in git Taking in consideration all we have just discussed about secrets inside git, it is clear that this is a problem that will persist and one we cannot solve with human code reviews. While automation is not always the answer, detecting secrets, in particular secrets inside git, automated secrets detection is a clear solution to this widespread problem. Unfortunately detecting secrets in git is not quite as easy as it first seems because of the probabilistic nature of secrets. This makes it hard to distinguish between a true secret and other random-looking strings like database IDs or other hashes. The good news however, is that has built powerful tools for developers to detect secrets in git. A great dashboard with native GitHub and GitLab integrations, a CLI tool called or you can even build custom your own git secrets scanner using the . GitGuardian GG-Shield GitGuardian API Wrap up Let’s have a quick review of what we have gone through. Git repositories are very common places to find secrets and they remain the perfect incubator for secrets to sprawl into multiple locations. Git keeps a track of a project's history which can be deep making finding secrets difficult. Because of the workflow git creates, it is common for any secrets to be missed during manual checking procedures and automated secrets detection should be introduced into the SDLC. Also published on GitGuardian .

How To Store Secrets Like API Keys

Cloud technology is no more unknown to any of the individuals in this digital world. This advanced technology is on the horizon, and it has created a huge buzz in every corner of the world these days. According to of Worldwide Cloud in 2017,  around 85% of Enterprise IT businesses have committed to multi-cloud architectures just during 2018, this helped them to drive up the rate and provided huge opportunities to bagged massive growth in their business field. IDC Predictions Nowadays, businesses need to seek help from one or more than one cloud-based service provider to secure and save their business data and for much other relevant information. Business demands huge space online these days, which has given rise to a multi-cloud approach. The multi-cloud solution is rapidly unfolding over the past few years because it provides many advantages to businesses, especially when it comes to reliability, cost, and scalability. It's also well known for providing hassle-free access to the businesses, they can access the best and newest cloud-based tools, IT resources which they used previously for maintaining all the service and save their precious time which was wasted after maintaining data traditionally. Ways to Consider for Making a Multi-Cloud Approach for Your Business Most of the business giants can't just settle down with one cloud-based service provider as they have a massive demand for the same. According to a , two-thirds of respondents responded that they are currently using or planning to seek services from multiple cloud-based providers, some of the popular choices which can be considered by them are, Microsoft Azure and Amazon Web Services (AWS). Apart from that, Oracle Cloud and Google Cloud hold second place in the market, whereas Tencent Cloud, Fujitsu Cloud Services, IBM Cloud, and Alibaba Cloud is used by some of the entrepreneurs and own a lesser portion in the current market. TechRepublic recent survey As the demand for a multi-cloud approach is increasing rapidly, it wouldn't be wrong if we say that multi-cloud holds the future. Even this approach has arrived in many business sectors and in various ways, which include ways of carrying out business, ushering new IT strategies, and much more. Today competition among cloud providers is also increasing as every one of them wants to provide much better service to the businesses on their demand. Even businesses give priority to a cloud approach before choosing any of the available ones for their business, now the for the businesses as they have a wide variety to choose from. 31% of businesses go for public cloud while 45% go for hybrid cloud and rest go for public or private cloud approach for their business. hybrid cloud is no more best option (Source) Top Reasons That Justify Why Businesses Must Opt for a Multi-Cloud Approach The multi-cloud approach allows businesses to reshape their business field. predicts that remarkable growth has been noted in the public cloud services market worldwide, it was found that public cloud revenue jumped and reached 17.5% in 2019. It is also predicted that it will reach around $332 billion in just three years (that is by 2022). There is no single point to doubt that growth has been driven by cloud infrastructure services, as more than a third of businesses cloud investments in a multi-cloud approach keeping it as a top-three investment priority. Gartner's recent report Multi-cloud is far beyond infrastructure, this approach encompasses various cloud-based tools that businesses use or needs to run or perform various activities of their organization. Many of the businesses have already started using various cloud-based services for marketing automation, accounting, analytics, and much more. The growing demand for a multi-cloud approach has made it win for Microsoft’s Azure, In October, the software giant bagged a massive competitive $10 billion contract. Even it has powered its marketing cloud due to which it has become a prime necessity in its multi-cloud strategy. (Source) Multi-cloud cloud approach allows businesses to use connected and multiple cloud services to reduce overall costs, become more agile with the application infrastructure, increase scalability, etc. This approach comes with huge potential and possesses the capacity to meet the specific needs of the businesses. Although multiple cloud solutions are complex, it helps businesses to meet their targeted goals in no time. There are various reasons that justify why businesses must opt for a multi-cloud approach for their business, they are: Utilize existing investment. Get unique capabilities. Operate freely from anywhere. Optimize costs. Provide sturdy infrastructure. The multi-cloud approach allows businesses to expand their business exponentially. They even get the flexibility to add or change cloud providers whenever they want, it will be advisable for them to choose their service provider, keeping in mind the market evolution. Effective Ways to Implement a Multi-Cloud Approach for Your Business More than responded that a multi-cloud strategy is proved to be a boon for their business. They also added that using multiple private and public clouds can reduce their application workload and can manage each information related to their business. 86% of respondents Over recent years, most of the cloud providers have experienced a drastic expansion in need of the business, they used on an average three public cloud and four private clouds. Unsurprisingly, a significant jump between intermediate and advanced users can be measured by cloud service providers as there is a huge adoption of cloud service by the businesses. (Source) Before moving to any decision about cloud service implementation, businesses have to consider the following steps. They can even explore steps to know how to leverage multi-cloud solution advantages in a much better way. The multi-cloud approach provides various benefits to businesses. But to leverage, its benefits businesses have to make sure that they implement the right cloud strategy for their business. Business needs to define its aims and motivations before adopting the cloud strategy. Some of the business goals which businesses can achieve with cloud service are listed below: Find Key Areas to Implement Cloud Quickening application delivery pace. Improving IT efficiency. Reducing IT infrastructure cost. Enhancing automation. Expand as per the new market. Every business needs an effective strategy when it comes to expanding business growth and success. Businesses these days are also leveraging fruitful results after seeking help from it will help you to reach more customers worldwide. But when it comes to satisfying all their customer's needs and manage the business effectively then cloud strategy comes into the spotlight. Today a large number of businesses have opted for multi-cloud and thus developed a hybrid strategy, which is simply a compo of private as well as public cloud options. Outlet Cloud Strategy pay per click service The cloud journey is not quite a touch for every business. They are always at the security risk as there is a fear of data threat, but when they use a cloud solutions, then it ensures data safety. It also helps them to manage every activity of their business very effectively and smartly. Businesses can also automate needed controls across a different area, which includes operations, finance, inventory, provisioning, and many more. Overcome Common Cloud Hurdles Businesses need to implement a centralized group of clouds for managing cloud initiatives. Define cloud policies before implementing the same for your business, also check its management cost. Make sure that the cloud team includes security, finance, procurement, development, and much more. Make sure that the team is capable enough to fulfill primary roles criteria, which simply include: Start Your Cloud Journey Cloud architecture. DevOps Engineers. Management specialists. Project managers. Make Sure the Multi-Cloud Approach Fits Your Business Needs Businesses can use various cloud business automation platform, which is available in the market these days, this can help them to manage various cloud environments. This cloud environment can include private, public, and provides various services to businesses. This service includes: Cloud provisioning and billing services. Support & inventory management. Marketing and Sales management. Business Intelligence. Customizable marketplace to sell cloud. Multi-Cloud Approach: Opt It to Prepare Your Business For Future! Implementing and adopting a multi-cloud approach for your business with a little budget is a daunting task. Many of the businesses hesitate to jump to this advance approach due to its complexity. This is one of the common concerns of all the startups who have just established their branches in a fiercely competitive market. But they have to understand that it's the future and if they don't adopt it, then they will remain far back in the competitive business race. However, before choosing the multi-cloud solutions, there are several questions which businesses have to consider, they are: Whether the solutions are user-friendly? Will it scale up company growth? Can the solutions help them to create a single source with minimal maintenance? Once they find all the answers, they can just adopt the right cloud solution for their business and can enjoy the fruit of success even after. By adopting the multi-cloud approach, they can quickly achieve the targeted goals in their business. It can also help them to make their business advance and future proof. All Image: (Source)

Multi-Cloud Approach⁠: An Excellent Way to Future-Proof Your Business

Society is built upon a story. The glue that holds humanity together is fiction, a creatively invented story for “the greater good.” Look at money. Why trade our lives for paper? Wars are waged over this meaningless money. And money my friends is the one bubble that’s never popped. Money’s also the source of power for government. Before that it was royalty, and force. Before rulers were gods, or ordained by the gods… and on and on. The power of stories defines humanity. The story of the Ponzi scheme For anyone who does not know, a Ponzi scheme is an elaborate scam where you pay to participate and just need to convince 3 friends to convince 3 friends to convince 3 friends… and so the money flows. Notice the pyramid-esque nature. This is a Ponzi scheme, aka a scam. And it is illegal. ( ) BTW we aren’t talking about ICOs and cryptocurrencies, which are definitely a ponzi scheme (plus much more). For more on ICOs boom, bust and rebirth, and see this pos t You have probably heard of Bernie Madoff. He ran the most infamous Ponzi scheme ever, stealing a supposed $65B for “innocent” investors/fellow Ponzi participants. Source: ScamAvenger But that begs the questions: where are Bezos and Zuckerberg on this list? Let’s talk network effects (NFx) You could argue today’s social networks look eerily similar to a Ponzi scheme ( ). The dynamics of a connected world create tangible value for members and enormous financial rewards for the creator(s) of the network. still a far cry from ICO scammyness network As an investor, I am constantly looking for startups with true network effects. Companies able to leverage the flywheel of continuously increasing value warrant ridiculous valuations. From social platforms like: Facebook, Instagram and Whatsapp to B2C marketplaces like Amazon, Airbnb and , successful network effects result in nearly untouchable monopolies (f_or more_ ). Uber on today’s monopolies stifling innovation, see this post And network effects aren’t only applicable to consumer companies. Look at and Box. Slack What do all these companies have in common? Great products with killer network effects. Acquiring customers One of the most important responsibilities of any founder is acquiring customers. Without users and cash, the business dies. Network effects and “virality” are two of the most crucial strategies for organic growth. startup And organic growth built the unicorns and tech behemoths of today. Few giants grow from paid acquisition — the costs are just too high. And as markets saturate, CAC rises. This is not true with true network effects. Instead as networks grow, all participants benefit and CAC remains constant, if not decreasing. Look at Amazon. Onboarding initial buyers and sellers was hard. It was just a bookstore. Fast forward to today to the Everything Store. Suddenly Amazon’s doing $136B/yr and sellers everywhere are scrambling to get onboard. Amazon doesn’t need to pay a dime. And customers come too. When Amazon has more options and better prices than Walmart, and delivers to your door, it isn’t even fair. Amazon’s flywheel fixes their initial capital challenges, increases margins, decreases CAC and drives LTV through the roof. Source: PointofSale.com The 5 types of network effects Not all businesses can leverage NFx. And even many that do have limits ( ). which is why Airbnb’s a 100x better business than Uber There are five type of network effects, each with strengths and weaknesses. They are: Marketplaces — aka Two Sided Networks Channel partners — aka Three Sided Networks Communications networks Content networks Local networks 1. Marketplaces — Two sided networks The first and most obvious type of network the market. Markets have existed since the dawn of time and grown exponentially in the internet era. Markets just make sense. People have needs and wants — from your stomach to sex to survival, markets pop up around every conceivable human want and desire. These traditionally consist of buyers and sellers. Historically, prior to the rise of cities, nomadic traders travelled between small settlements, continuously peddling their wares and pulling in profit. Farmers bought tools, craftsmen bought meats and traders traded in everything. Notice the movement. Traders came to the people. This has not changed. As buyers aggregate, sellers somehow find their way. Implication: Because acquiring customers is so much harder than acquiring sellers, businesses need to spend the bulk of their resources focused on buyers. As the number and quality of buyers in a market increase, companies can start to focus more on sellers — specifically quality control. Look at Amazon and Ebay. What is the primary difference, besides success? Ebay’s auction platform that encourages negotiation and lowball offers, hurting sellers and slowing the speed of transactions. This attracts low price, unattractive buyers. Amazon does the exact opposite. They focus on seller quality, ensuring good customer experiences which leads to more word of mouth. Buyers don’t brag about Ebay, sellers either. But everyone and their mother talks about Amazon. That is because Amazon is fast, cheap and easy to use — everything Ebay is not. Source: The Motley Fool Application: Building a two-sided market? NPS is incredibly important (first for buyers, then for sellers). Onboard customers, make the process fast and seamless and suddenly you have a sharable marketplace. Two-Sided Marketplace Examples Low Tech: shopping centers, farmer’s markets and brothels. Tech: Airbnb, Uber, Amazon Strengths of marketplaces Nearly untouchable after critical mass Monopolistic pricing power to increase commissions Low maintenance and operating costs once established (unless like Amazon you own distribution) Huge valuation multiples due to strength of network Weaknesses of marketplaces Very challenging to acquire customers/users Need to balance supply and demand Cash intensive to get started Revenue lags growth ( .) NOTE: For more on tokens/ICOs ability to disrupt established markets, see this post Channel partners — Three sided networks Not all commerce is conducted via a platform. The reason? Acquiring customers in a nascent market is hard. Because the business models and structures of marketplaces and channel partnerships are so different, most companies cannot do both well. A real world example. As an ecommerce/Amazon strategist, I’ve consulted for numerous public companies on their future and the world of Amazon. These corporates knew they needed to increase sales online but they had a problem. To sell on a marketplace meant alienating existing distributors (channel partners). If XYZ competes with their distributors, distributors suffer and thus XYZ suffers. Can you kill the aging golden goose? Perhaps the best example of the monopolistic power for this type of network is Microsoft. Microsoft owned the desktop and personal computing space. They built a vast network of suppliers and channel partners and then squeezed them. Companies could not afford to exclude Windows and Microsoft Office, users HAD to have it. And because legacy systems and software were all built and reliant upon Windows, switching costs were prohibitively high. So Gates got to charge mafia money — pay the ransom or watch your business burn… Source: JumpCloud.com Most channel partnerships do no go like this. That said, it is usually the responsibility of the brand to boost awareness — unless you pick your partners well. Take for instance bookkeeping. Few entrepreneurs know, like or care about bookkeeping. They just need to get it done. So they find a bookkeeper who uses and recommends . Xero Now the owner has two choices: find a new bookkeeper or use Xero. Inevitably most buy in. That is how Xero became a ~$4.5B business, off the back of recommendations (aka channel partnerships or affiliate marketing). Implications/Application: Channel partnerships do not work for every type of business. It really depends on the product and end customer. They can be massively lucrative and scalable when done right. Channel partnerships require trust and demand generation — both of which can come from you or your partner. Here the medium and product play a large role. For instance grocery stores get millions of pitches, they aren’t going to push your product. It is up to the brand to create demand and move inventory. If they can’t, shelf space goes to someone who can. A great counter example would be podcast marketing. Joe Rogan (the UFC guy with a massive podcast audience) singlehandedly turned the Fleshlight into a huge hit. The adult product was Joe’s first advertising partnership and the product hit it off, quickly connecting with Joe’s audience and outside the box approach — the company didn’t need to do a thing… What type of channel partnership can your company leverage? Channel partnership examples: Low Tech: bigbox retail, QVC, catalogs Tech: Microsoft, Xero, affiliate marketing Strengths of channel partnerships Outsourced acquisition means lower capital expenditure Ability to scale sales quickly with established networks Low maintenance and operating costs once established Ability to focus more on core product Weaknesses of channel partnerships Long sales cycle for onboarding channel partners Demand/trust component Lower margins Less direct contact with end customer More dependent on partners, thus riskier [LIKE THIS ARTICLE SO FAR? THEN YOU’LL REALLY WANT TO SIGN UP FOR MY NEWSLETTER — AND GET SOME FREE BONUSES!] OVER HERE Communication networks Successful social media companies warrant crazy valuations. Instagram sold for $1B before making a buck. Snap has yet to prove they can make money and are valued at ~$17B. And don’t forget the 500k pound gorilla in the room, Facebook’s evil empire. Communication is one of the truest forms of network effects. For every new user, my value goes up. If ½ my friends are on and ½ are off, there are ONLY two options: onboard everyone or switch to something better. Imagine if your cell phone could only make calls within your carrier’s network. Joe’s on Tmobile, mom and dad have AT&T, grandma’s got Verizon… you would need 3 different phones and 3 different plans. The same is true for social. Everyone or no one. If you have to switch between apps and services, you are not going to stick around. But with communication the viral component is key. Add all your friends. Invite everyone on LinkedIn. Want to join Whatsapp? The pressure to grow the network for my own personal reasons does the dirty work for them. Facebook isn’t spamming my friends, I am. And I and billions like me built a 2B+ person network worth $531B+. The key to communications networks is onboarding — as quickly as possible. Networks that leverage users’ social proof scale. Networks that pay to acquire customers fail. It is that simple. Implications/Application: Given these dynamics, it is no surprise that social networks are winner take all. And that justifies massive valuations and upfront investments. In winner take all markets, monetizing too soon can be catastrophic. In social, the outdated VC mantra of “growth at all costs” actually makes sense. Any advertising, any monetization and you risk reduced NPS scores and lower virality (the two MOST important aspects of any communication network). Communication Network Examples: Low Tech: fax, mail, phone Tech: Facebook, Whatsapp, Slack Strengths of communication networks Viral growth as users onboard their networks Increasing value as # of users increases Low maintenance and operating costs once established Massive switching costs to leave network Weaknesses of communication networks Require lots of capital and runway to get started Need very strong NPS score to encourage sharing No direct monetization model Some networks get too big to control the noise Content networks Whereas communication networks involve two-sided, near real-time communication, content networks are much more one-sided. And as they say, content is king — and it lasts. Content does for eyeballs and attention what markets do for commerce, it aggregates it. And content has long been a profitable business. Look at magazines and newspapers (pre-internet). These publications put out content, users subscribe (pay) and advertisers add tons of ads. The editors can sell ad space because readers are hooked and keep coming back for more. And unlike communication networks, the one-sided nature of content allows it to scale easier. An article or video can have 100 or 100M views without much increase in base costs. There are two keys to creating a killer content network: 1) the content and 2) the hook. It is one thing to wow readers or viewers, it is another to get them coming back. But because advertising is such a weak monetization model, it means you need big volume and better have users coming back for more. This is where the hook comes in. In television it is the cliffhanger ending that makes you come back next week (or next episode with Netflix). But blogs do this too. It is all about subscribing and remarketing again and again and again to drive the network. Source: TVRopes.org Implications/Application: Content networks function inversely to marketplaces. Whereas marketplaces make money by initially aggregating buyers, content companies need creators. Without the first few landmark pieces, people get bored. This is why organizations like Youtube or Netflix spend so heavily on content (empowering creators and creating content respectively). Evergreen content keeps producing, even after production ends. And with all great content, people love sharing. How many of your friends recommended Game of Thrones or House of Cards or that horrible Gangnam Style video? People want to feel cool and know about things before their friends — look at any social feed. Any content network that optimizes for quality and creativity can create a massive loyal following — as long as it effective remarkets and suggests additional content. Content Network Examples: Low Tech: newspaper, TV, radio Tech: Youtube, Instagram, Medium Strengths of content networks Evergreen content continually delivers Value (and potential customer base) increases as amount of content grows Creators do much of early marketing for you Large switching costs for creators leave network Low to no cost of content creation Weaknesses of communication networks Onboarding creators or creating content is expensive/hard Need very strong NPS score to encourage sharing No direct monetization model Some networks get too big to control the noise Local Networks Last but not least, we have local networks. As technology and the internet have connected the world, these networks have struggled. That said they are still very relevant, especially the dynamics behind them. Local networks will never die, unless VR ends reality. Look at humanity’s basic needs: food, clothing, shelter, sex/relationships. All of these are physical, tangible and heavily influence our daily lives. And evolution bred society into humanity. We are social creatures. We are built to connect. Whether that is meeting for dinner, going to the game, poker night or a casual hookup, we tend to aggregate ourselves. Think about sports? Almost every American has a TV. You could watch any game at home, whenever you wanted. So why do fans buy tickets? Why are sports pubs so popular? No one likes drinking alone. These dynamics are what make local networks so strong? Church groups, sports teams, the neighborhood pool… there are local networks all around us. The problem with local networks is that they are small. For the most part they are not VC fundable. But businesses based locally with nationwide scale can succeed. Look at — local networks of everything under the sun, organized by location and interest — like the Reddit or real life. Meetup Implications/Application: You build massive companies like this one piece at a time. Facebook was a local network before it was a communication network. The keys to growth: focus on one target customer in one location at one time. A leaking ship eventually sinks. Networks that try to scale before perfecting the model need significantly more capital and still usually fail. Franchisees are perfect example of this model. McDonalds started small, perfected the experience and slowly started to scale. Ray Kroc (not actually the founder), focused on each locale, perfecting the process and exciting the public. From their the franchise spread as nationwide and then international demand exploded. Local Network Examples: Low Tech: restaurant, sports teams, church group Tech: Meetup.com, , Tinder Nextdoor Strengths of local networks Easy to interact with early adopters/customers Brand ambassadors help with much of marketing In-person events drive greater emotional attachments Every new city/expansion increases business defensibility Weaknesses of communication networks Scaling presents logistical challenges Once it “works”, users may no longer need app (example: Tinder, Match.com etc…) Culture fit/demand differences between regions Need to compete with increasingly digital world Closing Thoughts Network effects are one of the strongest indicators of business model success. Organic growth eats paid acquisition for breakfast. Unfortunately designing network effects isn’t exactly easy. Hopefully the concepts discussed in this post help you better understand your business model. What are you investing in and how do network effects play into your investment philosophy? For investors: What are you building and how can you leverage organic growth, virality and most importantly networks effects to build an enormous business? For founders: Answer those questions successfully and this insanely long blog post will have been worth it. Before you go… If you got something actionable or valuable from this post, click the💚 or 👏 button below or share the article on Facebook and Twitter so your friends can benefit from it too. Hold down the clap button if you liked the content! It helps me gain exposure . Clap 50 times! Donations and support are always greatly appreciated BTC Wallet: 14qFdA5A6BVY8oxbWDBjUmXdTkpujbCAKr ETH Wallet: 0x36d558c84204f3902F0C5C02afBEfd649CD3E23E LTC Wallet: LNyMG1Kf6UhD5GircU8iD6uQeo5FFJCiiK

The 5 Types of Network Effects and How to Hack Them

Hi Guys !! Many people asking me recently to explain how to deploy Algo VPN on a Cloud provider, VPN that includes 'ad-blocker'. Well, I explained and installed too many, now decided to prepare a video tutorial that all can watch and follow to deploy Algo VPN by themselves. The video is online now on . YouTube Please, comment with beneficial recommendations so we can all enjoy this beautiful open-source project better ! Enjoy ! Regards.

How To Deploy Algo VPN with NextDNS on UpCloud [Video]

167 Stories To Learn About Vpn

This is the second part of my orchestration tools review. As a sequel of . Let’s try next. Docker How to Build SaaS Using Docker-based Tools — Part 1 Kontena Kontena CLI The first impression of is “really cool” and “very developer friendly”. The first thing you need to do is to install Kontena CLI. I’ve installed it both on MacOS and Ubuntu by following the documentation and didn’t have any problems with that. After installation, you can start using it right away because it’s very simple to use and is good enough (as opposed to Docker Cloud CLI). Kontena documentation For using Kontena, you need to have a master node — this is a machine that you could bootstrap on any cloud provider and that could be used as a central point for managing other nodes, grids, and services. Supported cloud providers At this moment, as mentioned in docs, Kontena supports 5 cloud providers: AWS, Azure, Digital Ocean, Packer and UpCloud. I’ve tried to setup it with Amazon Web Services. To start installing you master node on AWS, you need to setup AWS user with security credentials. Just go to AWS IAM and create a new user. For example, “kontena-master” or any other name. Make sure to check “Programmatic access” checkbox because you need to have access and secret keys. Then set permissions for the just created user by selecting policy. “AmazonEC2FullAccess” On the last step, you’ll get “Access Key ID” and “Secret access key”. Save it somewhere in your project. But that’s not all: you need to have an AWS EC2 Key Pair — these are keys you need for Kontena to be able to login into your nodes. Just switch your AWS Console to Amazon EC2 service and create a key pair. Once done, now you have all information to setup Kontena master node on AWS. Lets’ do this by using CLI command described in docs (don’t forget to change and options according to your needs): — storage — type $ kontena aws master create \  --access-key <aws_master_key> \  --secret-key <aws_secret_key> \  --key-pair <aws_key_pair_name> \  --type m3.medium \  --storage 100 \  --region eu-west-1 During the process of master node creation, you will be asked for several questions. One is about authentication for accessing your Kontena managed infrastructure and recommended variant at this point is to go with authentication via Kontena Cloud. This is what I’ve chosen as well. But besides Kontena Cloud, any other OAuth2-compatible authentication providers could be used. Grids, Stacks and Services Let’s create the first grid: recommended initial node counts are 3,5,7 — according to rules. These nodes will form a cluster infrastructure — main nodes responsible for your cluster stability and failover. etcd etcd $ kontena grid create --initial-size=<initial_node_count> aws-grid

# Now let's create AWS EC2 node in the current grid

$ kontena aws node create \  --access-key <aws_master_key> \  --secret-key <aws_secret_key> \  --key-pair <aws_key_pair_name> \  --type m4.medium \  --storage 100 \  --zone a \  --region eu-west-1 After your grid is ready for operating, you are becoming closer to prepare your infrastructure. Let’s say you have a web app that needs a database and of course, web server/load balancer. I’ve come to using micro-services approach and having each component in the separate container. There are two ways of organizing your containers: Via separate independent (that could be linked together) Services Via (which is the preferred way and recommended by Kontena team) Stacks So Kontena Stack is actually an organized group of Services and Service is a group of containers using the same Docker image. The second option is what I’ve chosen and started preparing my stacks: stack for a web app, stack for DB, stack for a load balancer. You could use pre-defined Kontena images for database and load balancer, but you need to pack your web app into an image as well. I’ve already had one for my web app — APIQ CMS. So my services are Ruby on Rails app packed into Docker image, PostgreSQL and Load Balancer (web server with public HTTP exposure). Here are examples of my Stacks definition (you define it using YAML format and according to ) Kontena.yml # lb-stack.ymlstack: apiq/lbdescription: APIQ LBexpose: internet_lbservices:internet_lb:image: kontena/lb:latestports:- 80:80- 443:443deploy:strategy: daemon Need to note option here — this allows you to expose your service to other stacks. expose: # db-stack.ymlstack: apiq/dbdescription: APIQ DBexpose: dbservices:db:image: postgres:lateststateful: true A database is a stateful service and I guess you want to store you databases data somewhere and database nodes removal won’t affect your data. # kontena.ymlstack: apiq/coredescription: APIQ Corevariables:secret_token: # variable nametype: string  # type (string, integer, boolean, uri, enum)from: # where to obtain a value for this variablerandom_string: 64 # still no value, auto generate a randomto:env: SECRET_TOKEN # send this value to the vault on kontena masterservices:app:image: webgradus/kmscommand: bundle exec rails s -p 3000 -b '0.0.0.0'environment:SECRET_TOKEN: ${SECRET_TOKEN}RAILS_SERVE_STATIC_FILES: 'true'KONTENA_LB_INTERNAL_PORT: 3000KMS_ASSETS_STORAGE: 'fog'hooks:post_start:- name: db_createcmd: rails db:createinstances: 1oneshot: true- name: db_migrationcmd: rails db:migrateinstances: 1links:- lb/internet_lb- db/dbports:- "3000:3000"secrets:- secret: AWS_ACCESS_KEY_IDname: AWS_ACCESS_KEY_IDtype: env- secret: AWS_SECRET_ACCESS_KEYname: AWS_SECRET_ACCESS_KEYtype: env- secret: AWS_REGIONname: AWS_REGIONtype: env Let’s review this in details. First, section. This section allows you to define variables and the way of obtaining them that could be used in Kontena.yml. In my Stack file, I’m generating a random string and pass it to environment variable (that should be specified for the app). defines the way app should be launched, — environment variables for an app as well as special Kontena variables (for example, that links web app and load balancer). Next, I’ve defined several hooks executing Rails-specific tasks necessary for correct app bootstrapping — database creation and migration. It’s pretty clear that section links service with and services exposed from first two stacks. And section is what Kontena Vault is responsible for — you’re specifying keys for API or database passwords accessible on the Grid level. In my example, I’m using Amazon S3 service in my web app and want any service node to have access to Amazon credentials. Prepare your secrets by saving them into Vault: variables SECRET_TOKEN command environment KONTENA_LB_INTERNAL_PORT links app db internet_lb secrets app $ kontena vault write AWS_REGION us-east-1 Now we could install prepared stacks on nodes: $ kontena stack install lb-stack.yml$ kontena stack install db-stack.yml$ kontena stack install --name cms-1 kontena.yml After this, I’ve got APIQ CMS instance connected to database and load balancer and available on the web. To find out public IP and check out your app in browser: $ kontena service show cms-1 Where is SaaS actually? Ok, very good question :-) So now after you’ve tried everything using CLI, it’s time to perform the same operations via Kontena Master REST API. Just generate API token and start installing app stacks on your customer demand. Follow and write code in your favorite language. API documentation $ kontena master token create --expires-in 0 --token Kontena Cloud Once you’ve signed up on Kontena Cloud and used it for authentication, you could login anytime and view the dashboard of your Kontena-based infrastructure (absolutely for free now). Really cool! There you’ll find your Grids, Nodes, Services, resources usage, and logs. Community Next thing about Kontena need to mention is very good support from the core team. I’ve asked two questions on Kontena forum and always got responses. Also, there are many new resources appearing: video guides on YouTube, the regular email digest of Kontena news and updates, as well as blog posts. In conclusion, I’d recommend to give it a try even if you don’t need to build SaaS. You could just manage infrastructure for your app by easy scaling your services, adding more nodes and grids.

How to Build SaaS Using Docker-based Tools — Part 2

TO GET OUR LATEST BENCHMARKS, SEE OUR 2019 UPDATE HERE We have offered a fully managed Kafka service for some time now, and we are quite often asked about just how many messages can you pipe through a given service plan tier on a selected cloud. So here’s a benchmark we conducted to give you a rough idea on just how well Apache Kafka performs in the public cloud. What is Kafka? Apache Kafka is a for collecting and processing large numbers of messages in real-time. It enables you to accept streaming data such as website click streams, events, transactions or other telemetry in real-time and at scale, and serve it downstream to stream processing applications. Kafka is built distributed for both scalability as well as fault tolerance. Adding more horizontal nodes to tackle growing loads is fairly straightforward and automatic replication of the data over more than one node maintains availability when nodes fail. The basic concepts in Kafka are and . A is an application that generates data but only to provide it to some other application. An example of a application could be a web server that produces “page hits” that tell when a web page was accessed, from which IP address, what the page was and how long it took to render the page by the web server. On the side there could be multiple systems interested in the same page hit data stream: high-performance open-source stream processing platform producers consumers producer producer consumer A time series database that is used to plot the total number of page hits over time A reporting application collecting summaries of the pages accessed and sending them to a data warehouse database system A DDoS detection system trying to find abnormal access patterns A rate limiting monitor counting the number of hits from a specific source address And so on… Kafka suits these kinds of applications very well: it provides a method of getting the data out of the hands of the producing application quickly and safely. Once the producer has written the message to Kafka, it can be sure that its part of the job is done. The producer application does not need to know how the data is used and by which applications, it just stores it in Kafka and moves on. On the consumer side a powerful feature of Kafka is that it allows multiple consumers to read the same messages. In our web page hit example above, each of the consumer applications get their own read cursor to the data and they can process the messages at their own pace, all without causing any performance issues or delays for the producer application.  Here’s what it roughly looks like: The Zookeeper cluster is a critical piece in keeping Kafka healthy and up and running. It maintains Kafka’s metadata and most importantly, a consensus between the Kafka nodes of who is doing what. Aiven Kafka as a Service Aiven Kafka is a a fully managed service based on the Apache Kafka technology. Our aim is to make it as easy as possible to use Kafka clusters with the least amount of operational effort possible. We handle the Kafka and Zookeeper setup and operations for you, so you can focus on value-adding application logic instead of infrastructure maintenance. Aiven Kafka services can be launched in minutes, and we’ll ensure they remain operational, well performing, up-to-date and secure at all times. Nodes are automatically distributed evenly across the available availability zones in order to minimize the impact of losing any of the zones. Aiven Kafka is available in , , , and with a total coverage of 53 cloud regions. In this performance comparison we ran the benchmark on all of these except DigitalOcean, where our Kafka offering is limited by the available plans. Each Kafka service used in these tests is a regular Aiven-provided service with no alterations to its default settings. Amazon Web Services Microsoft Azure Google Cloud Platform UpCloud DigitalOcean Benchmark Setup In this first Kafka benchmark post, we set out to estimate maximum for various Aiven Kafka plan tiers in different clouds. We wanted to use a typical customer message sizes and standard tools for producing load. We also wanted to generate the load from separate systems over the network to make sure the load could mimic the actual customer workloads as closely as possible. High-level view of the test setup, a single Aiven Kafka service with five nodes, distributed evenly over the availability zones: write throughput rates We picked message size of 512 bytes for our tests. Based on our experience, one of the most typical payloads is a JSON encoded message ranging somewhere between 100 bytes to 10 kilobytes in size. In these tests, we use a single topic with the partition count matching the node count of each Aiven plan tier. For more complex topic/partition setups Aiven actively balances the placement of the partitions, trying to achieve a “perfect” distribution of partitions. In the case of this test there is just a single partition for each node, so this is rather simple. We set the replication factor to one (1) in the case of this test, meaning each of the messages only resides on a single Kafka node. Apache Kafka version used was 0.10.1.1. For load generation, we chose to use and from the provided examples. We are using default settings for the most part, but bumped up single request timeout to 60 seconds as we expect the Kafka brokers to be under extreme load and request processing to take longer than under a normal healthy load level. Also, since Aiven Kafka services are offered only over encrypted TLS connections, we included the configuration for these, namely the required certificates and keys. librdkafka defaults to a maximum batch size of 10000 messages or to a maximun request size of one million bytes per request, whichever is met first. In these tests, we did not employ compression. producer.props configuration: librdkafka rdkafka_performance metadata.broker.list=target-kafka.benchmark.aivencloud.com:10947security.protocol=sslssl.key.location=client.keyssl.certificate.location=client.crtssl.ca.location=ca.crtrequest.timeout.ms=60000 We ran several instances of rdkafka_performance on multiple VMs on a different cloud provider from the one being tested. So all of the test load was coming from the internet thru the nodes’ public network interfaces. We kept increasing the number of instances until we could find the saturation point and the maximum message rates for each plan. Each rdkafka_performance instance was started on the command line with: rdkafka_performance -P -s 512 -t target-topic -X file=producer.props Benchmark Results First set of tests was run on an Aiven Kafka Business-4 plan, which is a three node cluster and a common starting point for many of our customers. Each node in this plan has 4 gigabytes of RAM, a single CPU core and 200 gigabytes of disk on each node, providing a total 600 gigabytes of raw Kafka storage capacity in the cluster.  Write performance (3 nodes @ 4 GB RAM, 1 CPU, 200 GB disk each): On UpCloud, we hit 200,000 messages per second. Azure and Google plans saturated at 120,000 and 130,000 messages per second and the Amazon deployment reached 50,000 messages per second. The performance is pretty respectable. The performance on Amazon is a bit behind the others because of the node types available and we will be looking at ways to optimize that in the future. As you will see in the next graph for the test with the bigger plan, the AWS performance is already more in line with the other providers. Next, we tested three node clusters but with larger underlying instances using the Business-8 plan. This plan has nodes with 8 gigabytes of RAM, two CPU cores and 400 gigabytes of disk per node, i.e. all the primary resources are doubled when compared to the Business-4 plan. This test indicates how well Kafka scales vertically with increased resources. Write performance (3 nodes @ 8 GB RAM, 2 CPU, 400 GB disk each): We see a nice increase in performance, with 320,000 messages per second on UpCloud, 205,000 on Azure, 170,000 on Google and 160,000 messages per second on AWS. In the last test, we wanted to verify how well Kafka scales horizontally. With this test, we went from the Business plan tier to the Premium tier, which bumps the node count from three to five, while keeping the node specs otherwise identical. Also the test setup was updated to utilize a partition count of five (vs. three) for this test. Write performance (5 nodes @ 8 GB RAM, 2 CPU, 400 GB disk each): The results here are solid for Kafka: a two-thirds increase in the number of nodes resulted in a straight 2/3 increase in write performance. Awesome! Aiven Kafka Premium-8 on UpCloud handled 535,000 messages per second, Azure 400,000, Google 330,000 and Amazon 280,000 messages / second. Benchmark Conclusions Apache Kafka performs just as well as we expected and scales nicely with added resources and increased cluster size. We welcome you to benchmark your own workloads with Aiven and to share your results. We utilize Kafka as a message broker within Aiven as well as use it as a medium for piping all of our telemetry metrics and logs. We are happy with with our technical choice, and can recommend Apache Kafka for handling all kinds of streaming data. Find out more about Aiven Kafka at . https://aiven.io/kafka

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