As you might know, Dynatrace, Inc. ( NYSE:DT ) just kicked off its latest third-quarter results with some very strong...

Results: Dynatrace, Inc. Exceeded Expectations And The Consensus Has Updated Its Estimates

On Tuesday, Morgan Stanley initiated coverage on shares of Dynatrace Inc. (NYSE: NYSE:DT), a software intelligence company, with an Equalweight rating and a price target of $60.00. The firm sees the Observability market, which Dynatrace operates within, as a significant $28 billion opportunity.

Morgan Stanley sets Dynatrace stock at equal weight, $60 target By Investing.com

Dynatrace, Inc. ( NYSE:DT ) just released its third-quarter report and things are looking bullish. It was overall a...

Dynatrace, Inc. Just Beat Earnings Expectations: Here's What Analysts Think Will Happen Next - Simply Wall St News

The initiative involves the use of Dynatrace's Carbon Impact application, which calculates the carbon dioxide equivalent (CO2e) of IT resource utilization, including CPU, memory, disk, and network I/O. The app also offers recommendations on reducing the carbon footprint and includes filters ...

Dynatrace aids Lloyds with IT carbon footprint tracking By Investing.com

Dynatrace, Inc. put the patience of investors to the test over the previous year, with its stock performance lagging several SaaS peers. Read more here.

Dynatrace Q3 Earnings: Requiring Further Patience From Investors (Downgrade) (NYSE:DT) | Seeking Alpha

All-in-one performance monitoring\n\nUse a single platform to analyze application performance throughout your application’s full stack, down to each individual transaction across all layers and technologies

Dynatrace

If you rely on the Cloud to run an awesome , this one here is for you. AWS business Let me assume that one of the biggest reasons you chose the AWS Cloud is because, with all of its resources and capabilities, this platform lets you deploy your solutions where your customers are. — Not having to worry about the infrastructure is a thing of beauty in itself for us too. However, in this ever-expanding, complex environment it’s becoming impossible for most of the monitoring tools out there —let alone humans — to find performance problems. Does the monitoring alert tell you about the impact that the threshold violation is having on the user experience of your customers? I collected three monitoring super powers in Dynatrace designed to optimize your use of the AWS Cloud — and make you feel like an AWS productivity wizard. 1. Automatic discovery of dynamic instances Dynatrace auto-detects AWS resources as well as application services and draws a real-time map of the dependencies between your applications, services, processes, and AWS infrastructure components. , your entire application topology is visualized in an interactive infographic. As simple as that. With no manual configuration Auto-discover your whole infrastructure within minutes 2. Full-stack AWS monitoring After discovering your cloud environment, Dynatrace starts to collect performance data about all your different AWS resources. On a single dashboard you will see key metrics about your: EC2 instances EBS volumes S3 buckets RDS instances Amazon DynamoDB tables Elastic Load Balancers AWS Lambda functions For example, it’s super easy to discover which Availability Zone an EC2 instance runs in and what applications and services are hosted on top of it. All your AWS resources on a single dashboard 3. Smart problem notification and root-cause analysis And last but not least, the magic: Dynatrace artificial intelligence automatically pinpoints the reasons underlying performance issues. When critical performance issues are discovered, you don’t have to manually interpret dozens of data sources to know the root cause. Dynatrace provides a single problem notification that identifies the root cause of the problem — an EC2 instance running low on disk space, for example — and see the precise number of affected user actions. What you get is a clear infographic of how one problem caused another one, and you can also replay the problem evolution to understand what happened when. This lets you cut down your mean time to repair by 95%, so you can focus on what’s most important: running your awesome business! Automatic root-cause analysis is a unique Dynatrace capability that’s changing the lives of operations teams

Dynatrace & AWS: Plug-n-play for the modern cloud

Future-Proof Your App: Scalability Considerations for Long-Term Success

ভবিষ্যত-প্রুফ আপনার অ্যাপ: দীর্ঘমেয়াদী সাফল্যের জন্য স্কেলেবিলিটি বিবেচনা

Machen Sie Ihre App zukunftssicher: Überlegungen zur Skalierbarkeit für langfristigen Erfolg

Prepare su aplicación para el futuro: consideraciones de escalabilidad para el éxito a largo plazo

Pérennisez votre application : considérations d'évolutivité pour un succès à long terme

अपने ऐप को भविष्य के लिए तैयार करें: दीर्घकालिक सफलता के लिए स्केलेबिलिटी पर विचार करें

将来を見据えたアプリの構築: 長期的な成功のためのスケーラビリティの考慮

미래 지향적인 앱: 장기적인 성공을 위한 확장성 고려 사항

Prepare seu aplicativo para o futuro: considerações de escalabilidade para sucesso a longo prazo

Готовность вашего приложения к будущему: соображения масштабируемости для долгосрочного успеха

Uygulamanızın Geleceğe Hazırlanması: Uzun Vadeli Başarı için Ölçeklenebilirlik Konuları

Chứng minh ứng dụng của bạn trong tương lai: Những cân nhắc về khả năng mở rộng để đạt được thành công lâu dài

让您的应用经得起未来考验：长期成功的可扩展性考虑因素

I. Introduction Web applications keep getting more advanced and multifaceted, providing users with tons of features. But this also makes them more vulnerable to performance problems, especially when handling lots of requests simultaneously. That can lead to slow response times, crashes, errors, and even downtime - all of which hurt the user experience and company reputation. To make sure web apps deliver high-quality service and meet user expectations, it's crucial to do load testing. Load testing simulates real user scenarios and measures how the app handles different load levels. It can identify and fix bottlenecks, optimize resources, improve scalability, and ensure reliability and availability. Mastering load testing for web apps takes understanding the basics, the process, tools, best practices, common pitfalls, and future trends. So, let's dive in and take a look. II. Load Testing Basics Before we get into the process and tools for load testing, let's first define what load testing is and the key metrics used. Definition Load testing simulates realistic user traffic to see how a web application handles varying load levels. It should be done on all the steps of the software lifecycle like development, integration, and deployment. The main goals are to: Verify that the app can handle expected and peak traffic without hurting performance. Find and fix bottlenecks that cause slow responses, crashes, errors, or downtime. Optimize resource use like CPU, memory, disk, and network by tuning configs. Improve scalability by determining max capacity and throughput, and how to increase them. Ensure reliability and availability by testing recovery from failures and consistent performance. Key Metrics To evaluate performance under load, we measure key metrics like: - how long requests take to process and respond. Affects user satisfaction so should be low. Response time - requests handled per unit of time. Indicates efficiency and capacity so should be high. Throughput - the percentage of failed requests. Indicates reliability so should be low. Error rate - resources consumed during testing like CPU, memory, etc. Should be within acceptable limits. Resource utilization III. The Load Testing Process The load testing process consists of four main steps: identifying workload and performance requirements, test scenarios and environment setup, test execution, and result analysis. Identifying Workload and Performance Requirements The first step in load testing is defining the expected performance and goals under different loads. This includes: - The minimum response time, throughput, etc. that should be met. Performance requirements - How users interact with the app. This covers user types, actions, locations, concurrency, arrival rate, think time, and more. Workload - The normal, peak, and stress loads that will be tested. Load levels These points should be realistic, based on business needs, user feedback, historical data, industry benchmarks, and other sources. Test Scenarios and Environment Setup The next step is designing a test plan and setting up the test environment to execute the load tests. The test cases should cover the most critical and frequently used functions, as well as the riskiest scenarios under load. They should align with the workload model and load levels. The test environment should mirror production as closely as possible in terms of hardware, software, network, configs, data, etc. It should include: - Generates and sends requests per the test cases and workload model. Load generator - Distributes requests across multiple app instances or servers. Load balancer - The app being tested. Application under test - Collects and displays performance metrics of the app and components during testing. Monitoring system Test Execution and Result Analysis The third step in load testing is to execute the test cases and analyze the results. The test execution should be done according to a predefined test plan that specifies the order, duration, frequency, and intensity of each test case. The test execution should also be monitored and controlled by adjusting the load levels, stopping, or restarting the tests, etc. The result analysis should be done by comparing the actual performance metrics with the performance requirements and identifying any deviations or anomalies. The result analysis should also include identifying and diagnosing the root causes of any performance issues, such as code defects, configuration errors, resource bottlenecks, etc. Test Reporting and Improvement The final step is reporting the findings and recommendations and implementing improvements based on the analysis. The test report should summarize the objectives, methodology, environment, execution, results, analysis, findings, recommendations, and conclusions. It should include supporting data like graphs, charts, tables, screenshots, logs, etc. Improvements may involve applying fixes or optimizations to the code, configs, architecture, design, etc. Changes should be verified by re-running load tests to validate their effectiveness. IV. Load Testing Tools A diverse array of tools exists for load-testing web applications, each with unique capabilities. , an open-source Java tool, enables load generation and performance measurement across various protocols like HTTP, WebSocket, and FTP. It provides recording, scripting, dynamic data simulation, result analysis, and more. Extensibility through plugins is a key benefit. JMeter by Micro Focus excels at load testing beyond just the web, with support for mobile, IoT, and media. Scripting uses languages like C and JavaScript. Visual analysis is facilitated through dashboards. LoadRunner , written in Scala, separates test logic from execution for scalability. Recording HTTP sessions and converting them to code accelerates test creation. Interactive charts empower insightful results analysis. Gatling leverages Python for load test scripting. Its master-slave architecture readily scales simulated users across multiple machines. An intuitive web interface displays live test metrics. Locust These tools and many others each have unique strengths. Careful selection based on the technology, test environment, metrics, integrations, and workflows to be assessed is advised. Thorough evaluation guides the way to the optimal choice. V. Best Practices and Common Pitfalls Mastering the art of load testing requires adhering to key best practices while avoiding frequent missteps. To conduct effective load testing: Set clear, realistic performance targets based on business needs, user feedback, historical data, and industry benchmarks. Don't define criteria that are unattainably high or low; Design comprehensive test cases that cover critical functions and probable high-risk scenarios under load. Align cases to workload models and load levels; Pay special attention to possible peak load scenarios: new marketing campaign planned and a lot of new users expected? Make sure to test the registration and login parts. Black Friday coming? Check scenarios for huge simultaneous checkouts; Build a test environment that closely resembles production in terms of hardware, software, network, configs, and data. Isolate it from anything that could skew results; Use capable, reliable load-testing tools that can simulate workflows and measure metrics accurately per the technology stack. Avoid inappropriate or unstable tools; Make sure test servers and bandwidth can generate the load required for testing. Using one request-generating server to test 100 servers might not generate the required load that would result in incorrect test results; Execute load tests per a systematic plan defining order, duration, frequency, and intensity. Monitor and adjust load levels carefully during execution; Analyse results by comparing metrics to criteria and diagnosing issues through data visualization and statistics. Avoid raw data or unclear graphs; Implement optimizations, then re-test to validate improvements. Don't deploy changes without verifying effectiveness. Common pitfalls to avoid: Inaccurate performance targets leading to false positives or negatives; Incomplete test cases missing critical functions and scenarios; Unrealistic test environments that skew metrics due to differences from production; Poorly matched tools that can't simulate workflows, generate required load, or measure metrics properly; Lack of execution plan causing disorganized, inefficient test runs; Weak analysis lacking visualization and statistics to surface meaningful insights; Failure to re-test and validate the impact of optimizations before deployment. VI. Future Trends in Load Testing Load testing continues to evolve as web development advances. Key emerging trends include: leverages scalable cloud computing services like and for benefits such as flexibility, cost savings, and geographic reach. The cloud enables large-scale distributed testing. Cloud-based load testing BlazeMeter Grafana Cloud k6 are transforming load testing through intelligent automation. AI can generate realistic workloads, predict metrics, diagnose issues, and recommend solutions. Tools like , , and incorporate these technologies. Artificial intelligence and machine learning Dynatrace Neoload Apica helps identify and prevent vulnerabilities under load. , and are examples of WAST tools that can detect risks like denial-of-service attacks and cross-site scripting. Integrating web application security testing (WAST) OWASP ZAP , Nmap Burp Suite Mastering these emerging practices will be key for load testing excellence as the web evolves. Forward-looking organizations are already putting them into practice. VII. Conclusion Mastering load testing is critical for releasing high-performing web applications. It requires blending technical and analytical expertise to model realistic user behaviors, select appropriate tools, develop comprehensive test cases, conduct controlled experiments, and extract meaningful performance insights. As technology evolves, agile adaptation alongside diligent mastery of load testing fundamentals will remain key. By building robust in-house competencies or working with experienced partners, organizations can gain a significant competitive advantage through expertise in this vital discipline. References Top 15 Open-Source Load Testing Software 2023 | TestGuild 9 Load Testing Best Practices (Don't Make These Mistakes) (testguild.com) Load testing best practices  |  Cloud Run Documentation  |  Google Cloud What is Load Testing: Process, Tools, & Best Practices | BrowserStack Load Testing Best Practices (multiple.dev)

Mastering the Art of Load Testing for Web Applications

Brief overview of Apache Kafka and common use cases, current tools to scale multi-cluster deployments, and connectivity solutions to simplify multi-cluster deployments. Table of Contents What is Kafka? Kafka and Kubernetes The Case for Multi Cluster Kafka Multi Cluster Kafka Stretched Clusters - Synchronous Replication Connected Clusters - Asynchronous replication Conclusion What is Kafka? Commonly known simply as , Apache Kafka is an open-source event streaming platform maintained by the Apache Software Foundation. Initially conceived at , Apache Kafka was collaboratively created by , and , and subsequently released as an open-source project in 2011. Kafka LinkedIn Jay Kreps Neha Narkhede Jun Rao Wiki Page Today, Kafka is one of the most popular event streaming platforms designed to handle real-time data feeds. It is widely used to build scalable, fault-tolerant, and high-performance streaming data pipelines. Kafka's uses are continually expanding, with the top 5 cases nicely illustrated by in the accompanying image. Brij Pandey As a brief primer, it is important to understand the components of the Kafka platform and how they work.. Kafka works as a distributed event streaming platform, designed to handle real-time data feeds efficiently. It operates based on the publish-subscribe messaging model and follows a distributed and fault-tolerant architecture. It maintains a persistent, ordered, and partitioned sequence of records called "topics." Producers write data to these topics, and consumers read from them. This enables decoupling between data producers and consumers and allows multiple applications to consume the same data stream independently. Key components of Kafka include: Kafka organizes data into topics. Each topic is a stream of records, and the data within a topic is split into multiple partitions. Each partition is an ordered, sequence of records. Partitions enable horizontal scalability and parallelism by allowing data to be distributed across multiple Kafka brokers. Topics and Partitions: immutable : Producers are applications that write data to Kafka topics. They publish records to specific topics, which are then stored in the topic's partitions. Producers can send records to a particular partition explicitly or allow Kafka to determine the partition using a partitioning strategy. Producers : Consumers are applications that read data from Kafka topics. They subscribe to one or more topics and consume records from the partitions they are assigned to. Consumer groups are used to scale consumption, and each partition within a topic can be consumed by only one consumer within a group. This allows multiple consumers to work in parallel to process the data from different partitions of the same topic. Consumers : Kafka runs as a cluster of servers, and each server is called a broker. Brokers are responsible for handling read and write requests from producers and consumers, as well as managing the topic partitions. A Kafka cluster can have multiple brokers to distribute the load and ensure fault tolerance. Brokers : To achieve fault tolerance and data durability, Kafka allows configuring replication for topic partitions. Each partition can have multiple replicas, with one replica designated as the leader and the others as followers. The leader replica handles all read and write requests for that partition, while followers replicate the data from the leader to stay in sync. If a broker with a leader replica fails, one of the followers automatically becomes the new leader to ensure continuous operation. Partitions/Replication : Kafka maintains the concept of offsets for each partition. An offset represents a unique identifier for a record within a partition. Consumers keep track of their current offset, allowing them to resume consumption from where they left off in case of failure or reprocessing. Offset Management : While not part of Kafka itself, ZooKeeper is often used to manage the metadata and coordinate the brokers in a Kafka cluster. It helps with leader election, topic and partition information, and managing consumer group coordination. [Note: ] ZooKeeper Zookeeper metadata management tool, will soon be phased out in favor of , or KRaft, a protocol for internally managed metadata Kafka Raft Overall, Kafka's design and architecture make it a highly scalable, fault-tolerant, and efficient platform for handling large volumes of real-time data streams. It has become a central component in many data-driven applications and data infrastructure, facilitating data integration, event processing, and stream analytics. A typical Kafka architecture would then be as follows: Kafka clustering refers to the practice of running multiple Kafka brokers together as a group to form a Kafka cluster. Clustering is a fundamental aspect of Kafka's architecture, providing several benefits, including scalability, fault tolerance, and high availability. A Kafka cluster is used to handle large-scale data streams and ensure that the system remains operational even in the face of failures. In the cluster, Kafka topics are divided into multiple partitions to achieve scalability and parallelism. Each partition is a linearly ordered, immutable sequence of records. Partitions therefore allow data to be distributed across multiple brokers in the cluster. It should be noted that a minimum Kafka cluster consists of 3 Kafka brokers, each of which can be run on a separate server (virtual or physical). The 3 node guidance is to help avoid a split brain scenario in case of a broker failure. Kafka and Kubernetes As more companies adopt Kafka, there is also an increasing interest in deploying Kafka on Kubernetes. In fact, the most recent by Dynatrace shows that over 40% of large organizations run their open source messaging platform within - the majority of this being Kafka. Kubernetes in the Wild report 2023 Kubernetes . Source The same report also makes a bold claim , that “Kubernetes is emerging as the ‘operating system’ of the cloud.” It is imperative then, for Kafka administrators to understand the interplay between Kafka and Kubernetes, and how to implement these appropriately for scale. The Case for Multi Cluster Kafka Running a Kafka cluster in a single Kubernetes cluster setup is fairly straightforward and enables scalability as needed in theory. In production however, the picture can get a bit murky. We should distinguish the use of the term between Kafka and Kubernetes. A Kubernetes deployment also uses the term to designate a grouping of connected nodes, referred to as Kubernetes cluster. When the Kafka workload is deployed on Kubernetes, you will end up with a Kafka cluster running inside a Kubernetes cluster, but more relevant to our discussion, you may also have a Kafka cluster that spans multiple Kubernetes clusters - for resiliency, performance, data sovereignty etc. cluster cluster To begin with, Kafka is not designed for multi-tenant setups. In technical terms, Kafka does not understand concepts such as Kubernetes namespaces or resource isolation. Within a particular topic, there is no easy mechanism to enforce security access restrictions between multiple user groups. Additionally, different workloads may have different update frequency and scale requirements eg batch application vs. real-time application. Combining the two workloads into a single cluster could cause adverse impacts or consume much more resources than necessary. Data sovereignty and regulatory compliance can also impose restrictions on co locating data and topics in a specific region or application. Resiliency of course is another strong driving force behind the need for multiple Kafka clusters. While Kafka clusters are designed for fault tolerance of topics, we still have to plan for a catastrophic failure of an entire cluster. In such cases, the need for a fully replicated cluster enables proper business continuity planning. For businesses that are migrating workload to the cloud or have a hybrid cloud strategy, you may want to set up multiple Kafka clusters and perform a planned workload migration over time rather than a risky full scale Kafka migration. These are just a few of the reasons why in practice, enterprises find themselves having to create multiple Kafka clusters that nevertheless need to interact with each other. Multi Cluster Kafka In order to have multiple Kafka clusters that are connected to each other, key items from one cluster must be replicated to the other cluster(s). These include the topics, offsets and metadata. In Kafka terms, this duplication is considered Mirroring. There are two approaches of multi-cluster setups that are possible. or Stretched Clusters Connected Clusters. Stretched clusters - Synchronous replication A stretched cluster is a logical cluster that is ‘stretched’ across several physical clusters. Topics and replicas are distributed across the physical clusters, but since they are represented as a logical cluster, the applications themselves are not aware of this multiplicity. Stretched clusters have strong consistency and are easier to manage and administer. Since applications are unaware of the existence of multiple clusters, they are easier to deploy on stretched clusters, compared to connected clusters. The downsides of stretched clusters are that it requires a synchronous connection between the clusters. They are not ideal for a hybrid cloud deployment, and will require a quorum of at least 3 clusters to avoid a ‘split-brain’ scenario. Connected Clusters - Asynchronous replication A Connected Cluster on the other hand, is deployed by connecting multiple independent clusters. These independent clusters could be running in different regions or cloud platforms and are managed individually. The primary benefit of the connected cluster model is that there is no downtime in cases of a cluster failure, since the other clusters are running independently. Each cluster can also be optimized for its particular resources. The major downside of connected clusters is that it relies on asynchronous connection between the clusters. Topics that are replicated between the clusters are not ‘copy on write’ but rather , depend on This can lead to possible data loss during the async mirroring process. eventual consistency. Additionally, applications that work across connected clusters have to be modified to be aware of the multiple clusters. Before we address the solution to this conundrum, I’ll briefly cover the common tools on the market to enable Kafka cluster connectivity. Open Source Kafka itself ships with a mirroring tool called Mirror Maker. Mirror Maker duplicates topics between different clusters via a built in producer. This way data is cross replicated between clusters with eventual consistency, but without interrupting individual processes. It is important to note that while Mirror Maker is simple in its concept, setting up Mirror Maker at scale can be quite a challenge for IT organizations. Managing IP addresses, naming conventions, number of replicas etc. must be done correctly or it could lead to what is known as ‘infinite replication’ where a topic is infinitely replicated, leading to eventual crash. Other downsides of Mirror Maker is the lack of dynamic configuration of allowed/disallowed lists for updates. Mirror Maker also does not sync topic properties properly, which makes it an operational headache at scale when adding or removing topics to be replicated. Mirror Maker 2 attempts to fix some of these challenges but many IT shops still struggle to get Mirror Maker set up correctly. Other Open Source tools for Kafka replication include Mirus from Salesforce, uReplicator from Uber and customised Flink from . Netflix For commercial licensed options, Confluent offers two options, Confluent Replicator and Cluster Linking. Confluent Replicator is essentially a Kafka Connect connector that provides a high-performance and resilient way to copy topic data between clusters. Cluster Linking is another offering, developed internally and is targeted at multi region replication while preserving topic offsets. Even so, Cluster Linking is an asynchronous replication tool with data having to cross network boundaries and traverse public traffic pathways.As should be clear by now, Kafka replication is a crucial strategy for production applications at scale, the question is which option to choose. Imaginative Kafka administrators will quickly realize that you may need connected clusters and stretched clusters, or a combination of these deployments, depending on the application performance and resiliency requirements. What is daunting however, is the exponential challenges of setting up the cluster configurations and managing these at scale across multiple clusters. What is a more elegant way to solve this nightmare? by Avesha is a simple way to get the best of both worlds. By creating a direct between clusters or namespaces, KubeSlice obviates the need for manually configuring individual connectivity between Kafka clusters. KubeSlice Service Connectivity At its core, creates a secure, synchronous Layer 3 network gateway between clusters; isolated at the application or namespace level. Once this is set up, Kafka administrators are free to deploy Kafka brokers in any of the clusters. KubeSlice Each broker has a synchronous connectivity to every other broker that is joined via the slice, even though the brokers themselves may be on separate clusters. This effectively creates a stretched cluster between the brokers and  provides the benefit of a strong consistency, and low administration overhead. Have your cake and eat it too ! For those that may want to deploy Mirror Maker into their clusters, this can be done with minimal effort since the connectivity between the clusters is delegated to KubeSlice. Thus, Kafka applications can have the benefits of synchronous (speed, resiliency) AND asynchronous (independence, scale) replication in the same deployment with the ability to mix and match the capabilities as needed. This is true of on-prem data centers, across public clouds or any combinations of these in a hybrid setup. The best part is that KubeSlice is a non-disruptive deployment, meaning that there is no need to uninstall any tool already deployed. It is simply a matter of establishing a and adding the Kafka deployment onto that . slice slice Conclusion This blog provided a brief overview of Apache Kafka and has touched on some of the more common use cases. We covered the current tools available to scale Kafka deployments across multiple clusters and discussed the advantages / disadvantages of each. Finally, the article also introduced Kubeslice - the emerging service connectivity solution that simplifies Kafka multi-cluster deployments and removes the headaches associated with configuring Kafka replication across multiple clusters at scale. A couple of links that readers may find useful: (before KubeSlice was introduced) An older blog of best practices running Kafka on AWS Guided setup of KubeSlice Deploying Kafka on GKE Also published here.

Mastering Multi-Cluster Deployment and Replication with Kafka

কাফকার সাথে মাল্টি-ক্লাস্টার স্থাপনা এবং প্রতিলিপি আয়ত্ত করা

Beherrschen Sie die Multi-Cluster-Bereitstellung und -Replikation mit Kafka

Dominar la implementación y replicación de múltiples clústeres con Kafka

Maîtriser le déploiement et la réplication multi-clusters avec Kafka

काफ्का के साथ मल्टी-क्लस्टर परिनियोजन और प्रतिकृति में महारत हासिल करना

Kafka を使用したマルチクラスターのデプロイメントとレプリケーションをマスターする

Kafka로 멀티 클러스터 배포 및 복제 마스터하기

Dominando a implantação e replicação de vários clusters com Kafka

Освоение многокластерного развертывания и репликации с помощью Kafka

Kafka ile Çok Kümeli Dağıtım ve Çoğaltmada Uzmanlaşma

Làm chủ việc triển khai và nhân rộng nhiều cụm với Kafka

使用 Kafka 掌握多集群部署和复制

DevOps stems from the strive to make software development better and faster. The practice of DevOps unites development and operations teams and improves collaboration between them, with the help of different software tools. What Is DevOps? DevOps is a bunch of practices that strive to bring software development (Dev) together with software operations (Ops). DevOps is also defined by the high level of automation and monitoring at all stages of the development process. The software development lifecycle becomes continuous – from planning, coding, building, and testing to deploying and monitoring. DevOps includes different daily practices, like continuous development, communication and collaboration, continuous integration, continuous delivery, continuous deployment, microservices, and continuous monitoring. What Is a DevOps Toolset? The plethora of tools used for DevOps is called the DevOps toolset. There are different tools that teams usually use for the different DevOps practices. For example: - Continuous development GitHub - Continuous development, Communication and Collaboration Jira - Continuous development, Communication, and Collaboration Azure DevOps - Continuous integration, Continuous delivery, Continuous deployment Jenkins - Continuous integration, Continuous deployment Circle CI - Continuous Delivery, Microservices Kubernetes – Microservices, Continuous monitoring Prometheus - Continuous monitoring Splunk - Continuous monitoring Dynatrace These software tools are usually leveraged in combination with each other, and different DevOps, IT, and service desk teams need to use them daily. What Are the Benefits of Integrating Your DevOps Tools? What happens when all these tools are scattered and do not work in sync? Engineers need to transfer data manually from one tool to another. Repetitive manual tasks lead to delays, faulty data, and errors. Important details might be omitted, feedback and action points are not clear, and the overall work process becomes slower and less efficient. Integrations between the multiple tools that are part of the DevOps toolset, on the other hand, bring numerous benefits like: Deploying more frequently and with higher success Fixing bugs earlier and faster Feedback is received in real-time and product quality is improved Faster time-to-market Boosted customer satisfaction Higher productivity and efficiency levels Improved communication and cross-team collaboration Eliminating errors and delays caused by manual transfer of data Streamlined development process The practices of continuous testing and integration, combined with an integrated DevOps toolset, make changes small and reversible, collaboration easier, and feedback instant. This streamlines problem-solving and makes it easier to deploy. DevOps tools integration significantly shortens the time from idea to production and brings lots of competitive advantages. What Are the Challenges of Integrating the DevOps Tools? There are numerous integration solutions, including various add-ons and connectors that make integrations between the DevOps tools. However, many of these integrations are perfunctory and do not meet some basic criteria. Add-ons and apps that connect different software tools often cannot bear complex use cases and specific requirements. Internal integrations, on the other hand, usually take more time and resources than expected and are difficult to predict. Of course, there are also integration platforms that are more sophisticated and allow precise filtering and mapping to fit the requirements and integration scenarios of each organization. Here is what to look for, when choosing an integration tool for your DevOps toolset. – Once you set and launch your integration, this does not mean it is set in stone. Most probably the needs of your organization and teams will grow or change with time. The integration tool needs to be able to accommodate the changes. Scalability – Security is one of the most important things nowadays. Have a thorough check of the security capabilities of the integration tool before you go for it. Security – How easy it is to control and manage your integrations? Does it require a developer or another type of expert to be dedicated to the integration management? Ease of use – How long will it take before your integration is ready and starts working? With some tools, it might take months, and with others, several weeks. Consider your terms. Time to launch – Some integration tools can be very easy and fast to set up, but they cannot handle specific requirements. Check if the tool can handle your precise scenario. Can it handle your use case? – Will you receive adequate and timely support in case you need it? Support – If the integration tool is a plugin, it usually requires changes and specific settings inside your DevOps toolset. This might affect performance, security, updates, etc. If the integration tool is a standalone one, it will not require any changes within your software tools. Is it a plugin – Connecting more than 2 tools – is the integration tool capable of integrating your whole DevOps toolset? Complex integrations It is not easy to find the perfect integration tool, but it pays off. If you want to reap all the benefits of integrating your DevOps toolset, you need to invest some time in choosing the best integration method. How Do We Bring the DevOps Toolset Together? Automation is the basis for creating a DevOps culture. Many experts claim that DevOps is a bunch of practices. However, these practices cannot exist without the tools DevOps teams are using daily. There are numerous tools that help design, build, test, manage, monitor, and deploy software. Unfortunately, these tools are usually brought by different vendors and don’t work very well together. In these cases, the way to synchronize the DevOps toolset is by a seamless integration, through the right integration solution. For example, one team can use Jira for planning and tracking tasks, and another team can use Azure DevOps to manage projects and workflows. On the other hand, they might use Kubernetes for individual development environments, Jenkins for continuous integration, GitHub to track code versions, etc. To achieve full coordination in your DevOps toolset, you would need multiple integrations. A good integration tool can connect all these systems and have them transfer the desired data between one another in real time. Integration eliminates the need for teams to learn new tools all the time, log in to multiple systems, and transfer data manually from one tool to another. This saves a significant amount of time for team members and allows them to focus on important tasks. Integration of the systems from the DevOps toolset also allows much more stable automation. Of course, the security and stability of the integrations depend on the integration platform you choose. There are platforms that guarantee high security, protect you in cases of downtime, and have advanced retry mechanisms. Continuous feedback improves the quality and delivery of software, and this is the main idea of the DevOps culture. Integrating all the systems the DevOps teams are using enables continuous feedback, as everyone gets relevant information in real time. There are multiple integration methods and platforms, but the best one would be defined from your specific needs and scenario. Let’s see some examples that illustrate how DevOps is boosted significantly by integrations. Use Cases and Examples Example 1: Let’s say the DevOps team in an organization is using Jira to manage all their tasks and projects. The IT service desk team, on the other hand, is using ServiceNow to handle tickets and requests. The DevOps engineers use GitHub to track versions and code changes. The service desk team receives a new ticket. They investigate the issue and find out that it is caused by an existing bug. They need to transfer the ticket data to the development team working on the bug. If ServiceNow and Jira are not integrated, this would need to happen manually, or by communicating the details via phone, chat, or email. This process is prone to errors, incomplete data, and delays. On the other hand, if there is a Jira ServiceNow integration, the details will automatically be transferred from the ServiceNow ticket to the corresponding Jira task in real time. The developer working on the issue will have the data immediately. If there is a Jira GitHub integration, new details will also be automatically added from Jira to GitHub and will easily be tracked. When the integration is bi-directional, the updates in GitHub will be reflected in Jira right away. Once the issue is resolved, this will be logged in ServiceNow and the client will be notified. Communication and collaboration between team members is one of the most important things in DevOps. For someone trying to quickly respond to a customer incident, for example, it would be quite challenging to transfer all the details manually between systems and wait for someone to respond, especially if the right person is not available or not online at the moment. As we can see, with integration in place, we have quick updates to issues, real-time feedback, and questions answered in a timely manner, as the right data reaches the right team members immediately. With teams working remotely and even in different time zones, collaboration is very difficult without integrating the DevOps tools they are using. Example 2: Monitoring and observability are another important part of the DevOps process. With the help of monitoring tools, performance problems, issues, and failures can be identified before they become critical. Some examples of great monitoring tools used by DevOps teams are Prometheus, Splunk, Nagios, Dynatrace, Datadog, and more. When the monitoring tools are integrated with the other systems the DevOps engineers use, data about any failures is received instantaneously by the right people in the team. This way issues are resolved much faster, and customers are happier. Let’s imagine the DevOps team in an organization is using Azure DevOps to handle and organize their projects. They are also using CircleCI to build and automate pipelines. Finally, they have Datadog to monitor and track metrics, databases, etc. If there is an integration between the three tools, pipelines in CircleCI that are related to tasks in Azure DevOps can be automatically updated with any new details. Keeping information in both places helps the DevOps team process the pipelines faster. On the other hand, the DevOps engineers can receive key CircleCI metrics, like the number of failed builds or the average build time, directly into Datadog. This way, they have everything connected, and the work process is highly optimized. Example 3: If the DevOps team in an organization is using Jira, but the team of a partner or a customer is using Azure DevOps, the collaboration between the two teams can be greatly enhanced with integration. A good integration platform can limit the transferred data by different criteria. It can be filtered by user groups, statuses, or even more complex conditions. This way, even integrating the systems of the company with a third party can be safe and effective. When the customer has an issue that needs to be resolved, if there is no integration between Jira and Azure DevOps, they need to make calls or send emails. This takes time, and the details provided might be incomplete or even false. The process causes delays and silos. With an integration in place, the details will be immediately available in the Jira instance of the respective team. They can start working on it right away. The issue will be resolved much faster, making the client happy. There are various scenarios and examples, and they are unique for each organization. This is why the integration solution used needs to be flexible and scalable, as well as stable and secure. Of course, when choosing an integration tool, you need to keep in mind the specific requirements and situations of your teams and organization. Conclusion Cross-team collaboration and good communication are among the most essential parts of DevOps. However, teams and team members nowadays are rarely in the same room. Typically, engineers work remotely and might even be scattered around the globe. The software tools that they use enable collaboration between them. Effective workflows and smart automation improve customer service and ease the work of developers and IT specialists. А smooth flow of data between systems and departments will eliminate the need for repetitive manual tasks and logging to multiple systems. When the DevOps tools used daily are integrated with one another, this creates effective workflows, speeds up issue resolutions, and makes the DevOps process smooth and productive. You can use integration solutions and comprehensive integration platforms to achieve full coordination in your DevOps toolset and get an unprecedented competitive edge.

Reinforce Your DevOps Toolset With Integrations – A Full Breakdown

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Let's learn about via these 40 free stories. They are ordered by most time reading created on HackerNoon. Visit the to find the most read stories about any technology. Ruby On Rails Development /Learn Repo 1. How to Install Bootstrap 5 on Rails 6 Struggling with installing Bootstrap 5 on Rails 6 application? This article will help you. 2. Building Rails Carousels Using Bootstrap Bootstrap carousel with rails 3. The 7 Best Ruby Frameworks for Web Development in 2022 While Ruby on Rails development services are highly demanded, there are other Ruby frameworks as well. Here are the best 7 Ruby frameworks for web development. 4. How to Deploy a Ruby on Rails App on Heroku The most satisfying thing beyond building something is to make it available to the world. Heroku is great for beginners because it’s a free and “simple” push-to-deploy system. 5. How to Set Up Ruby On Rails on Windows 10 Once upon a time, installing Ruby on Windows came with a myriad of problems (at least my search history can attest to that), but things have come a long way. 6. You Need to Know What is erb in Rails and How to Master it One of the first things that made me uncomfortable about learning Rails was taking a look at the views. I found some weird files with some weird syntax. Their name was something like ‘name.html.erb’ and I was like what? Why does this file which seems to be an HTML file with kind of HTML syntax has a .erb extension after the .html extension? 7. "Rewrite your Software," says Creator of Ruby on Rails David Heinemeier Hansson is the creator of Ruby on Rails, co-founder & CTO of Basecamp, best-selling author, Le Mans class-winning race car driver, family man, frequent podcast guest, and inspirational conference speaker. 8. How to Use Hotwire in Rails 7 to Build a Real Time Notification System This is a quick update of an article on how to build a notifications system with Rails and Redis, now using Hotwire 9. Is Ruby Installed On A Mac? How To Check On macOS Catalina and Big Sur Ruby comes installed on macOS Catalina and Big Sur. Learn how to check for the system Ruby on your Mac. 10. An Intro to Data Tables On Rails Let me help fellow developers out by opening the gates to data tables with Ruby on Rails. 11. Why Ruby Shall Still Be Relevant in 2021 Ruby and the Rails framework are both under active development and regular updates are released for both all year round 12. Why I Detest Programming (Even After Decades as a Software Engineer) Why are we addicted to programming? 13. All For One, And One For All: A Single Ruby on Rails Validator For All Controllers 14. Rails Migrations Are Not Scary [Beginners Guide] I am a full-stack developer student currently doing the Microverse program, and I've been developing things for months until now. This article is for helping newcomers to Rails to understand Rails migrations by showing some examples and explaining them the best I can. 15. Mistakes I've made treating file paths as strings Some things you do as a developer can work for you for years, then turn around and bite you when you were least expecting. These are the things that you wish another developer had told you early in your career so you never had to make the mistakes. This post is about one of those things and if you’re reading this, consider it me telling you. 16. Top Skills and Capacities of a Ruby-on-Rails Developer RoR is quite a unique thing in itself. Imagine a nugget that is hard to find, but when you get it, the payoff is worth the effort. And if Ruby is a gem, then RoR is a cutting tool to make it a perfect framework for all sorts of web applications thanks to vast time-efficiency and code reuse factors. 17. How to Manage Multiple Databases in Rails 6 With the launch of Rails 6, one of the new features that were announced was of multiple database support. 18. Redis Can Help You Deal With Tough Calculations for Geospatial Data Easily Learn how to use Redis and its geospatial indexes and commands for complex geo calculations. 19. Getting Your Ruby on Rails Application Live On The Internet With Heroku [A How-To Guide] As a back-end developer, the happy moment is to show your client that the work is now is live on the internet, everyone on the world can access the website. In this article, I will share my experience step by step to deploy your ruby on rails application on Heroku. 20. How to Write Your First Model Tests Using RSpec in Rails Applications Latest August of this year I started to learn Rails. I was following the book Ruby on Rails Tutorial from Learn Enough To Be Dangerous website which uses Minitest to test their application.  Truth is: I was not understanding a single line of code about those tests. So I decided to skip them and try to write it using RSpec because the syntax is way more similar to human language than others. But you may be wondering: 21. Assure Superb Quality of Ruby On Rails App Through Code Auditing This article is all about code auditing and how it helps in refining application code. It also explains the different types of code reviews and how they are use 22. A Dive into Active Record Connection Pools and Horizontal Sharding in a Gem Rails is a great framework that enables us to avoid grungy but essential topics like database connection pooling with all the inherent complexities of multi-threading and error handling. 23. Get Unused Routes of Large Rails App 🛠 After a few years of continuous development, your rails application becomes larger and it’s good practice to do some cleanup. One of the most obvious cleanups is cleaning up unused routes. 24. Live Updates in Ruby on Rails with ActionCable Feature There are times when one as a Ruby on Rails developer wants to implement a real-time feature, like a chat application, and after digging a bit you find a framework's feature named ActionCable, sounds new and scary, right? Fear not, I'll try to explain it as simple as possible so that at the end of this article you'll feel comfortable with the subject. 25. Testing  on Ruby on Rails [A How-To Guide] The testing stage is a very important step in the software development life-cycle of any application. It helps software developers detect and fix bugs much earlier in the development process. 26. Speed ​​Up Your Development Time With Queries in Ruby on Rails Whether you have made simple queries with SQL language to get records in some relational database or you have experience as a complex database administrator, when starting it might seem extremely difficult to learn the syntax to select only what you require, or which table to join with which to make the necessary consultation. 27. Open-sourcing Ferrum: a fearless Ruby Chrome driver If you want to run integration tests on your website, you have three options: Poltergeist, Selenium and now, a new secret weapon — Ferrum. 28. How to Build a Responsive Rails App with Hotwire Building single page application with Ruby on Rails? Check out Hotwire. It utilizes server side rendering to send HTML over the wire. by Aleksandr Ulanov 29. CarrierWave Upload Multiple Images When you are building a web application, you definitely want to add an option for image uploading as well. In this tutorial, we will show you the necessary steps to enable upload multiple images/files using CarrierWave in Rails 4+ from scratch. A similar procedure can be applied for nested forms. 30. How to Call a Method in Ruby: 12 Different Ways Ruby is one of the more flexible and versatile language. In this short experiment I come up with different ways to call the same method in Ruby. 31. The $5 Billion DevOps Stranglehold Ten years ago NewRelic, DataDog, Splunk, Dynatrace and SolarWinds built tools we loved to use. They were easy to implement and solved problems quickly and efficiently. Each company was known primarily for a single, well-conceived product. NewRelic’s APM. Splunk’s log file analyzer.  DataDog’s server monitor. SolarWinds’ network performance monitor. These companies were beloved by users during the 2000s. 32. Is Ruby on Rails Declining? Not If You Build It Is ruby on rails still used? absolutely. There is a list of reasons from many startups and a direct review from developers. Take a look at this article hurry! 33. Interview with Yukihiro Matsumoto: Ruby is Designed for Humans, not Machines Introduction 34. Design a SaaS application on Rails 6.1 with Horizontal Sharding Designing a multi-tenant application on Rails with horizontal sharding 35. Meeting the Query Interface in Ruby on Rails Ruby on Rails has many in built methods, it's in its nature; at some point it can create some sort of stigma due to its, well known, slowness performance, but several of those methods already can help you to write a clean and faster code. 36. The Good and The Bad of Ruby Development 37. Ruby on Rails is Not Dead! Here's How to Upgrade Ruby Legacy Applications Have you wondered how to modernize your Ruby on Rails? Read the owner’s guide for upgrading legacy software and learn why Ruby is still a good choice in 2021. 38. An Introduction to Backtracking in Ruby in this blog post, we are going to take a look at what is backtracking and how to implement it using ruby 39. Take a Ride on the Ruby Rails Express — Understanding The MVC Architecture A quick story explaining the Ruby on Rails MVC architecture. You find yourself at the Ruby on Rails train station exploring the MVC architecture. 40. Setting Up Ruby on Rails On Github Using CircleCI [A How-To Guide] This is the ultimate guide for your next Ruby on Rails project setup on Github with CircleCI for continuous integration in 2020. Thank you for checking out the 40 most read stories about Ruby On Rails Development on HackerNoon. to find the most read stories about any technology. Visit the /Learn Repo

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