Istanbul Spring Meetup - Migrating SpringBoot Apps from Annotation-based Config to Functional with Kotlin
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This document discusses migrating Spring Boot applications from annotation-based configuration to functional configuration using Kotlin. It notes that Kofu configuration for Spring Boot uses an explicit Kotlin DSL for configuration, avoids classpath detection and annotations, and provides faster startup times and lower memory usage compared to annotation-based configuration. Takeaways include using Kotlin beans and routes DSLs to define controllers and logic.
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A couple of years ago, continuous integration in the JVM ecosystem meant Jenkins. Since that time, a lot of other tools have been made available. But new tools don’t mean new features, just new ways. Besides that, what about continuous deployment? There’s no tool that allows deploying new versions of a JVM-based application without downtime. The only way to achieve zero downtime is to have multiple nodes deployed on a platform, and let that platform achieve that e.g. Kubernetes.
And yet, achieving true continuous deployment of bytecode on one single JVM instance is possible if one changes one’s way of looking at things. What if the compilation could be seen as changes? What if those changes could be stored in a data store, and a listener on this data store could stream those changes to the running production JVM via the Attach API?
In this talk, we'll demo exactly that using Hazelcast and Hazelcast Jet - but it’s possible to re-use the principles that will be shown using other streaming technologies.
Zero-downtime deployment on Kubernetes with Hazelcast
Kubernetes allows a lot. After discovering its features, it’s easy to think it can magically transform your application deployment process into a painless no-event. For Hello World applications, that is the case. Unfortunately, not many of us do deploy such applications day-to-day because we need to handle state. Though it would be much easier to have stateless apps, and despite our best efforts in this direction, state is found in (at least) two places: sessions and databases.
You need to think keeping the state while stopping and starting application nodes. In this talk, I’ll demo how to update a Spring Boot app deployed on a Kubernetes cluster with a non-trivial database schema change with the help of Hazelcast, while keeping the service up during the entire update process.
Orchestrate Your End-to-end Mainframe Application Release Pipeline
What steel and concrete are to a skyscraper, the mainframe is to the global economy. The mainframe is the transactional backbone for 96 of the world’s top 100 banks, 23 of the 25 top US retailers and 9 out of 10 of the world’s largest insurance companies.
When you think of a mainframe, you probably think of an old green computer screen. Did you know you can use the same modern tools and techniques with mainframes that you use with cloud and mobile?
With the growth of mission-critical mainframe workloads showing no signs of slowing down, application delivery cannot remain slow and complex. Organizations must apply the same DevOps processes to the mainframe as they do with other platforms.
Compuware and XebiaLabs enable large enterprises to automatically build, test and deploy mainframe releases within a cross-platform application release pipeline.
JavaDay Istanbul - 3 improvements in your microservices architecture
While a microservices architecture is more scalable than a monolith, it has a direct hit on performance.
To cope with that, one performance improvement is to set up a cache. It can be configured for database access, for REST calls or just to store session state across a cluster of server nodes. In this demo-based talk, I’ll show how Hazelcast In-Memory Data Grid can help you in each one of those areas and how to configure it. Hint: it’s much easier than one would expect.
XP Days Ukraine - Zero-downtime deployment with Kubernetes, Spring Boot and F...
Kubernetes allows a lot. After discovering its features, it’s easy to think it can magically transform your application deployment process into a painless no-event. For Hello World applications, that is the case. Unfortunately, not many of us do deploy such applications day-to-day.
You need to think about application backward compatibility, possible rollback, database schema migration, etc. I believe the later is one of the biggest pain point. In this talk, I’ll demo how to update a Spring Boot app deployed on a Kubernetes cluster with a non-trivial database schema migration with the help of Flyway, while keeping the service up during the entire update process.
Bootstrap4XPages
The document provides information about Bootstrap4XPages, a plugin that brings the Bootstrap front-end framework to XPages applications. It discusses what Bootstrap is, the benefits of using it, and how the Bootstrap4XPages plugin works. The plugin provides Bootstrap styling for XPages components out of the box. It can be installed on Domino servers and in Designer. The document demonstrates how to enable Bootstrap styling on an XPages application by selecting the Bootstrap4XPages library and theme. It also covers creating layouts, views, dialogs, and other application elements using Bootstrap styled XPages components. Custom themes and upcoming features are mentioned. Contributions to the open source project are welcomed.
Create a copy of your SharePoint farm with SPDocKit and AutoSPInstaller
SPDocKit helps you generate an XML configuration file that you can use with AutoSPInstaller to create a new SharePoint farm that is the same as or similar to your current farm environment.
The configuration file reduces the time needed to deploy SharePoint, and since everything is reusable, you can create production and staging/testing environments that are identical.
Achieving end-to-end visibility into complex event-sourcing transactions usin...
Event-sourcing systems usage like Kafka is growing rapidly among Node.js applications. Building systems around an event-driven architecture simplifies horizontal scalability in distributed computing models and makes them more resilient to failure. With these advantages, we face new challenges - how to get visibility into these complex processes.
Event-driven architecture is async by nature. Tracking the communication between different components is both extremely difficult and important when debugging or figuring out bottlenecks in the system.
In this talk, I will present ways to achieve end-to-end and granular visibility into complex event-sourcing transactions using distributed tracing. I will use open-source tools like OpenTelemetry, Jaeger, and Zipkin to showcase a complex Node.js system using Kafka.
OpenStack Glance Project Update
Glance is the OpenStack image service that provides services to store, browse, share, distribute, and manage disk images and metadata. It is deployed by 97% of OpenStack clouds. Contributor numbers grew for the Ocata release but declined after budget cuts impacted the core reviewer team. The presentation outlines features added in recent releases and themes for upcoming releases, and asks for help contributing in various ways.
Effective .NET Core Unit Testing with SQLite and Dapper
- The document discusses effective unit testing for .NET Core applications that use SQLite and Dapper for data access.
- It motivates the importance of unit testing, then demonstrates how to set up an in-memory SQLite database to run tests against instead of a live SQL Server database for isolation.
- Best practices like test-driven development, code coverage reports, and writing tests for bug fixes are also covered, with examples shown of integrating the XUnit testing framework.
It’s 2021. Why are we -still- rebooting for patches? A look at Live Patching.
Presented by: Igor Seletskiy
Presented at the All Things Open 2021
Raleigh, NC, USA
Raleigh Convention Center
Abstract: IT Teams know the drill. New security bulletins, new issues, new patches to deploy. Schedule another maintenance operation and prepare for system downtime.
There is a better way to do things. Live patching has been around in the Linux Kernel for some time now, but adoption has not been ideal so far - either because of a lack of trust in the technology or just lack of awareness - or sysadmins just enjoy interrupting their workloads or users.
Live patching consists of two aspects. First, there has to be a mechanism for function redirection in the kernel. As in many things, the kernel actually provides three different subset of tools that provide this functionality - kprobes, fprobes and Livepatching. Secondly, Live Patching relies on a set of tools to generate the actual patches to deploy, replacing the old code with new one. This is arguably the most involved part: you need to fit your new code in the proper space, you can’t overwrite other unrelated code and you need to maintain compatibility with other functions. If you change your parameter list, for example, its game over - something will break in the worst possible way.
In this talk we’ll go over issues like Consistency model, patch generation, deployment mechanisms and identify situations that are ideal candidates for live patching instead of traditional patching operations.
Similar to Istanbul Spring Meetup - Migrating SpringBoot Apps from Annotation-based Config to Functional with Kotlin (20)
OSCONF Jaipur - A Hitchhiker's Tour to Containerizing a Java application
OSCONF Jaipur - A Hitchhiker's Tour to Containerizing a Java application
GeekOut - Configuration Management with Kubernetes, a Spring-Boot use-case
GeekOut - Configuration Management with Kubernetes, a Spring-Boot use-case
AllTheTalks.online - A Streaming Use-Case: And Experiment in Continuous Deplo...
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From idea to production in a day – Leveraging Azure ML and Streamlit to build...
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JCon - Zero-Downtime-Deployment with Kubernetes, Spring Boot and Flyway
JCon - Zero-Downtime-Deployment with Kubernetes, Spring Boot and Flyway
VoxxedDays Cluj - Zero-downtime deployment with Kubernetes, Spring Boot and F...
VoxxedDays Cluj - Zero-downtime deployment with Kubernetes, Spring Boot and F...
DevOpsDays Madrid - Zero-downtime deployment with Kubernetes, Spring Boot and...
DevOpsDays Madrid - Zero-downtime deployment with Kubernetes, Spring Boot and...
London Java Community - An Experiment in Continuous Deployment of JVM applica...
London Java Community - An Experiment in Continuous Deployment of JVM applica...
Zero-downtime deployment on Kubernetes with Hazelcast
Zero-downtime deployment on Kubernetes with Hazelcast
Orchestrate Your End-to-end Mainframe Application Release Pipeline
Orchestrate Your End-to-end Mainframe Application Release Pipeline
JavaDay Istanbul - 3 improvements in your microservices architecture
JavaDay Istanbul - 3 improvements in your microservices architecture
XP Days Ukraine - Zero-downtime deployment with Kubernetes, Spring Boot and F...
XP Days Ukraine - Zero-downtime deployment with Kubernetes, Spring Boot and F...
Create a copy of your SharePoint farm with SPDocKit and AutoSPInstaller
Create a copy of your SharePoint farm with SPDocKit and AutoSPInstaller
Achieving end-to-end visibility into complex event-sourcing transactions usin...
Achieving end-to-end visibility into complex event-sourcing transactions usin...
Effective .NET Core Unit Testing with SQLite and Dapper
Effective .NET Core Unit Testing with SQLite and Dapper
It’s 2021. Why are we -still- rebooting for patches? A look at Live Patching.
It’s 2021. Why are we -still- rebooting for patches? A look at Live Patching.
More from Nicolas Fränkel
SnowCamp - Adding search to a legacy application
Most applications evolve to a point where they need to provide search capabilities. But updating an application is always a risk. Plus, sometimes, you don’t have access to the source code. The easiest way to access the data is by getting them directly from the database.
The initial load is the easiest step. However, how do you keep the search index in sync with the database? How do you keep the latency between the search store and the source of truth, so your users don’t have to wait for the next run of the batch to access the newest changes?
In this live coding session, we will show you how you can solve this issue by connecting Elasticsearch to the database with a touch of Hazelcast.
Un CV de dévelopeur toujours a jour
On dit que GitHub est le CV d'un développeur. Un rapide coup d'œil à votre historique de commit et les recruteurs savent tout de vous. Cette approche comporte quelques problèmes. La plupart des entreprises ne publient même pas leur code sous une licence Open Source. Si vous travaillez pour l'une d'entre elles, et si vous n'êtes pas un développeur Open Source les soirs et les week-ends, alors vous n'avez aucune chance.
Récemment, GitHub a permis un certain degré de personnalisation de son profil. Ainsi, même si votre historique de commit a plus de blanc que de vert, vous pouvez fournir un bon point d'entrée pour les employeurs potentiels. Mais ça ne vaut que l'effort que vous y mettez et les données perdent leur valeur rapidement. Pourtant, avec un peu de travail et l'aide d'outils d'automatisation (tels que GitHub Actions), vous pouvez présenter un profil toujours à jour.
jLove - A Change-Data-Capture use-case: designing an evergreen cache
When one’s app is challenged with poor performances, it’s easy to set up a cache in front of one’s SQL database. It doesn’t fix the root cause (e.g. bad schema design, bad SQL query, etc.) but it gets the job done. If the app is the only component that writes to the underlying database, it’s a no-brainer to update the cache accordingly, so the cache is always up-to-date with the data in the database.
Things start to go sour when the app is not the only component writing to the DB. Among other sources of writes, there are batches, other apps (shared databases exist unfortunately), etc. One might think about a couple of ways to keep data in sync i.e. polling the DB every now and then, DB triggers, etc. Unfortunately, they all have issues that make them unreliable and/or fragile.
You might have read about Change-Data-Capture before. It’s been described by Martin Kleppmann as turning the database inside out: it means the DB can send change events (SELECT, DELETE and UPDATE) that one can register to. Just opposite to Event Sourcing that aggregates events to produce state, CDC is about getting events out of states. Once CDC is implemented, one can subscribe to its events and update the cache accordingly. However, CDC is quite in its early stage, and implementations are quite specific.
In this talk, I’ll describe an easy-to-setup architecture that leverages CDC to have an evergreen cache.
BigData conference - Introduction to stream processing
This document discusses stream processing and summarizes a presentation about the topic. It introduces Hazelcast Jet as a stream processing engine and covers open data standards like GTFS. It also describes a demo that uses GTFS data to enrich public transit vehicle position updates in real-time using Hazelcast Jet. The presentation discusses streaming approaches, benefits over batch processing, and provides an overview of stream processing concepts.
ADDO - Your own Kubernetes controller, not only in Go
In Kubernetes, operators allow the API to be extended to your heart content. If one task requires too much YAML, it’s easy to create an operator to take care of the repetitive cruft, and only require a minimum amount of YAML.
On the other hand, since its beginnings, the Go language has been advertised as closer to the hardware, and is now ubiquitous in low-level programming. Kubernetes has been rewritten from Java to Go, and its whole ecosystem revolves around Go. For that reason, It’s only natural that Kubernetes provides a Go-based framework to create your own operator. While it makes sense, it requires organizations willing to go down this road to have Go developers, and/or train their teams in Go. While perfectly acceptable, this is not the only option. In fact, since Kubernetes is based on REST, why settle for Go and not use your own favorite language?
In this talk, I’ll describe what is an operator, how they work, how to design one, and finally demo a Java-based operator that is as good as a Go one.
TestCon Europe - Mutation Testing to the Rescue of Your Tests
Unit testing ensures your production code is relevant. But what does ensure your testing code is relevant? Come discover mutation testing and make sure your never forget another assert again.
In the realm of testing, the code coverage metrics is the most often talked about. However, it doesn’t mean that the test has been useful or even that an assert has been coded. Mutation testing is a strategy to make sure that the test code is relevant.
In this talk, Nicolas will explain how Code Coverage is computed and what its inherent flaw is. Afterwards, he will describe how Mutation Testing work and how it helps pointing out code that is tested but leave out corner cases. He will also demo PIT, a Java production-grade framework that enables Mutation Testing.
GeekcampSG 2020 - A Change-Data-Capture use-case: designing an evergreen cache
CDC is a brand new approach that "turns the database inside out": it allows to get events out of the database state. This can be leveraged to get a cache that is never stale.
OSCONF Hyderabad - Shorten all URLs!
At a point in the past, it was forecast that Java would die, but the JVM platform would be its legacy. And in fact, for a long time, the JVM has been tremendously successful. Wikipedia itself lists a bunch of languages that run on it, some of them close to Java e.g. Kotlin, some of them very remote e.g. Clojure.
But nowadays, the Cloud is becoming ubiquitous. Containerization is the way to go to alleviate some of the vendor lock-in issues. Kubernetes is a de facto platform. If a container needs to be killed for whatever reason (resource consumption, unhealthy, etc.), a new one needs to replace it as fast as possible. In that context, the JVM seems to be a dead-end: its startup time is huge in comparison to a native process. Likewise, it consumes a lot of memory that just increase the monthly bill.
What does that mean for us developers? Has all the time spent in learning the JVM ecosystem been invested with no hope of return over investment? Shall we need to invest even more time in new languages, frameworks, libraries, etc.? That is one possibility for sure. But we can also leverage our existing knowledge, and embrace the Cloud and containers ways with the help of some tools.
In this talk, I’ll create a simple URL shortener with a “standard” stack: Kotlin, JAX-RS and Hazelcast. Then, with the help of Quarkus and GraalVM, I’ll turn this application into a native executable with all Cloud/Container related work has been moved to the build process.
Devclub.lv - Introduction to stream processing
While “software is eating the world”, those who are able to best manage the huge mass of data will emerge out on the top.
The batch processing model has been faithfully serving us for decades. However, it might have reached the end of its usefulness for all but some very specific use-cases. As the pace of businesses increases, most of the time, decision-makers prefer slightly wrong data sooner, than 100% accurate data later. Stream processing – or data streaming – exactly matches this usage: instead of managing the entire bulk of data, manage pieces of them as soon as they become available.
JOnConf - A CDC use-case: designing an Evergreen Cache
This document discusses using change data capture (CDC) and Hazelcast Jet to build an evergreen cache that remains in sync with a database. It covers alternatives to cache invalidation like polling and triggers, introduces CDC and the Debezium implementation, and proposes a Jet job that watches database change events, analyzes them, and updates the cache accordingly to solve the cache freshness problem.
London In-Memory Computing Meetup - A Change-Data-Capture use-case: designing...
When one’s app is challenged with poor performances, it’s easy to set up a cache in front of one’s SQL database. It doesn’t fix the root cause (e.g. bad schema design, bad SQL query, etc.) but it gets the job done. If the app is the only component that writes to the underlying database, it’s a no-brainer to update the cache accordingly, so the cache is always up-to-date with the data in the database.
Things start to go sour when the app is not the only component writing to the DB. Among other sources of writes, there are batches, other apps (shared databases exist, unfortunately), etc. One might think about a couple of ways to keep data in sync i.e. polling the DB every now and then, DB triggers, etc. Unfortunately, they all have issues that make them unreliable and/or fragile.
In this talk, I will describe an easy-to-setup architecture that leverages CDC to have an evergreen cache.
JUG Tirana - Introduction to data streaming
This talk will be about the reasons behind the new stream processing model, how it compare to the old batch model, what are their pros and cons, and a list of existing technologies implementing stream processing with their most prominent characteristics. It will contain details of one possible use-case of data streaming that is not possible with batches: display in (near) real-time all trains in Switzerland and their position on a map, beginning with an overview of all the requirements and the design. Finally, using an OpenData endpoint and the Hazelcast platform,showing a working demo implementation of it.
Java.IL - Your own Kubernetes controller, not only in Go!
In Kubernetes, operators allow the API to be extended to your heart content. If one task requires too much YAML, it’s easy to create an operator to take care of the repetitive cruft, and only require a minimum amount of YAML.
On the other hand, since its beginnings, the Go language has been advertised as closer to the hardware, and is now ubiquitous in low-level programming. Kubernetes has been rewritten from Java to Go, and its whole ecosystem revolves around Go. For that reason, It’s only natural that Kubernetes provides a Go-based framework to create your own operator. While it makes sense, it requires organizations willing to go down this road to have Go developers, and/or train their teams in Go. While perfectly acceptable, this is not the only option. In fact, since Kubernetes is based on REST, why settle for Go and not use your own favorite language?
In this talk, I’ll describe what is an operator, how they work, how to design one, and finally demo a Java-based operator that is as good as a Go one.
vJUG - Introduction to data streaming
The document discusses stream processing and provides an overview of Hazelcast Jet. It begins with explaining why streaming is useful and describes different streaming approaches like event-driven programming. It then provides details on Hazelcast Jet, including its concepts of pipelines and jobs. The document also discusses open data standards like GTFS and demonstrates a sample streaming pipeline that enriches public transportation data from open APIs.
OSCONF - Your own Kubernetes controller: not only in Go
This document discusses creating Kubernetes operators and controllers using different programming languages besides Go. It suggests that a Java-based controller is possible using the GraalVM, which allows creating native executables from Java bytecode. Key points covered include what controllers and operators are, that no specific technology stack is required, and that the JVM could be a good option for controller development with GraalVM's support for polyglot programming and creating native applications.
Tech talks - 3 performance improvements
While a microservices architecture is more scalable than a monolith, it has a direct hit on performance.
To cope with that, one performance improvement is to set up a cache. It can be configured for database access, for REST calls or just to store session state across a cluster of server nodes. In this demo-based talk, I’ll show how Hazelcast In-Memory Data Grid can help you in each one of those areas and how to configure it.
Hint: it’s much easier than one would expect.
ING Meetup - Migrating Spring Boot Config Annotations to Functional with Kotlin
In the latest years, there has been some push-back against frameworks, and more specifically annotations: some call them magic. Obviously, they make understanding the flow of the application harder. Spring and Spring Boot latest versions go along this trend, by offering an additional way to configure beans with explicit code instead of annotations. It's declarative in the sense it looks like configuration, though it's based on Domain-Specific Language(s). This talk aims to demo a step-by-step process to achieve that.
SouJava- 3 easy performance improvements in your microservices architecture
While a microservices architecture is more scalable than a monolith, it has a direct hit on performance.
To cope with that, one performance improvement is to set up a cache. It can be configured for database access, for REST calls or just to store session state across a cluster of server nodes. In this demo-based talk, I'll show how Hazelcast In-Memory Data Grid can help you in each one of those areas and how to configure it. Hint: it's much easier than one would expect.
JUG SF - Introduction to data streaming
While “software is eating the world”, those who are able to best manage the huge mass of data will emerge out on the top.
The batch processing model has been faithfully serving us for decades. However, it might have reached the end of its usefulness for all but some very specific use-cases. As the pace of businesses increases, most of the time, decision makers prefer slightly wrong data sooner, than 100% accurate data later. Stream processing - or data streaming - exactly matches this usage: instead of managing the entire bulk of data, manage pieces of them as soon as they become available.
In this talk, I’ll define the context in which the old batch processing model was born, the reasons that are behind the new stream processing one, how they compare, what are their pros and cons, and a list of existing technologies implementing the latter with their most prominent characteristics. I’ll conclude by describing in detail one possible use-case of data streaming that is not possible with batches: display in (near) real-time all trains in Switzerland and their position on a map. I’ll go through the all the requirements and the design. Finally, using an OpenData endpoint and the Hazelcast platform, I’ll try to impress attendees with a working demo implementation of it.
SCALE - Stream processing and Open Data, a match made in Heaven
While “software is eating the world”, those who are able to best manage the huge mass of data will emerge out on the top.
Some countries in Europe understand the potential there's in existing data that sits behind closed fences, and passed laws to make this data available to everyone.
On the other hand, the batch processing model gets more and more obsolete: users want the information as soon as possible. While there’s a trade-off between correctness of data, and its speed of delivery, most business decisions do not rely on 100% correct data.
In this talk, I’ll explain how one can leverage the data related to public transportation in Switzerland, to display them in (near) real-time on a map.
More from Nicolas Fränkel (20)
jLove - A Change-Data-Capture use-case: designing an evergreen cache
jLove - A Change-Data-Capture use-case: designing an evergreen cache
BigData conference - Introduction to stream processing
BigData conference - Introduction to stream processing
ADDO - Your own Kubernetes controller, not only in Go
ADDO - Your own Kubernetes controller, not only in Go
TestCon Europe - Mutation Testing to the Rescue of Your Tests
TestCon Europe - Mutation Testing to the Rescue of Your Tests
GeekcampSG 2020 - A Change-Data-Capture use-case: designing an evergreen cache
GeekcampSG 2020 - A Change-Data-Capture use-case: designing an evergreen cache
JOnConf - A CDC use-case: designing an Evergreen Cache
JOnConf - A CDC use-case: designing an Evergreen Cache
London In-Memory Computing Meetup - A Change-Data-Capture use-case: designing...
London In-Memory Computing Meetup - A Change-Data-Capture use-case: designing...
Java.IL - Your own Kubernetes controller, not only in Go!
Java.IL - Your own Kubernetes controller, not only in Go!
OSCONF - Your own Kubernetes controller: not only in Go
OSCONF - Your own Kubernetes controller: not only in Go
ING Meetup - Migrating Spring Boot Config Annotations to Functional with Kotlin
ING Meetup - Migrating Spring Boot Config Annotations to Functional with Kotlin
SouJava- 3 easy performance improvements in your microservices architecture
SouJava- 3 easy performance improvements in your microservices architecture
SCALE - Stream processing and Open Data, a match made in Heaven
SCALE - Stream processing and Open Data, a match made in Heaven
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1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
办理(sdsu毕业证书)圣地亚哥州立大学毕业证【微信：741003700 】外观非常简单，由纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。
办理(sdsu毕业证书)圣地亚哥州立大学毕业证【微信：741003700 】格式相对统一，各专业都有相应的模板。通常包括以下部分：
校徽：象征着学校的荣誉和传承。
校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。
颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。
其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。
办理(sdsu毕业证书)圣地亚哥州立大学毕业证【微信：741003700 】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。
综上所述，办理(sdsu毕业证书)圣地亚哥州立大学毕业证【微信：741003700 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。
Malibou Pitch Deck For Its €3M Seed Round
French start-up Malibou raised a €3 million Seed Round to develop its payroll and human resources
management platform for VSEs and SMEs. The financing round was led by investors Breega, Y Combinator, and FCVC.
A Comprehensive Guide on Implementing Real-World Mobile Testing Strategies fo...
In today's fiercely competitive mobile app market, the role of the QA team is pivotal for continuous improvement and sustained success. Effective testing strategies are essential to navigate the challenges confidently and precisely. Ensuring the perfection of mobile apps before they reach end-users requires thoughtful decisions in the testing plan.
Enums On Steroids - let's look at sealed classes !
These are slides for my session"Enums On Steroids - let's look at sealed classes !" - delivered among others, on Devoxx UK 2024 conference
UI5con 2024 - Bring Your Own Design System
How do you combine the OpenUI5/SAPUI5 programming model with a design system that makes its controls available as Web Components? Since OpenUI5/SAPUI5 1.120, the framework supports the integration of any Web Components. This makes it possible, for example, to natively embed own Web Components of your design system which are created with Stencil. The integration embeds the Web Components in a way that they can be used naturally in XMLViews, like with standard UI5 controls, and can be bound with data binding. Learn how you can also make use of the Web Components base class in OpenUI5/SAPUI5 to also integrate your Web Components and get inspired by the solution to generate a custom UI5 library providing the Web Components control wrappers for the native ones.
Why Apache Kafka Clusters Are Like Galaxies (And Other Cosmic Kafka Quandarie...
Closing talk for the Performance Engineering track at Community Over Code EU (Bratislava, Slovakia, June 5 2024) https://eu.communityovercode.org/sessions/2024/why-apache-kafka-clusters-are-like-galaxies-and-other-cosmic-kafka-quandaries-explored/ Instaclustr (now part of NetApp) manages 100s of Apache Kafka clusters of many different sizes, for a variety of use cases and customers. For the last 7 years I’ve been focused outwardly on exploring Kafka application development challenges, but recently I decided to look inward and see what I could discover about the performance, scalability and resource characteristics of the Kafka clusters themselves. Using a suite of Performance Engineering techniques, I will reveal some surprising discoveries about cosmic Kafka mysteries in our data centres, related to: cluster sizes and distribution (using Zipf’s Law), horizontal vs. vertical scalability, and predicting Kafka performance using metrics, modelling and regression techniques. These insights are relevant to Kafka developers and operators.
一比一原版(USF毕业证)旧金山大学毕业证如何办理
USF硕士毕业证成绩单【微信95270640】一比一伪造旧金山大学文凭@假冒USF毕业证成绩单+Q微信95270640办理USF学位证书@仿造USF毕业文凭证书@购买旧金山大学毕业证成绩单USF真实使馆认证/真实留信认证回国人员证明
#一整套旧金山大学文凭证件办理#—包含旧金山大学旧金山大学本科毕业证成绩单学历认证|使馆认证|归国人员证明|教育部认证|留信网认证永远存档教育部学历学位认证查询办理国外文凭国外学历学位认证#我们提供全套办理服务。
一整套留学文凭证件服务：
一：旧金山大学旧金山大学本科毕业证成绩单毕业证 #成绩单等全套材料从防伪到印刷水印底纹到钢印烫金
二：真实使馆认证（留学人员回国证明）使馆存档
三：真实教育部认证教育部存档教育部留服网站永久可查
四：留信认证留学生信息网站永久可查
国外毕业证学位证成绩单办理方法：
1客户提供办理旧金山大学旧金山大学本科毕业证成绩单信息：姓名生日专业学位毕业时间等（如信息不确定可以咨询顾问：我们有专业老师帮你查询）；
2开始安排制作毕业证成绩单电子图；
3毕业证成绩单电子版做好以后发送给您确认；
4毕业证成绩单电子版您确认信息无误之后安排制作成品；
5成品做好拍照或者视频给您确认；
6快递给客户（国内顺丰国外DHLUPS等快读邮寄）。
教育部文凭学历认证认证的用途：
如果您计划在国内发展那么办理国内教育部认证是必不可少的。事业性用人单位如银行国企公务员在您应聘时都会需要您提供这个认证。其他私营 #外企企业无需提供！办理教育部认证所需资料众多且烦琐所有材料您都必须提供原件我们凭借丰富的经验帮您快速整合材料让您少走弯路。
实体公司专业为您服务如有需要请联系我: 微信95270640奈一次次令他失望山娃今年岁上五年级识得很多字从走出小屋开始山娃就知道父亲的家和工地共有一个很动听的名字——天河工地的底层空空荡荡很宽阔很凉爽在地上铺上报纸和水泥袋父亲和工人们中午全睡在地上地面坑坑洼洼山娃曾多次绊倒过也曾有长铁钉穿透凉鞋刺在脚板上但山娃不怕工地上也常有五六个从乡下来的小学生他们的父母亲也是高楼上的建筑工人小伙伴来自不同省份都操着带有浓重口音的普通话可不知为啥山娃不仅很快与他们熟识了
Microservice Teams - How the cloud changes the way we work
A lot of technical challenges and complexity come with building a cloud-native and distributed architecture. The way we develop backend software has fundamentally changed in the last ten years. Managing a microservices architecture demands a lot of us to ensure observability and operational resiliency. But did you also change the way you run your development teams?
Sven will talk about Atlassian’s journey from a monolith to a multi-tenanted architecture and how it affected the way the engineering teams work. You will learn how we shifted to service ownership, moved to more autonomous teams (and its challenges), and established platform and enablement teams.
如何办理(hull学位证书)英国赫尔大学毕业证硕士文凭原版一模一样
原版定制【微信:bwp0011】《(hull学位证书)英国赫尔大学毕业证硕士文凭》【微信:bwp0011】成绩单 、雅思、外壳、留信学历认证永久存档查询，采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信bwp0011】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信bwp0011】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
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