RabbitMQ is an open source message-broker software that originally implemented the Advanced Message Queuing Protocol (AMQP).it accepts and forwards messages.
What is an API-first enterprise? Where do APIs fit into modern application architecture? Are they just new terms for SOA? Presentation from Apigee's City Tour in Paris 23 June 2016.
MuleSoft Deployment Strategies (RTF vs Hybrid vs CloudHub)Prashanth Kurimella
Differences between MuleSoft Deployment Strategies (RTF vs Hybrid vs CloudHub)
For additional information, read https://www.linkedin.com/pulse/mulesoft-deployment-strategies-rtf-vs-hybrid-cloudhub-kurimella/
The document discusses the Kubernetes API server and its RESTful HTTP API. It describes the API endpoints for accessing different Kubernetes resources, how API groups and versions are organized, how API requests are routed and processed, how Kubernetes objects are converted between different versions, and how storage and code generation are used.
Draft: building secure applications with keycloak (oidc/jwt)Abhishek Koserwal
Building an enterprise level single sign-on application with the help of keycloak (Open Source Identity and Access Management). And understanding the way to secure your application; frontend & backend API’s. Managing user federation with minimum configuration.
As the industry’s first enterprise identity bus (EIB), WSO2 Identity Server is the central backbone that connects and manages multiple identities across applications, APIs, the cloud, mobile, and Internet of Things devices, regardless of the standards on which they are based. The multi-tenant WSO2 Identity Server can be deployed directly on servers or in the cloud, and has the ability to propagate identities across geographical and enterprise borders in a connected business environment.
Introduction to red hat agile integration (Red Hat Workshop)Judy Breedlove
This presentation provides and overview of Red Hat's approached to Agile integration. It was presented at the "Agile integration with Containers & APIs" workshop series. Fall 2018
Integration Patterns and Anti-Patterns for Microservices ArchitecturesApcera
Integration Patterns and Anti-Patterns for Microservices Architectures
David Williams
Co-Founder and Partner, Williams Garcia
You can learn more about NATS at http://www.nats.io
What is DevOps | DevOps Introduction | DevOps Training | DevOps Tutorial | Ed...Edureka!
***** DevOps Masters Program : https://www.edureka.co/masters-progra... *****
This DevOps tutorial takes you through what is DevOps all about and basic concepts of DevOps and DevOps Tools. This DevOps tutorial is ideal for beginners to get started with DevOps. Check our complete DevOps playlist here: http://goo.gl/O2vo13
DevOps Tutorial Blog Series: https://goo.gl/P0zAfF
This document provides an overview of service mesh and Istio on Kubernetes. It discusses microservices and the need for visibility, monitoring, and traffic management which a service mesh can provide. It then describes Kubernetes, Istio architecture including Pilot, Envoy proxy, and Mixer components. It covers how Istio provides mutual TLS, ingress/egress traffic routing, request routing to service versions, observability with metrics and tracing, and application resilience through features like timeouts and retries. The document concludes with instructions for deploying Kubernetes and getting started with Istio.
Introduction of cloud native CI/CD on kubernetesKyohei Mizumoto
This document discusses setting up continuous integration and continuous delivery (CI/CD) pipelines on Kubernetes using Concourse CI and Argo CD. It provides an overview of each tool, instructions for getting started with Concourse using Helm and configuring sample pipelines in YAML, and instructions for installing and configuring applications in Argo CD through application manifests.
Are your APIs becoming too complicated and ad hoc? Feeling the need to set up policies for your API? This presentation will give you strategy options for designing and developing your APIs.
Apigee and Amazon Web Services join together in this webcast to discuss using Apigee's API management for AWS-powered backends. API management makes it easy to expose and consume APIs from services built on AWS. Whether your backend runs on EC2, DynamoDB, or AWS Lambda, we'll show you the best way to build AWeSome APIs.
At the Devoxx 2015 conference in Belgium, Guillaume Laforge, Product Ninja & Advocate at Restlet, presented about the never-ending REST API design debate, covering many topics like HTTP status codes, Hypermedia APIs, pagination/searching/filtering, and more.
Best Practices with Azure Kubernetes ServicesQAware GmbH
- AKS best practices discusses cluster isolation and resource management, storage, networking, network policies, securing the environment, scaling applications and clusters, and logging and monitoring for AKS clusters.
- It provides an overview of the different Kubernetes offerings in Azure (DIY, ACS Engine, and AKS), and recommends using at least 3 nodes for upgrades when using persistent volumes.
- The document discusses various AKS networking configurations like basic networking, advanced networking using Azure CNI, internal load balancers, ingress controllers, and network policies. It also covers cluster level security topics like IAM with AAD and RBAC.
Building an enterprise level single sign-on application with the help of keycloak (Open Source Identity and Access Management).
And understanding the way to secure your application; frontend & backend API’s. Managing user federation with minimum configuration.
Building Kubernetes images at scale with Tanzu Build ServiceVMware Tanzu
Building a secure software supply chain
Leveraging Tanzu Build Service
How Build Service fits in the Tanzu portfolio
Modernize your applications
Live demos
Look ma: no Dockerfile!
API Security in a Microservice ArchitectureMatt McLarty
This presentation was given at the O'Reilly Software Architecture Conference in New York on Feb. 28, 2018. It gives an overview of the new book, Securing Microservice APIs. Download available here: https://transform.ca.com/API-securing-microservice-apis-oreilly-ebook.html
What's a good API business model? If you have an API, or you plan to have an open API, or just want to use APIs in your web or mobile app, what models make sense? See 20 different API business models. This comprehensive survey of the gamut of today's options covering anything from paid to getting paid to indirect.
Migrating .NET Application to .NET CoreBaris Ceviz
"Migrate .NET Application to .NET Core with scenarios" presentation in Dotnetkonf. How to migrate .NET Desktop, ASP.NET MVC & Web API or WCF Service applications to .NET Core
I Love APIs 2015: Advanced Crash Course in Apigee Edge Workshop Apigee | Google Cloud
The document discusses OAuth 2.0 authorization concepts including access tokens, refresh tokens, scopes, and grant types. It provides examples of how a third-party web application can use the authorization code grant type to obtain an access token from an authorization server to access protected resources, such as by redirecting the user to a login page to authenticate.
Mule Common Logging & Error Handling FrameworkVijay Reddy
The mCLE framework provides a common solution for logging and handling exceptions across enterprise applications. It follows a top-down and bottom-up approach to standardize error handling. The mCLE connector can be used in Mule flows to publish errors to a queue. It generates dashboards and reports to analyze errors by activity, type, application and flow. Alerts are also sent to notify teams of technical errors.
WSO2 API Manager 2.0.0 is a complete open source API management solution that allows users to create, publish, protect, and manage the lifecycles of APIs. It includes features such as a developer portal, API testing tools, an API gateway, analytics reporting, and support for multi-tenancy. The solution can be deployed on-premises, as a managed service, or as software as a service (SaaS) and is highly scalable.
A REST API uses HTTP requests with verbs like GET, POST, PUT, and DELETE to perform CRUD (Create, Read, Update, Delete) operations on resources identified by URLs. It provides a lightweight alternative to SOAP that returns data in JSON format and HTTP response codes. Well-known codes include 200 for OK, 201 for Created, 400 for Bad Request, and 404 for Not Found. REST enables building applications and platforms that can easily integrate new interfaces over time.
Speaker: Chris Du Preez
Host: Angel Alberici
Youtube: Virtual Muleys (https://www.youtube.com/c/VirtualMuleysOnline/videos)
Meetups: https://meetups.mulesoft.com/events/details/mulesoft-online-group-english-presents-runtime-fabric-rtf-foundations/
Runtime Fabric Foundations. Tune in this time to get a full overview around RTF: architecture, learning paths, tips, how to avoid pitfalls and more. Time to learn. Chris Du Preez will be guiding us through this 50 minutes session!
Anypoint Runtime Fabric is a container service that automates the deployment and orchestration of Mule applications and API gateways. Runtime Fabric runs within a customer-managed infrastructure on AWS, Azure, virtual machines (VMs), and bare-metal servers. (Find out more: https://docs.mulesoft.com/runtime-fabric/1.7/)
This document discusses design patterns for running Apache Camel applications on Kubernetes. It begins with an introduction of the presenter and an overview of trends driving cloud native application development. It then discusses what cloud native means and reviews popular container orchestration platforms like Kubernetes, Docker Swarm and Mesos. The remainder of the document focuses on deployment patterns for Camel applications on Kubernetes, including packaging, health checks, configuration, service discovery, circuit breakers and retries.
Integration Patterns and Anti-Patterns for Microservices ArchitecturesApcera
Integration Patterns and Anti-Patterns for Microservices Architectures
David Williams
Co-Founder and Partner, Williams Garcia
You can learn more about NATS at http://www.nats.io
What is DevOps | DevOps Introduction | DevOps Training | DevOps Tutorial | Ed...Edureka!
***** DevOps Masters Program : https://www.edureka.co/masters-progra... *****
This DevOps tutorial takes you through what is DevOps all about and basic concepts of DevOps and DevOps Tools. This DevOps tutorial is ideal for beginners to get started with DevOps. Check our complete DevOps playlist here: http://goo.gl/O2vo13
DevOps Tutorial Blog Series: https://goo.gl/P0zAfF
This document provides an overview of service mesh and Istio on Kubernetes. It discusses microservices and the need for visibility, monitoring, and traffic management which a service mesh can provide. It then describes Kubernetes, Istio architecture including Pilot, Envoy proxy, and Mixer components. It covers how Istio provides mutual TLS, ingress/egress traffic routing, request routing to service versions, observability with metrics and tracing, and application resilience through features like timeouts and retries. The document concludes with instructions for deploying Kubernetes and getting started with Istio.
Introduction of cloud native CI/CD on kubernetesKyohei Mizumoto
This document discusses setting up continuous integration and continuous delivery (CI/CD) pipelines on Kubernetes using Concourse CI and Argo CD. It provides an overview of each tool, instructions for getting started with Concourse using Helm and configuring sample pipelines in YAML, and instructions for installing and configuring applications in Argo CD through application manifests.
Are your APIs becoming too complicated and ad hoc? Feeling the need to set up policies for your API? This presentation will give you strategy options for designing and developing your APIs.
Apigee and Amazon Web Services join together in this webcast to discuss using Apigee's API management for AWS-powered backends. API management makes it easy to expose and consume APIs from services built on AWS. Whether your backend runs on EC2, DynamoDB, or AWS Lambda, we'll show you the best way to build AWeSome APIs.
At the Devoxx 2015 conference in Belgium, Guillaume Laforge, Product Ninja & Advocate at Restlet, presented about the never-ending REST API design debate, covering many topics like HTTP status codes, Hypermedia APIs, pagination/searching/filtering, and more.
Best Practices with Azure Kubernetes ServicesQAware GmbH
- AKS best practices discusses cluster isolation and resource management, storage, networking, network policies, securing the environment, scaling applications and clusters, and logging and monitoring for AKS clusters.
- It provides an overview of the different Kubernetes offerings in Azure (DIY, ACS Engine, and AKS), and recommends using at least 3 nodes for upgrades when using persistent volumes.
- The document discusses various AKS networking configurations like basic networking, advanced networking using Azure CNI, internal load balancers, ingress controllers, and network policies. It also covers cluster level security topics like IAM with AAD and RBAC.
Building an enterprise level single sign-on application with the help of keycloak (Open Source Identity and Access Management).
And understanding the way to secure your application; frontend & backend API’s. Managing user federation with minimum configuration.
Building Kubernetes images at scale with Tanzu Build ServiceVMware Tanzu
Building a secure software supply chain
Leveraging Tanzu Build Service
How Build Service fits in the Tanzu portfolio
Modernize your applications
Live demos
Look ma: no Dockerfile!
API Security in a Microservice ArchitectureMatt McLarty
This presentation was given at the O'Reilly Software Architecture Conference in New York on Feb. 28, 2018. It gives an overview of the new book, Securing Microservice APIs. Download available here: https://transform.ca.com/API-securing-microservice-apis-oreilly-ebook.html
What's a good API business model? If you have an API, or you plan to have an open API, or just want to use APIs in your web or mobile app, what models make sense? See 20 different API business models. This comprehensive survey of the gamut of today's options covering anything from paid to getting paid to indirect.
Migrating .NET Application to .NET CoreBaris Ceviz
"Migrate .NET Application to .NET Core with scenarios" presentation in Dotnetkonf. How to migrate .NET Desktop, ASP.NET MVC & Web API or WCF Service applications to .NET Core
I Love APIs 2015: Advanced Crash Course in Apigee Edge Workshop Apigee | Google Cloud
The document discusses OAuth 2.0 authorization concepts including access tokens, refresh tokens, scopes, and grant types. It provides examples of how a third-party web application can use the authorization code grant type to obtain an access token from an authorization server to access protected resources, such as by redirecting the user to a login page to authenticate.
Mule Common Logging & Error Handling FrameworkVijay Reddy
The mCLE framework provides a common solution for logging and handling exceptions across enterprise applications. It follows a top-down and bottom-up approach to standardize error handling. The mCLE connector can be used in Mule flows to publish errors to a queue. It generates dashboards and reports to analyze errors by activity, type, application and flow. Alerts are also sent to notify teams of technical errors.
WSO2 API Manager 2.0.0 is a complete open source API management solution that allows users to create, publish, protect, and manage the lifecycles of APIs. It includes features such as a developer portal, API testing tools, an API gateway, analytics reporting, and support for multi-tenancy. The solution can be deployed on-premises, as a managed service, or as software as a service (SaaS) and is highly scalable.
A REST API uses HTTP requests with verbs like GET, POST, PUT, and DELETE to perform CRUD (Create, Read, Update, Delete) operations on resources identified by URLs. It provides a lightweight alternative to SOAP that returns data in JSON format and HTTP response codes. Well-known codes include 200 for OK, 201 for Created, 400 for Bad Request, and 404 for Not Found. REST enables building applications and platforms that can easily integrate new interfaces over time.
Speaker: Chris Du Preez
Host: Angel Alberici
Youtube: Virtual Muleys (https://www.youtube.com/c/VirtualMuleysOnline/videos)
Meetups: https://meetups.mulesoft.com/events/details/mulesoft-online-group-english-presents-runtime-fabric-rtf-foundations/
Runtime Fabric Foundations. Tune in this time to get a full overview around RTF: architecture, learning paths, tips, how to avoid pitfalls and more. Time to learn. Chris Du Preez will be guiding us through this 50 minutes session!
Anypoint Runtime Fabric is a container service that automates the deployment and orchestration of Mule applications and API gateways. Runtime Fabric runs within a customer-managed infrastructure on AWS, Azure, virtual machines (VMs), and bare-metal servers. (Find out more: https://docs.mulesoft.com/runtime-fabric/1.7/)
This document discusses design patterns for running Apache Camel applications on Kubernetes. It begins with an introduction of the presenter and an overview of trends driving cloud native application development. It then discusses what cloud native means and reviews popular container orchestration platforms like Kubernetes, Docker Swarm and Mesos. The remainder of the document focuses on deployment patterns for Camel applications on Kubernetes, including packaging, health checks, configuration, service discovery, circuit breakers and retries.
Clipper: A Low-Latency Online Prediction Serving SystemDatabricks
Machine learning is being deployed in a growing number of applications which demand real-time, accurate, and robust predictions under heavy serving loads. However, most machine learning frameworks and systems only address model training and not deployment.
Clipper is a general-purpose model-serving system that addresses these challenges. Interposing between applications that consume predictions and the machine-learning models that produce predictions, Clipper simplifies the model deployment process by isolating models in their own containers and communicating with them over a lightweight RPC system. This architecture allows models to be deployed for serving in the same runtime environment as that used during training. Further, it provides simple mechanisms for scaling out models to meet increased throughput demands and performing fine-grained physical resource allocation for each model.
In this talk, I will provide an overview of the Clipper serving system and then discuss how to get started using Clipper to serve Spark and TensorFlow models in a production serving environment.
This document provides an overview of integrating microservices with Apache Camel and JBoss Fuse. It introduces Apache Camel as a lightweight integration library that uses enterprise integration patterns and domain-specific languages to define integration "flows" and "routes". It describes how Camel supports features like dynamic routing, REST APIs, backpressure, load balancing, and circuit breakers that are useful for building microservices. The document also introduces JBoss Fuse as a development and runtime platform for microservices that provides tooling, frameworks, management capabilities and container support using technologies like Apache Camel, CXF, ActiveMQ and Karaf.
This document provides an overview of enterprise integration patterns (EIPs) and how they are implemented using Apache Camel and Project Fuji frameworks. It discusses core EIP principles like asynchronous messaging for integration. It also describes various EIP implementations like content-based routing, dead letter channels, and message transformation patterns. Code examples are shown using the Java and Spring DSLs for Apache Camel and the DSL and web UI for Project Fuji.
Low Latency Polyglot Model Scoring using Apache ApexApache Apex
This document discusses challenges in building low-latency machine learning applications and how Apache Apex can help address them. It introduces Apache Apex as a distributed streaming engine and describes how it allows embedding models from frameworks like R, Python, H2O through custom operators. It provides various data and model scoring patterns in Apex like dynamic resource allocation, checkpointing, exactly-once processing to meet SLAs. The document also demonstrates techniques like canary deployment, dormant models, model ensembles through logical overlays on the Apex DAG.
Stream Data Processing at Big Data Landscape by Oleksandr Fedirko GlobalLogic Ukraine
This document provides an overview of stream data processing and common stream processing tools. It discusses streaming basics like stateful and stateless operations. It also covers microbatch vs realtime streaming and compositional vs declarative stream processing engines. Typical stream processing architectures and use cases are presented. Main considerations for projects using stream processing are outlined. An overview of popular stream processing tools like Apache Spark, Storm, Flink, and cloud services from AWS, GCP and Azure is provided. The document concludes with a case study example and questions for discussion.
The document discusses using Apache Kafka for event detection pipelines. It describes how Kafka can be used to decouple data pipelines and ingest events from various source systems in real-time. It then provides an example use case of using Kafka, Hadoop, and machine learning for fraud detection in consumer banking, describing the online and offline workflows. Finally, it covers some of the challenges of building such a system and considerations for deploying Kafka.
Energy efficient AI workload partitioning on multi-core systemsDeepak Shankar
o create an AI system, the semiconductor, software, and systems team need to work together. Multi-core systems can provide extremely low latency and higher throughput at lower power consumption. But concurrent access to shared resources by multiple of AI workloads running on different cores can create higher worst-case execution time (WCET) and causes multiple system failures. Architecture exploration can be used to efficiently balance the compute, communication, synchronization, and storage. In this Webinar, we will be using Workloads from automotive, and data centers to demonstrate the methodology.
VisualSim Architect enables designers to assemble architecture models that extend from the smallest IoT to full automotive, and Radar systems to Data Centers. These models will include any combination of software, processors, ECU, RTOS and networks. Using this platform, software designer can explore the partitioning of the AI tasks (software or model) on to cores based on the latency, bandwidth, and power constraints. Within the IoT, the processor, A/D, Bluetooth and software can be modeled while an automotive design will require the network, ECU and firmware. Both have a unique mechanism to define the traffic, test scenarios and AI workloads. Hardware engineers can select cores, cores per cluster, cache hierarchy, memory controller, accelerators, and the interface topology. Software engineers can tune the partitioning, synchronization overhead, memory access schedules and scheduling.
This document provides an introduction to Apache Camel with Spring Boot. It discusses the architecture of Apache Camel including the routing engine, processor, components and Camel context. It explains that Apache Camel is an integration framework that allows defining routing rules and connecting different systems using a common API. The document outlines reasons to use Apache Camel like routing, extensive components, easy configuration and integration patterns. It also provides an example of how to integrate Apache Camel with Spring Boot and build routes in Apache Camel.
This document provides an introduction to Apache Camel with Spring Boot. It discusses the architecture of Apache Camel including the routing engine, processor, components and Camel context. It explains that Apache Camel is an integration framework that allows defining routing rules and connecting different systems using a common API. The document outlines reasons to use Apache Camel such as routing, extensive components, easy configuration and integration patterns. It also provides an example of how to build routes with Apache Camel and integrate it with Spring Boot.
This document provides an overview of Apache Camel, an open source framework for integration. It discusses key Camel concepts like routes, endpoints, components, messages and integration patterns. It provides examples of routing messages between different endpoints using the Java and XML domain specific languages.
Scaling Up Machine Learning Experimentation at Tubi 5x and BeyondScyllaDB
Scylla enables rapid Machine Learning experimentation at Tubi. The current-generation personalization service, Ranking Service, ramps up experimentation by 5x, while Popper, the next-generation experimentation engine, will grow by 10x and beyond. We'll talk about what's so special about these services.
OS for AI: Elastic Microservices & the Next Gen of MLNordic APIs
AI has been a hot topic lately, with advances being made constantly in what is possible, there has not been as much discussion of the infrastructure and scaling challenges that come with it. How do you support dozens of different languages and frameworks, and make them interoperate invisibly? How do you scale to run abstract code from thousands of different developers, simultaneously and elastically, while maintaining less than 15ms of overhead?
At Algorithmia, we’ve built, deployed, and scaled thousands of algorithms and machine learning models, using every kind of framework (from scikit-learn to tensorflow). We’ve seen many of the challenges faced in this area, and in this talk I’ll share some insights into the problems you’re likely to face, and how to approach solving them.
In brief, we’ll examine the need for, and implementations of, a complete “Operating System for AI” – a common interface for different algorithms to be used and combined, and a general architecture for serverless machine learning which is discoverable, versioned, scalable and sharable.
AWS Serverless patterns & best-practices in AWSDima Pasko
This presentation discusses serverless patterns and best practices in AWS. It defines what serverless computing is and outlines the business case for serverless, including faster time to market, reduced costs, improved reliability, and increased innovation. It then covers AWS serverless design patterns and solutions, serverless myths and anti-patterns to avoid, and best practices like using the Serverless Application Model, AWS CDK, nested stacks, managing limits and connections, and power tuning.
Infrastructure Challenges in Scaling RAG with Custom AI modelsZilliz
Read more: https://zilliz.com/blog/infrastructure-challenges-in-scaling-rag-with-custom-ai-models
Building Retrieval-Augmented Generation (RAG) systems with open-source and custom AI models is a complex task. This talk explores the challenges in productionizing RAG systems, including retrieval performance, response synthesis, and evaluation. We’ll discuss how to leverage open-source models like text embeddings, language models, and custom fine-tuned models to enhance RAG performance. Additionally, we’ll cover how BentoML can help orchestrate and scale these AI components efficiently, ensuring seamless deployment and management of RAG systems in the cloud.
This document discusses multi-threading and actors. It covers topics like Moore's Law driving the need for threads to reduce latency, the differences between concurrency and parallelism, blocking vs non-blocking I/O, potential issues with asynchronous programming like memory leaks and callback hells, using synchronized and atomics to address thread safety issues, Java executors as a better abstraction than direct threads, Java concurrent collections, examples of actor programming with Akka in Scala, and exercises to build concurrent and actor-based applications in Java and Scala.
This document discusses performance-oriented design and what metrics should be measured. It emphasizes that performance is important and organizations should care about it. Key metrics that should be measured include arrival rate, service time, throughput, queues, method counts, response times, and other application and system-level metrics. References for further reading on performance engineering and capacity planning are also provided.
AskTom: How to Make and Test Your Application "Oracle RAC Ready"?Markus Michalewicz
Oracle Real Application Clusters (Oracle RAC) is the preferred availability and scalability solution for Oracle Databases, as most applications can benefit from its capabilities without making any changes. This mini session explains the secrets behind Oracle RAC’s horizontal scaling algorithm, Cache Fusion, and how you can test and ensure that your application is “Oracle RAC ready.”
This deck was first presented in OOW19 as an AskTom theater / mini session and will be presented as a full version in other conferences going forward at which time I will provide an updated version of the deck.
Apache Camel is an open source framework for integrating applications and systems. It uses an declarative domain-specific language to configure routing and mediation rules. Components in Camel act as endpoint factories to interact with external systems using different protocols. Routes define the application logic and flow using the Java DSL or XML definitions. Camel provides numerous components out of the box and also allows custom components to be created.
Dapr - A 10x Developer Framework for Any LanguageBilgin Ibryam
Dapr is a distributed application runtime that aims to simplify building microservices applications. It provides out of the box capabilities for service invocation, publish/subscribe, state management, and other operational needs like security, observability and configuration. Dapr allows building applications using any programming languages or frameworks that can run on any infrastructure. It handles common developer and operational challenges like service discovery, retries, failure handling, and access control. The open source project has seen significant community growth with over 21k GitHub stars and is part of the CNCF.
Modernization patterns to refactor a legacy application into event driven mic...Bilgin Ibryam
A use-case-driven introduction to the most common design patterns for modernizing monolithic legacy applications to microservices using Apache Kafka, Debezium, and Kubernetes.
During this talk, Bilgin will take you on a journey exploring distributed application needs and how they evolved with Kubernetes, Istio, Knative, Dapr, and other projects. By the end of the session, you will know what is coming after microservices
The Evolution of Distributed Systems on KubernetesBilgin Ibryam
Cloud native applications of the future will consist of hybrid workloads: stateful applications, batch jobs, stateless microservices, functions, (and maybe something else too) wrapped as Linux containers and deployed via Kubernetes on any cloud. Functions and the so-called serverless computing model is the latest evolution of what started as SOA years ago. But is it the last step of the application architecture evolution and is it here to stay? During this talk, we will take you on a journey exploring distributed application needs and how they evolved with Kubernetes, Istio, Knative, Dapr, and other projects. By the end of the session, you will know what is coming after microservices.
Bilgin Ibryam presented on enterprise integration with Ethereum. He discussed Red Hat's involvement in blockchain through partner ecosystems like BlockApps, participating in consortia like Hyperledger, and enabling their portfolio for blockchain projects. He described Apache Camel, an open source integration framework, and its Camel-web3j connector which provides integration with Ethereum nodes and APIs and supports smart contract interactions through filters and web3 operations.
The document discusses Kubernetes and cloud native application design. It begins by defining cloud native as structuring teams and technology around automation and microservices packaged as containers orchestrated by platforms like Kubernetes. It then covers common Kubernetes resources like pods, services, deployments and Kubernetes design patterns like sidecars, init containers and immutable configuration. The document advocates principles for container-based applications including single concern, self-containment and image immutability. It also recommends techniques like using volumes for persistent data and logging to standard output/error.
This document discusses cloud native patterns for applications adopting microservices packaged as containers orchestrated by platforms running on cloud infrastructure. It outlines common abstractions like containers, pods, deployments, services, and namespaces. It then covers 10 specific cloud native patterns including observable process health, declarative updates, predictable resource profiles, dynamic placement, service discovery, scheduled batch jobs, clustered services, executable application manifests, and letting the platform automate routine tasks while focusing on the business domain.
Kubernetes is awesome! But what does it takes for a Java developer to design, implement and run Cloud Native applications? In this session, we will look at Kubernetes from a user point of view and demonstrate how to consume it effectively. We will discover which concerns Kubernetes addresses and how it helps to develop highly scalable and resilient Java applications.
FOSDEM TALK: https://fosdem.org/2017/schedule/event/cnjavadev/
Adobe Master Collection CC Crack Advance Version 2025kashifyounis067
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Adobe Master Collection CC (Creative Cloud) is a comprehensive subscription-based package that bundles virtually all of Adobe's creative software applications. It provides access to a wide range of tools for graphic design, video editing, web development, photography, and more. Essentially, it's a one-stop-shop for creatives needing a broad set of professional tools.
Key Features and Benefits:
All-in-one access:
The Master Collection includes apps like Photoshop, Illustrator, InDesign, Premiere Pro, After Effects, Audition, and many others.
Subscription-based:
You pay a recurring fee for access to the latest versions of all the software, including new features and updates.
Comprehensive suite:
It offers tools for a wide variety of creative tasks, from photo editing and illustration to video editing and web development.
Cloud integration:
Creative Cloud provides cloud storage, asset sharing, and collaboration features.
Comparison to CS6:
While Adobe Creative Suite 6 (CS6) was a one-time purchase version of the software, Adobe Creative Cloud (CC) is a subscription service. CC offers access to the latest versions, regular updates, and cloud integration, while CS6 is no longer updated.
Examples of included software:
Adobe Photoshop: For image editing and manipulation.
Adobe Illustrator: For vector graphics and illustration.
Adobe InDesign: For page layout and desktop publishing.
Adobe Premiere Pro: For video editing and post-production.
Adobe After Effects: For visual effects and motion graphics.
Adobe Audition: For audio editing and mixing.
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Wondershare Filmora is a video editing software and app designed for both beginners and experienced users. It's known for its user-friendly interface, drag-and-drop functionality, and a wide range of tools and features for creating and editing videos. Filmora is available on Windows, macOS, iOS (iPhone/iPad), and Android platforms.
Not So Common Memory Leaks in Java WebinarTier1 app
This SlideShare presentation is from our May webinar, “Not So Common Memory Leaks & How to Fix Them?”, where we explored lesser-known memory leak patterns in Java applications. Unlike typical leaks, subtle issues such as thread local misuse, inner class references, uncached collections, and misbehaving frameworks often go undetected and gradually degrade performance. This deck provides in-depth insights into identifying these hidden leaks using advanced heap analysis and profiling techniques, along with real-world case studies and practical solutions. Ideal for developers and performance engineers aiming to deepen their understanding of Java memory management and improve application stability.
This presentation explores code comprehension challenges in scientific programming based on a survey of 57 research scientists. It reveals that 57.9% of scientists have no formal training in writing readable code. Key findings highlight a "documentation paradox" where documentation is both the most common readability practice and the biggest challenge scientists face. The study identifies critical issues with naming conventions and code organization, noting that 100% of scientists agree readable code is essential for reproducible research. The research concludes with four key recommendations: expanding programming education for scientists, conducting targeted research on scientific code quality, developing specialized tools, and establishing clearer documentation guidelines for scientific software.
Presented at: The 33rd International Conference on Program Comprehension (ICPC '25)
Date of Conference: April 2025
Conference Location: Ottawa, Ontario, Canada
Preprint: https://arxiv.org/abs/2501.10037
Societal challenges of AI: biases, multilinguism and sustainabilityJordi Cabot
Towards a fairer, inclusive and sustainable AI that works for everybody.
Reviewing the state of the art on these challenges and what we're doing at LIST to test current LLMs and help you select the one that works best for you
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Secure Test Infrastructure: The Backbone of Trustworthy Software DevelopmentShubham Joshi
A secure test infrastructure ensures that the testing process doesn’t become a gateway for vulnerabilities. By protecting test environments, data, and access points, organizations can confidently develop and deploy software without compromising user privacy or system integrity.
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How to Batch Export Lotus Notes NSF Emails to Outlook PST Easily?steaveroggers
Migrating from Lotus Notes to Outlook can be a complex and time-consuming task, especially when dealing with large volumes of NSF emails. This presentation provides a complete guide on how to batch export Lotus Notes NSF emails to Outlook PST format quickly and securely. It highlights the challenges of manual methods, the benefits of using an automated tool, and introduces eSoftTools NSF to PST Converter Software — a reliable solution designed to handle bulk email migrations efficiently. Learn about the software’s key features, step-by-step export process, system requirements, and how it ensures 100% data accuracy and folder structure preservation during migration. Make your email transition smoother, safer, and faster with the right approach.
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Proactive Vulnerability Detection in Source Code Using Graph Neural Networks:...Ranjan Baisak
As software complexity grows, traditional static analysis tools struggle to detect vulnerabilities with both precision and context—often triggering high false positive rates and developer fatigue. This article explores how Graph Neural Networks (GNNs), when applied to source code representations like Abstract Syntax Trees (ASTs), Control Flow Graphs (CFGs), and Data Flow Graphs (DFGs), can revolutionize vulnerability detection. We break down how GNNs model code semantics more effectively than flat token sequences, and how techniques like attention mechanisms, hybrid graph construction, and feedback loops significantly reduce false positives. With insights from real-world datasets and recent research, this guide shows how to build more reliable, proactive, and interpretable vulnerability detection systems using GNNs.
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Explaining GitHub Actions Failures with Large Language Models Challenges, In...ssuserb14185
GitHub Actions (GA) has become the de facto tool that developers use to automate software workflows, seamlessly building, testing, and deploying code. Yet when GA fails, it disrupts development, causing delays and driving up costs. Diagnosing failures becomes especially challenging because error logs are often long, complex and unstructured. Given these difficulties, this study explores the potential of large language models (LLMs) to generate correct, clear, concise, and actionable contextual descriptions (or summaries) for GA failures, focusing on developers’ perceptions of their feasibility and usefulness. Our results show that over 80% of developers rated LLM explanations positively in terms of correctness for simpler/small logs. Overall, our findings suggest that LLMs can feasibly assist developers in understanding common GA errors, thus, potentially reducing manual analysis. However, we also found that improved reasoning abilities are needed to support more complex CI/CD scenarios. For instance, less experienced developers tend to be more positive on the described context, while seasoned developers prefer concise summaries. Overall, our work offers key insights for researchers enhancing LLM reasoning, particularly in adapting explanations to user expertise.
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Camel Desing Patterns Learned Through Blood, Sweat, and Tears
1. Apache Camel Design Patterns
Learned Through Blood, Sweat, and Tears
June 2016
Bilgin Ibryam
@bibryam
2. Apache Camel Design Patterns Learned Through Blood, Sweat, and Tears2
Bilgin Ibryam
● Senior Middleware Architect at Red Hat UK
● Apache Camel Committer and PMC member
● Apache OFBiz Committer and PMC member
● Author of Camel Design Patterns (new)
● Author of Apache Camel Message Routing
● Twitter: @bibryam
● Email: [email protected]
● Blog: http://ofbizian.com
● LinkedIn: http://www.linkedin.com/in/bibryam
3. Apache Camel Design Patterns Learned Through Blood, Sweat, and Tears3
Apache Camel Project Status
It has all necessary ingredients for a successful open source project.
● Community: 52 committers, 903 users
● Support by large vendors (Red Hat)
● Connectors (256), DataFormats (40)
● Enterprise Integration Patterns++
● Domain Specific Language
● Ecosystem: Karaf, ActiveMQ, CXF, Fabric,
Hawtio, Spring and others
● Monolith, SOA, Microservices, Serverless
Source https://www.openhub.net/p/camel/
Stats Date: 06/06/2016
4. Apache Camel Design Patterns Learned Through Blood, Sweat, and Tears4
Application Integration with Camel
What do you need to know to create great Camel applications?
5. Apache Camel Design Patterns Learned Through Blood, Sweat, and Tears5
Happy Path Scenarios
How Pipes and Filters Pattern looks like in Camel?
6. Apache Camel Design Patterns Learned Through Blood, Sweat, and Tears6
VETRO
What is a typical processing flow for a Camel route?
● Validate: validation, schematron, MSV, Jing, bean validation components
● Enrich: enrich and pollEnrich EIPs, custom beans
● Transform: Data formats, auto type conversion, templating components
● Route: Message routing EIPs
● Operate: this is the essence of the processing flow
7. Apache Camel Design Patterns Learned Through Blood, Sweat, and Tears7
Edge Component
Let's start with a simple Camel route that consumes files
8. Apache Camel Design Patterns Learned Through Blood, Sweat, and Tears8
Edge Component
How to expose the same business functionality to multiple consumers?
9. Apache Camel Design Patterns Learned Through Blood, Sweat, and Tears9
Edge Component
Encapsulate endpoint-specific details and prevent them from leaking into
the business logic of an integration flow.
10. Apache Camel Design Patterns Learned Through Blood, Sweat, and Tears10
Read vs Write Operations
How to evolve Read and Write operations independently?
11. Apache Camel Design Patterns Learned Through Blood, Sweat, and Tears11
CQRS
This decouples read from write operations to allow them to evolve
independently.
12. Apache Camel Design Patterns Learned Through Blood, Sweat, and Tears12
Unhappy Path Scenarios
Happy paths are the easy ones. More work is required for designing and
implementing the unhappy paths.
● Data Integrity Pattern
● Saga Pattern
● Retry Pattern
● Idempotent Filter Pattern
● Circuit Breaker Pattern
● Error Channels Pattern
● Throttling Pattern
13. Apache Camel Design Patterns Learned Through Blood, Sweat, and Tears13
Data Integrity
How hard can it be to copy files from one location to another?
Download Data Integrity Chapter: http://bit.ly/came-design-patterns-sample
14. Apache Camel Design Patterns Learned Through Blood, Sweat, and Tears14
Data Integrity
Transactional systems
Local transaction manager
Global transaction manager
15. Apache Camel Design Patterns Learned Through Blood, Sweat, and Tears15
Saga
How to avoid distributed transactions and ensure data consistency?
16. Apache Camel Design Patterns Learned Through Blood, Sweat, and Tears16
Saga
Ensures that each step of the business process has a compensating action
to undo the work completed in the case of partial failures.
17. Apache Camel Design Patterns Learned Through Blood, Sweat, and Tears17
Retry
To enable applications handle anticipated transient failures by transparently
retrying a failed operation with expectation it to be successful.
● Which failures to retry?
● How often to retry?
● Idempotency
● Monitoring
● Timeouts and SLAs
18. Apache Camel Design Patterns Learned Through Blood, Sweat, and Tears18
Retry
Camel RedeliveryPolicy
● The most well known retry mechanism in Camel
● Retries only the failing endpoint
● Fully in-memory
● Thread blocking behavior by default
● Can be asynchronous
● Good for small number of quick retries (in milliseconds)
19. Apache Camel Design Patterns Learned Through Blood, Sweat, and Tears19
Retry
ActiveMQ consumer RedeliveryPolicy
● Retries the message from the beginning of the Camel route
● Not used very often, but enabled by default
● Fully in-memory
● Thread blocking by default
● Good for small number of quick retries (in milliseconds)
20. Apache Camel Design Patterns Learned Through Blood, Sweat, and Tears20
Retry
ActiveMQ Broker Redelivery
● ActiveMQ specific and requires custom logic
● It will consume the message again from a queue
● Persisted at the broker rather than application memory
● Can be clustered and use fail over, load balancing, etc
● Good for long persisted retries (in minutes or hours)
21. Apache Camel Design Patterns Learned Through Blood, Sweat, and Tears21
Circuit Breaker
How to guard a system by cascading failures and slow responses from
other systems?
22. Apache Camel Design Patterns Learned Through Blood, Sweat, and Tears22
Circuit Breaker
Improves the stability and the resilience of a system by guarding integration
points from cascading failures and slow responses.
Closed state
Open state
23. Apache Camel Design Patterns Learned Through Blood, Sweat, and Tears23
Circuit Breaker
Improves the stability and the resilience of a system by guarding integration
points from cascading failures and slow responses.
24. Apache Camel Design Patterns Learned Through Blood, Sweat, and Tears24
Circuit Breaker
Two Circuit Breaker Implementations in Camel 2.18
25. Apache Camel Design Patterns Learned Through Blood, Sweat, and Tears25
Bulkhead
How to enforce resource partitioning and damage containment in order to
preserve partial functionality in the case of a failure?
26. Apache Camel Design Patterns Learned Through Blood, Sweat, and Tears26
Bulkhead
Enforces resource partitioning and damage containment in order to
preserve partial functionality in the case of a failure.
● Multi-threaded EIPs: Delayer, Multicast, Recipient List, Splitter, Threads,
Throttler, Wire Tap, Polling Consumer, ProducerTemplate, and OnCompletion.
● Async Error Handler
● Circuit Breaker EIP
Possible Camel bulkhead points:
27. Apache Camel Design Patterns Learned Through Blood, Sweat, and Tears27
Scalability Scenarios
Vertical scaling (performance tuning)
● Endpoints: messaging client buffers, DB client batching, template caching choices
● Concurrent consumers option: Seda, VM, JMS, RabbitMQ, Disruptor, AWS-SQS
● Data types choice: affects content based router, splitter, filter, aggregator
● Multithreading: parallel processing EIPs, threads DSL construct, Seda component,
asynchronous redelivery/retry
● Micro optimizations: log tuning, camel sampler EIP, disable JMX, disable message
history, disable original message record
● Startup/Shutdown: Use lazyLoadTypeConverters for a faster application startup, or
configure the shutdownStrategy for a faster shutdown
● Tune: JVM options, networking and operating system
Camel performance tuning blog post:
http://bit.ly/camel-tuning
Camel performance tuning blog post:
http://bit.ly/camel-tuning
28. Apache Camel Design Patterns Learned Through Blood, Sweat, and Tears28
Horizontal Scaling
Service Instance Pattern for accommodating increasing workloads.
29. Apache Camel Design Patterns Learned Through Blood, Sweat, and Tears29
Service Instance
Areas to consider before horizontally scaling a Camel application.
● Service state: load balancer, circuit breaker, resequencer, sampler,
throttler, idempotent consumer and aggregator are stateful EIPs!
● Request dispatcher: Messaging, HTTP, file (what about locking?)
● Message ordering: exclusive consumer, message groups, consumer
priority, message priority, virtual topics
● Singleton service requirements: for batch jobs, and concurrent polling
● Other resource contention and coupling considerations
30. Apache Camel Design Patterns Learned Through Blood, Sweat, and Tears30
What did we cover so far?
31. Apache Camel Design Patterns Learned Through Blood, Sweat, and Tears31
How patterns are changing?
What is happening in the IT industry today?
● Canonical Data Model
● Edge Component
● Reusable Route
● Runtime Reconfiguration
● Singleton Service
● Batch jobs in JVM
● Bounded Context
● Standalone services
● Favor code duplication
● Less configuration, more redeployment
● Container managed singleton
● Container scheduling
● Circuit Breaker, Bulkhead, Health checks..
32. Apache Camel Design Patterns Learned Through Blood, Sweat, and Tears32
Win a print copy of Camel Design Patterns
When was the first commit to Apache Camel project done?
33. Apache Camel Design Patterns Learned Through Blood, Sweat, and Tears33
Win a print copy of Camel Design Patterns
When was the first commit to Apache Camel project done?
34. More Information
Learn more about Apache Camel: http://camel.apache.org
Check out Camel Design Patterns: https://leanpub.com/camel-design-patterns
Develop Apache Camel based integrations using Red Hat JBoss Fuse: red.ht/FuseDev