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Patterns overview for enterprise application integration
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confluent
In this presentation, I will explain event driven architecture, describe the different types of events, demonstrate how events can be related and orchestrated, and provide a basic understanding of how this method can drive the architecture of enterprise systems. In addition to understanding the concepts of event driven architecture, we will explore a working sample built using an open-source .NET messaging framework called MassTransit.
Event Driven Architecture
Event Driven Architecture
Chris Patterson
Learn the latest industry trends surrounding REST API standardization and what this means for your roadmap. OData is an OASIS standard REST API and has been established among tech companies such as Microsoft, SAP, CA, IBM and Salesforce. GraphQL was created by Facebook in 2015 and has already been deployed at tech companies such as Facebook, Shopify and Intuit. ORDS is the Oracle REST API and delivers similar standardization for Oracle-centric applications.
REST API debate: OData vs GraphQL vs ORDS
REST API debate: OData vs GraphQL vs ORDS
Sumit Sarkar
Temporary description
Simplify DevOps with Microservices and Mobile Backends.pptx
Simplify DevOps with Microservices and Mobile Backends.pptx
ssuser5faa791
Watch this talk here: https://www.confluent.io/online-talks/benefits-of-stream-processing-and-apache-kafka-use-cases-on-demand This talk explains how companies are using event-driven architecture to transform their business and how Apache Kafka serves as the foundation for streaming data applications. Learn how major players in the market are using Kafka in a wide range of use cases such as microservices, IoT and edge computing, core banking and fraud detection, cyber data collection and dissemination, ESB replacement, data pipelining, ecommerce, mainframe offloading and more. Also discussed in this talk are the differences between Apache Kafka and Confluent Platform. This session is part 1 of 4 in our Fundamentals for Apache Kafka series.
Benefits of Stream Processing and Apache Kafka Use Cases
Benefits of Stream Processing and Apache Kafka Use Cases
confluent
Netflix Data Pipeline and how we operate Kafka in AWS
Netflix Data Pipeline With Kafka
Netflix Data Pipeline With Kafka
Allen (Xiaozhong) Wang
In any enterprise or cloud application, Task scheduling is a key requirement. A highly available and fault-tolerant task scheduling will help us to improve our business goals. A classic task scheduling infrastructure is typically backed by databases. The instances/service that performs the scheduling, loads the task definitions from the database into memory and performs the task scheduling. This kind of infrastructure creates issues like stateful services, inability to scale the services horizontally, being prone to frequent failures, etc., If the state of these kinds of services is not maintained well, it may lead to inconsistent and integrity issues. To mitigate these issues, we will explore a high available and fault-tolerant task scheduling infrastructure using Kafka, Kafka Streams, and State Store.
High Available Task Scheduling Design using Kafka and Kafka Streams | Naveen ...
High Available Task Scheduling Design using Kafka and Kafka Streams | Naveen ...
HostedbyConfluent
A brief overview of the significance of API Gateways in microservices architecture by providing Kong as an example. Slide 2: Monolith Vs Microservices Monolith: Pros- Simple to implement Less integration test - easy to test Easy to ship Fast development Cons- Violates Open-Close principle Nightmare when it comes to managing the code Difficult to enhance Bigger artifacts Hard to replace individual components like DB, Logger etc. Microservices- Pros- Easy to manage One reason to change Dynamic scaling Single responsibility Cons- Multiple points of failure Hard to test - rich integration tests required Heterogeneity in infrastructure Slide 3: API Gateway Pattern It is microservices design pattern. An API gateway is a service which is the entry point into the application from the outside world. It’s responsible for request routing, API composition, and other functions, such as authentication. There are a lot of issues when client is talking to multiple components to get the job done. These include multiple proxies at client side, different logic to handle different calls, client needs to know the implementation details of server. A much better approach is for a client to make a single request to what’s known as an API gateway. An API gateway is a service which is the single entry-point for API requests into an application. It’s similar to the Facade pattern from object-oriented design. Like a facade, an API gateway encapsulates the application’s internal architecture and provides an API to its clients. It might also have other responsibilities, such as authentication, monitoring, and rate limiting. These are also termed as BFF - Backend For Frontend Slide 4: API Gateway in Action It acts as a “backend for the frontend”. The clients do not know which services they are talking to. They communicate with a single interface - API Gateway. The gateway resolves the client requests and distributes them to respective services. Slide 7: Kong Architecture Kong is a cloud-native, fast, scalable, and distributed Microservice Abstraction Layer (also known as an API Gateway, API Middleware or in some cases Service Mesh). Made available as an open-source project in 2015, its core values are high performance and extensibility. Actively maintained, Kong is widely used in production at companies ranging from startups to Global 5000 as well as government organizations.
Api gateway in microservices
Api gateway in microservices
Kunal Hire
Was ist angesagt?
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Apache Kafka Architecture & Fundamentals Explained
Apache Kafka Architecture & Fundamentals Explained
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Enterprise Integration Patterns
APIsecure 2023 - API orchestration: to build resilient applications, Cherish ...
APIsecure 2023 - API orchestration: to build resilient applications, Cherish ...
XStream: stream processing platform at facebook
XStream: stream processing platform at facebook
Event-Driven Architecture (EDA)
Event-Driven Architecture (EDA)
Introduction and Overview of Apache Kafka, TriHUG July 23, 2013
Introduction and Overview of Apache Kafka, TriHUG July 23, 2013
When NOT to use Apache Kafka?
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A Thorough Comparison of Delta Lake, Iceberg and Hudi
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Designing and Implementing a Real-time Data Lake with Dynamically Changing Sc...
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Infrastructure at Scale: Apache Kafka, Twitter Storm & Elastic Search (ARC303...
Building Event Driven Architectures with Kafka and Cloud Events (Dan Rosanova...
Building Event Driven Architectures with Kafka and Cloud Events (Dan Rosanova...
Event Driven Architecture
Event Driven Architecture
REST API debate: OData vs GraphQL vs ORDS
REST API debate: OData vs GraphQL vs ORDS
Simplify DevOps with Microservices and Mobile Backends.pptx
Simplify DevOps with Microservices and Mobile Backends.pptx
Benefits of Stream Processing and Apache Kafka Use Cases
Benefits of Stream Processing and Apache Kafka Use Cases
Netflix Data Pipeline With Kafka
Netflix Data Pipeline With Kafka
High Available Task Scheduling Design using Kafka and Kafka Streams | Naveen ...
High Available Task Scheduling Design using Kafka and Kafka Streams | Naveen ...
Api gateway in microservices
Api gateway in microservices
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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
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Patterns for Enterprise Integration Success
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WSO2
Employing Enterprise Application Integration (EAI)
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Hossam Karim
In 2015, I had two talks about Enterprise Integration Patterns at OOP 2015 in Munich, Germany and at JavaDay 2015 in Kiev, Ukraine. I reused a talk from 2013 and updated it with current trends to show how important Enterprise Integration Patterns (EIP) are everywhere today and in the upcoming years.
Enterprise Integration Patterns Revisited (again) for the Era of Big Data, In...
Enterprise Integration Patterns Revisited (again) for the Era of Big Data, In...
Kai Wähner
At a high level, both SOA and web APIs seem to solve the same problem – expose business function in real-time and in a reusable way. This tutorial looks at how these initiatives are different and how they align into an evolving integration architecture. It discusses how API Management differs from the integration architectures that came before it, such as SOA and EAI.
Differentiating between web APIs, SOA, & integration…and why it matters
Differentiating between web APIs, SOA, & integration…and why it matters
Kim Clark
Overview of Enterprise Application Integration Technologies. Enterprise Application Integration, or EAI in short, aims at integrating different applications into an IT application landscape. Traditionally, EAI was understood as using the same communication infrastructure by all applications without service-orientation in mind. This meant that the benefits of a shared infrastructure were limited while driving up costs through additional integration platforms. Service Oriented Architectures (SOA) brought a new paradigm by decomposing applications into reusable and shareable services. Service orientation requires careful design of services. A hierarchic scheme of services may help to define a suitable service decomposition. While SOA is technically based on big web service technologies, namely SOAP, WSDL and BPEL, WOA or Web Oriented Architecture stands for the lightweight service paradigm. WOA makes use of REST-based technologies like JSON and HTTP. In many cases, an Enterprise Service Bus (ESB) is used as an infrastructure element to achieve the technical integration of the services. The ESB core functions like message routing, filtering and transformation provide the mediation services required to integrate heterogeneous application landscapes.
Enterprise Application Integration Technologies
Enterprise Application Integration Technologies
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Rest Essentials
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Easy Enterprise Integration Patterns with Apache Camel, ActiveMQ and ServiceMix
elliando dias
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Smart Enterprise Application Integration with Apache Camel
Smart Enterprise Application Integration with Apache Camel
Kai Wähner
Data exchanges between companies increase a lot. The number of applications which must be integrated increases, too. The interfaces use different technologies, protocols and data formats. Nevertheless, the integration of these applications shall be modeled in a standardized way, realized efficiently and supported by automatic tests. Three integration frameworks are available in the JVM environment, which fulfil these requirements: Apache Camel, Spring Integration and Mule. They implement the well-known Enteprise Integration Patterns (EIP) and therefore offers a standardized, domain-specific language to integrate applications. These Integration Frameworks can be used in almost every integration project within the JVM environment - no matter which technologies, transport protocols or data formats are used. All integration projects can be realized in a consistent way without redundant boilerplate code. This session shows and compares the three alternatives and discusses their pros and cons. Besides, a recommendation will be given when to use a more powerful Enterprise Service Bus (ESB) instead of one of these frameworks.
Scandev / SDC2013 - Spoilt for Choice: Which Integration Framework to use – A...
Scandev / SDC2013 - Spoilt for Choice: Which Integration Framework to use – A...
Kai Wähner
A presentation based on "The Art of Unit Testing" book by Roy Osherove to cover the key moments
Unit Testing
Unit Testing
Sergey Podolsky
To View this webinar replay: http://ecast.opensystemsmedia.com/316 As Operational Technologies (OT) like embedded devices, control and monitoring systems are increasingly integrated with Information Technology (IT) systems running in the back office, interaction patterns between systems are becoming more complex and diverse. Publish-Subscribe is the most commonly used messaging pattern for OT systems. It provides the real-time information access, scalability, and loose coupling required for integration of these types of systems. IT and OT integration, however, commonly requires messaging patterns that provide stronger end-to-end properties, such as Guaranteed Delivery, Request-Reply, and (load-balancing) Queues. RTI is greatly enhancing its infrastructure software with new messaging patterns that combine the performance, scalability, and reliability needed by OT systems with the integration and flexible messaging capabilities of IT systems.
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First Operational Technology (OT) High Performance Messaging Patterns for Ent...
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Enterprise integration patterns, Apache Camel, EIP
Enterprise Integration Patterns with Apache Camel
Enterprise Integration Patterns with Apache Camel
Ioan Eugen Stan
JavaOne 2009 presentation on Enterprise Integration Patterns In Practice by Andreas Egloff and Bruce Snyder
TS 4839 - Enterprise Integration Patterns in Practice
TS 4839 - Enterprise Integration Patterns in Practice
aegloff
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Google Protocol Buffers
Google Protocol Buffers
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Node Js, AngularJs and Express Js Tutorial - Learn Node.js framework in simple and easy steps starting from Introduction, Environment Setup, Node Package Manager, Node Callbacks Concept, Node Buffers Module, Node Streams, Node File System, Node Utility Modules, Node Web Module, Node Express Application, Node RESTFul API, Node Scaling Application
Node Js, AngularJs and Express Js Tutorial
Node Js, AngularJs and Express Js Tutorial
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Introduction to Node.JS Express
Eueung Mulyana
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Enterprise Integration Patterns
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Employing Enterprise Application Integration (EAI)
Employing Enterprise Application Integration (EAI)
Enterprise Integration Patterns Revisited (EIP) for the Era of Big Data, Inte...
Enterprise Integration Patterns Revisited (EIP) for the Era of Big Data, Inte...
JDC2008 - Enterprise Integration and Service Oriented Design
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Enterprise Integration Patterns Revisited (again) for the Era of Big Data, In...
Enterprise Integration Patterns Revisited (again) for the Era of Big Data, In...
Differentiating between web APIs, SOA, & integration…and why it matters
Differentiating between web APIs, SOA, & integration…and why it matters
Enterprise Application Integration Technologies
Enterprise Application Integration Technologies
Rest Essentials
Rest Essentials
Easy Enterprise Integration Patterns with Apache Camel, ActiveMQ and ServiceMix
Easy Enterprise Integration Patterns with Apache Camel, ActiveMQ and ServiceMix
Smart Enterprise Application Integration with Apache Camel
Smart Enterprise Application Integration with Apache Camel
Scandev / SDC2013 - Spoilt for Choice: Which Integration Framework to use – A...
Scandev / SDC2013 - Spoilt for Choice: Which Integration Framework to use – A...
Unit Testing
Unit Testing
First Operational Technology (OT) High Performance Messaging Patterns for Ent...
First Operational Technology (OT) High Performance Messaging Patterns for Ent...
Enterprise Integration Patterns with Apache Camel
Enterprise Integration Patterns with Apache Camel
TS 4839 - Enterprise Integration Patterns in Practice
TS 4839 - Enterprise Integration Patterns in Practice
Google Protocol Buffers
Google Protocol Buffers
Node Js, AngularJs and Express Js Tutorial
Node Js, AngularJs and Express Js Tutorial
Introduction to Node.JS Express
Introduction to Node.JS Express
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Diameter Presentation
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Introduction to SignalR
Introduction to SignalR
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Creating responsive and interactive web applications has always been one of my hidden dreams. One of the biggest show stopper has been the communication between server and clients. The rise of websockets now open some space for a brand new kind of applications; but we need a library that makes things 'easy' for us and that is able to fall back on previous solutions when the latest technologies are not available. Suddenly on the ASP.NET scene appears SignalR: a persistence connection abstraction library that helps ASP.NET developers deal with 'real time' client-server communication sceneries.
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complete web service1.ppt
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Test automation is a cornerstone of software development and quality assurance in today's rapidly evolving digital landscape. Its significance cannot be overstated. Businesses can enhance efficiency, productivity, and accelerate software delivery to market through automation, streamlining testing processes effectively. This comprehensive guide addresses the best practices for test automation in 2024. It offers a detailed checklist to empower you to optimize your automation efforts and maintain a competitive edge.
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Foundation models are machine learning models which are easily capable of performing variable tasks on large and huge datasets. FMs have managed to get a lot of attention due to this feature of handling large datasets. It can do text generation, video editing to protein folding and robotics. In case we believe that FMs can help the hospitals and patients in any way, we need to perform some important evaluations, tests to test these assumptions. In this review, we take a walk through Fms and their evaluation regimes assumed clinical value. To clarify on this topic, we reviewed no less than 80 clinical FMs built from the EMR data. We added all the models trained on structured and unstructured data. We are referring to this combination of structured and unstructured EMR data or clinical data.
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Data spaces in distributed environments should be allowed to evolve in agile ways providing data space owners with large flexibility about which data they store. Agility and heterogeneity, however, jeopardize data exchanges because representations may build on varying ontologies and data consumers may not rely on the semantic correctness of their queries in the context of semantically heterogeneous, evolving data spaces. Graph data spaces are one example of a powerful model for representing and querying data whose semantics may change over time. To assert and enforce conditions on individual graph data spaces, shape languages (e.g SHACL) have been developed. We investigate the question of how querying and programming can be guarded by reasoning over SHACL constraints in a distributed setting and we sketch a picture of how a future landscape based on semantically heterogeneous data spaces might look like.
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In today's dynamic e-commerce landscape, the payment gateway emerges as a linchpin, ensuring smooth and secure transactions between buyers and sellers. In this discourse, we delve into the meticulous process of devising test cases tailored for scrutinizing payment gateways. Crafting precise test cases for payment gateways is a quintessential responsibility for testers operating within the service industry. This article meticulously explores pivotal scenarios integral to how to test payment gateways, coupled with essential guidelines for drafting effective test cases.
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Enterprise Integration Patterns
1.
Enterprise [Application] Integration
Patterns Overview Sergey Podolsky sergey.podolsky@gmail.com
2.
The Bible of
Enterprise Integration Patterns http://www.eaipatterns.com/toc.html
3.
Visual Pattern Language
4.
Basic Definitions
5.
Integration styles
6.
In Nova
7.
Main building blocks •
Endpoint • Channel • Message
8.
Messages
9.
Message types
10.
In Nova
11.
Message attributes Return address:
Correlation ID: Expiration time:
12.
Messaging Channels
13.
Message exchange styles
14.
Invalid Message Channel Dead
Letter Channel
15.
Datatype Channel Channel Adapter
16.
Message Endpoints
17.
Messaging endpoints
18.
Messaging endpoints
19.
Messaging endpoints Service activatorCompeting
consumers Risk Engine Farm Request Router
20.
Pattern categories • Message
routing patterns • Message transformation patterns • Message management patterns
21.
Message routing patterns
22.
Pipes and filters In
Nova Workunit NOF TCP Results Writer Post ProcessorCRF NCRF
23.
Content-Based Router Instrument-Valuation Request TCP X2E Zenith ZA MTM Workunit Nova
Request Router
24.
Dynamic Router
25.
Sequencer (Splitter) In Nova NIF
(Nova Request) Request Router Instrument-Valuation Requests
26.
Aggregator Instrument-Valuation Request (package) NOF (Nova
Result) Package Aggregator TCP X2E Packager Splitter Router
27.
In Nova -
Batching • ILS, Snap Service, Zenith • Correlation ID = Request ID • Completion = on time-out || on max count
28.
Broadcast with Aggregate
Response Completion criterion: • Timeout • Count • External event
29.
Recipient List
30.
Resequencer Example – TCP
datagrams
31.
Routing Slip Linear flow
32.
Process manager Complex message
flow
33.
Message broker Integration Spaghetti
as a Result of Point-to-Point Connections A Hierarchy of Message Brokers Provides Decoupling while Avoiding the "Über-Broker"
34.
Message transformation patterns
35.
Message Translator In Nova Results
Processor CRF NCRF
36.
Envelope Wrapper /
Unwrapper Example: TCP/IP
37.
Content Enricher In Nova ILS
Enricher Instrument Instrument Risk Engine ILS
38.
Content Filter In Nova
– Message Flattening Package Hierarchy Package constituents
39.
Message filter In Nova NCRF
NCRF (cancelled) NCRF Cancelled Requests Filter NCRF NCRF
40.
Claim Check use the
Claim Check to hide the sensitive portions of data
41.
Normalizer
42.
In Nova Results Processor CRF (X2E) CRF (TCP) NCRF
NCRF Error NOF Package Hierarchy RR2PP NOF NOF Nova Post Processor
43.
Canonical Data Model
44.
System Management Patterns
45.
Control Bus • Configuration •
Heartbeat • Test Messages • Exceptions • Statistics • Live Console In Nova: • Request Router – configuration • Progress Monitor – Statuses
46.
Detour Purposes: • Debugging • Validation •
Testing • Inspection
47.
WireTap In Nova –
logging messages to disk
48.
Message History TCP:PID ResultsWriter:PID NCRF CRF TCP:PID ResultsWriterTCPWorkunit Diagnostic Info
49.
Message Store 1) Xvis,
Instrumentation DB 2) Message logging to disk in Nova
50.
Smart Proxy The Smart
Proxy can store this data in two places: • Inside the Message • Inside the Smart Proxy
51.
Test Message
52.
Message Purger
53.
Enterprise Message Brokers
54.
Frameworks that implement
EIP
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