Domain Driven Design provides not only the strategic guidelines for decomposing a large system into microservices, but also offers the main tactical pattern that helps in decoupling microservices. The presentation will focus on the way domain events could be implemented using Kafka and the trade-offs between consistency and availability that are supported by Kafka.
https://youtu.be/P6IaxNcn-Ag?t=1466
Building Cloud-Native App Series - Part 2 of 11
Microservices Architecture Series
Event Sourcing & CQRS,
Kafka, Rabbit MQ
Case Studies (E-Commerce App, Movie Streaming, Ticket Booking, Restaurant, Hospital Management)
Building Cloud-Native App Series - Part 11 of 11
Microservices Architecture Series
Service Mesh - Observability
- Zipkin
- Prometheus
- Grafana
- Kiali
Any team that has made the jump from building monoliths to building microservices knows the complexities you must overcome to build a system that is functional and maintainable. Building a microservice architecture that is low latency and only communicates using REST APIs is even more tricky, with high latency for requests being a common concern. This talk explains how you can use events as the backbone of your microservice architecture and build an efficient, event-driven system. It covers how to get started with designing your microservice architecture and the key requirements any system needs to fulfil. It also introduces the different patterns you will encounter in event-driven architectures and the advantages and disadvantages of these choices. Finally it explains why Apache Kafka is a great choice for event-driven microservices.
Real-Life Use Cases & Architectures for Event Streaming with Apache KafkaKai Wähner
Streaming all over the World: Real-Life Use Cases & Architectures for Event Streaming with Apache Kafka.
Learn about various case studies for event streaming with Apache Kafka across industries. The talk explores architectures for real-world deployments from Audi, BMW, Disney, Generali, Paypal, Tesla, Unity, Walmart, William Hill, and more. Use cases include fraud detection, mainframe offloading, predictive maintenance, cybersecurity, edge computing, track&trace, live betting, and much more.
The Top 5 Apache Kafka Use Cases and Architectures in 2022Kai Wähner
I see the following topics coming up more regularly in conversations with customers, prospects, and the broader Kafka community across the globe:
Kappa Architecture: Kappa goes mainstream to replace Lambda and Batch pipelines (that does not mean that there is no batch processing anymore). Examples: Kafka-powered Kappa architectures from Uber, Disney, Shopify, and Twitter.
Hyper-personalized Omnichannel: Retail and customer communication across online and offline channels becomes the new black, including context-specific upselling, recommendations, and location-based services. Examples: Omnichannel Retail and Customer 360 in Real-Time with Apache Kafka.
Multi-Cloud Deployments: Business units and IT infrastructures span across regions, continents, and cloud providers. Linking clusters for bi-directional replication of data in real-time becomes crucial for many business models. Examples: Global Kafka deployments.
Edge Analytics: Low latency requirements, cost efficiency, or security requirements enforce the deployment of (some) event streaming use cases at the far edge (i.e., outside a data center), for instance, for predictive maintenance and quality assurance on the shop floor level in smart factories. Examples: Edge analytics with Kafka.
Real-time Cybersecurity: Situational awareness and threat intelligence need to process massive data in real-time to defend against cyberattacks successfully. The many successful ransomware attacks across the globe in 2021 were a warning for most CIOs. Examples: Cybersecurity for situational awareness and threat intelligence in real-time.
Building Cloud-Native App Series - Part 4 of 11
Microservices Architecture Series
NoSQL vs SQL
Redis, MongoDB, AWS DynamoDB
Big Data Design Patterns
Sharding, Partitions
Building Cloud-Native App Series - Part 7 of 11
Microservices Architecture Series
Containers Docker Kind Kubernetes Istio
- Pods
- ReplicaSet
- Deployment (Canary, Blue-Green)
- Ingress
- Service
Building Cloud-Native App Series - Part 2 of 11
Microservices Architecture Series
Event Sourcing & CQRS,
Kafka, Rabbit MQ
Case Studies (E-Commerce App, Movie Streaming, Ticket Booking, Restaurant, Hospital Management)
Building Cloud-Native App Series - Part 11 of 11
Microservices Architecture Series
Service Mesh - Observability
- Zipkin
- Prometheus
- Grafana
- Kiali
Any team that has made the jump from building monoliths to building microservices knows the complexities you must overcome to build a system that is functional and maintainable. Building a microservice architecture that is low latency and only communicates using REST APIs is even more tricky, with high latency for requests being a common concern. This talk explains how you can use events as the backbone of your microservice architecture and build an efficient, event-driven system. It covers how to get started with designing your microservice architecture and the key requirements any system needs to fulfil. It also introduces the different patterns you will encounter in event-driven architectures and the advantages and disadvantages of these choices. Finally it explains why Apache Kafka is a great choice for event-driven microservices.
Real-Life Use Cases & Architectures for Event Streaming with Apache KafkaKai Wähner
Streaming all over the World: Real-Life Use Cases & Architectures for Event Streaming with Apache Kafka.
Learn about various case studies for event streaming with Apache Kafka across industries. The talk explores architectures for real-world deployments from Audi, BMW, Disney, Generali, Paypal, Tesla, Unity, Walmart, William Hill, and more. Use cases include fraud detection, mainframe offloading, predictive maintenance, cybersecurity, edge computing, track&trace, live betting, and much more.
The Top 5 Apache Kafka Use Cases and Architectures in 2022Kai Wähner
I see the following topics coming up more regularly in conversations with customers, prospects, and the broader Kafka community across the globe:
Kappa Architecture: Kappa goes mainstream to replace Lambda and Batch pipelines (that does not mean that there is no batch processing anymore). Examples: Kafka-powered Kappa architectures from Uber, Disney, Shopify, and Twitter.
Hyper-personalized Omnichannel: Retail and customer communication across online and offline channels becomes the new black, including context-specific upselling, recommendations, and location-based services. Examples: Omnichannel Retail and Customer 360 in Real-Time with Apache Kafka.
Multi-Cloud Deployments: Business units and IT infrastructures span across regions, continents, and cloud providers. Linking clusters for bi-directional replication of data in real-time becomes crucial for many business models. Examples: Global Kafka deployments.
Edge Analytics: Low latency requirements, cost efficiency, or security requirements enforce the deployment of (some) event streaming use cases at the far edge (i.e., outside a data center), for instance, for predictive maintenance and quality assurance on the shop floor level in smart factories. Examples: Edge analytics with Kafka.
Real-time Cybersecurity: Situational awareness and threat intelligence need to process massive data in real-time to defend against cyberattacks successfully. The many successful ransomware attacks across the globe in 2021 were a warning for most CIOs. Examples: Cybersecurity for situational awareness and threat intelligence in real-time.
Building Cloud-Native App Series - Part 4 of 11
Microservices Architecture Series
NoSQL vs SQL
Redis, MongoDB, AWS DynamoDB
Big Data Design Patterns
Sharding, Partitions
Building Cloud-Native App Series - Part 7 of 11
Microservices Architecture Series
Containers Docker Kind Kubernetes Istio
- Pods
- ReplicaSet
- Deployment (Canary, Blue-Green)
- Ingress
- Service
CQRS and Event Sourcing, An Alternative Architecture for DDDDennis Doomen
Most of us will be familiar with the standard 3- or 4-layer architecture you often see in larger enterprise systems. Some are already practicing Domain Driven Design and work together with the business to clarify the domain concepts. Perhaps you’ve noticed that is difficult to get the intention of the 'verbs' from that domain into this standard architecture. If performance is an important requirement as well, then you might have discovered that an Object-Relational Mapper and a relational database are not always the best solution.
One of the main reasons for this is the fact that the interests of a consistent domain that takes into account the many business rules, and those of data reporting and presentation are conflicting. That’s why Betrand Meyer introduced the Command Query Separation principle.
An architecture based on this principle combined with the Event Sourcing concept provides the ideal architecture for building high-performance systems designed using DDD. Well-known bloggers like Udi Dahan and Greg Young have already spent quite a lot of of posts on this, and this year’s Developer Days had some coverage as well.
But how do you build such a system with the. NET framework? Is it really as complex as some claim, or is just different work?
Docker Kubernetes Istio
Understanding Docker and creating containers.
Container Orchestration based on Kubernetes
Blue Green Deployment, AB Testing, Canary Deployment, Traffic Rules based on Istio
Melbourne Jan 2019 - Microservices adoption anti-patterns: Obstacles to decom...Chris Richardson
A typical mission-critical enterprise application is a large, complex monolith developed by large team. The velocity of software delivery is usually slow, and the team struggles to keep up with the demands of the business. Consequently, many enterprise applications are good candidates to be migrated to the microservice architecture. As you might expect, migrating to microservices requires an enterprise to tackle numerous technology-related challenges. But enterprises often encounter obstacles that have less to do with technology and more to do with strategy, process, and organization.
In this talk I describe the essential characteristics of the microservice architecture.You will learn about its benefits and its drawbacks. I describe several anti-patterns of microservices adoption that he’s observed while working with clients around the world. You’ll learn the challenges that enterprises often face and how to overcome them as well as how to avoid the potholes when escaping monolithic hell.
Building Cloud-Native App Series - Part 1 of 11
Microservices Architecture Series
Design Thinking, Lean Startup, Agile (Kanban, Scrum),
User Stories, Domain-Driven Design
Confluent REST Proxy and Schema Registry (Concepts, Architecture, Features)Kai Wähner
High level introduction to Confluent REST Proxy and Schema Registry (leveraging Apache Avro under the hood), two components of the Apache Kafka open source ecosystem. See the concepts, architecture and features.
Serverless Kafka on AWS as Part of a Cloud-native Data Lake ArchitectureKai Wähner
AWS Data Lake / Lake House + Confluent Cloud for Serverless Apache Kafka. Learn about use cases, architectures, and features.
Data must be continuously collected, processed, and reactively used in applications across the entire enterprise - some in real time, some in batch mode. In other words: As an enterprise becomes increasingly software-defined, it needs a data platform designed primarily for "data in motion" rather than "data at rest."
Apache Kafka is now mainstream when it comes to data in motion! The Kafka API has become the de facto standard for event-driven architectures and event streaming. Unfortunately, the cost of running it yourself is very often too expensive when you add factors like scaling, administration, support, security, creating connectors...and everything else that goes with it. Resources in enterprises are scarce: this applies to both the best team members and the budget.
The cloud - as we all know - offers the perfect solution to such challenges.
Most likely, fully-managed cloud services such as AWS S3, DynamoDB or Redshift are already in use. Now it is time to implement "fully-managed" for Kafka as well - with Confluent Cloud on AWS.
Building a central integration layer that doesn't care where or how much data is coming from.
Implementing scalable data stream processing to gain real-time insights
Leveraging fully managed connectors (like S3, Redshift, Kinesis, MongoDB Atlas & more) to quickly access data
Confluent Cloud in action? Let's show how ao.com made it happen!
Translated with www.DeepL.com/Translator (free version)
Introducing Apache Kafka - a visual overview. Presented at the Canberra Big Data Meetup 7 February 2019. We build a Kafka "postal service" to explain the main Kafka concepts, and explain how consumers receive different messages depending on whether there's a key or not.
Event-Driven Microservices architecture has gained a lot of attention recently. The trend in the industry is to move away from Monolithic applications to Microservices to innovate faster. While Microservices have their benefits, implementing them is hard. This talk focuses on the challenges faced and how to solve them.
It covers topics like using Domain Driven Design to break functionality into small parts. Various communication patterns among Microservices are also discussed.
One major drawback is the problem of distributed data management, as each Microservice has its own database. Event-Driven Architecture enables a way to make microservices work together and the talks show how to use architectural patterns like Event Sourcing & CQRS to implement them.
Another implementation challenge is to manage transactions that update entities owned by multiple services in an eventually consistent fashion. This challenge is solved using sagas, which can be thought of as Long running transactions that use compensating actions to handle failures.
The objective of the talk is to show how to implement highly distributed Event Driven Microservices architecture that are scalable and easy to maintain.
New Features in Confluent Platform 6.0 / Apache Kafka 2.6Kai Wähner
New Features in Confluent Platform 6.0 / Apache Kafka 2.6, including REST Proxy and API, Tiered Storage for AWS S3 and GCP GCS, Cluster Linking (On-Premise, Edge, Hybrid, Multi-Cloud), Self-Balancing Clusters), ksqlDB.
CQRS and Event Sourcing, An Alternative Architecture for DDDDennis Doomen
Most of us will be familiar with the standard 3- or 4-layer architecture you often see in larger enterprise systems. Some are already practicing Domain Driven Design and work together with the business to clarify the domain concepts. Perhaps you’ve noticed that is difficult to get the intention of the 'verbs' from that domain into this standard architecture. If performance is an important requirement as well, then you might have discovered that an Object-Relational Mapper and a relational database are not always the best solution.
One of the main reasons for this is the fact that the interests of a consistent domain that takes into account the many business rules, and those of data reporting and presentation are conflicting. That’s why Betrand Meyer introduced the Command Query Separation principle.
An architecture based on this principle combined with the Event Sourcing concept provides the ideal architecture for building high-performance systems designed using DDD. Well-known bloggers like Udi Dahan and Greg Young have already spent quite a lot of of posts on this, and this year’s Developer Days had some coverage as well.
But how do you build such a system with the. NET framework? Is it really as complex as some claim, or is just different work?
Docker Kubernetes Istio
Understanding Docker and creating containers.
Container Orchestration based on Kubernetes
Blue Green Deployment, AB Testing, Canary Deployment, Traffic Rules based on Istio
Melbourne Jan 2019 - Microservices adoption anti-patterns: Obstacles to decom...Chris Richardson
A typical mission-critical enterprise application is a large, complex monolith developed by large team. The velocity of software delivery is usually slow, and the team struggles to keep up with the demands of the business. Consequently, many enterprise applications are good candidates to be migrated to the microservice architecture. As you might expect, migrating to microservices requires an enterprise to tackle numerous technology-related challenges. But enterprises often encounter obstacles that have less to do with technology and more to do with strategy, process, and organization.
In this talk I describe the essential characteristics of the microservice architecture.You will learn about its benefits and its drawbacks. I describe several anti-patterns of microservices adoption that he’s observed while working with clients around the world. You’ll learn the challenges that enterprises often face and how to overcome them as well as how to avoid the potholes when escaping monolithic hell.
Building Cloud-Native App Series - Part 1 of 11
Microservices Architecture Series
Design Thinking, Lean Startup, Agile (Kanban, Scrum),
User Stories, Domain-Driven Design
Confluent REST Proxy and Schema Registry (Concepts, Architecture, Features)Kai Wähner
High level introduction to Confluent REST Proxy and Schema Registry (leveraging Apache Avro under the hood), two components of the Apache Kafka open source ecosystem. See the concepts, architecture and features.
Serverless Kafka on AWS as Part of a Cloud-native Data Lake ArchitectureKai Wähner
AWS Data Lake / Lake House + Confluent Cloud for Serverless Apache Kafka. Learn about use cases, architectures, and features.
Data must be continuously collected, processed, and reactively used in applications across the entire enterprise - some in real time, some in batch mode. In other words: As an enterprise becomes increasingly software-defined, it needs a data platform designed primarily for "data in motion" rather than "data at rest."
Apache Kafka is now mainstream when it comes to data in motion! The Kafka API has become the de facto standard for event-driven architectures and event streaming. Unfortunately, the cost of running it yourself is very often too expensive when you add factors like scaling, administration, support, security, creating connectors...and everything else that goes with it. Resources in enterprises are scarce: this applies to both the best team members and the budget.
The cloud - as we all know - offers the perfect solution to such challenges.
Most likely, fully-managed cloud services such as AWS S3, DynamoDB or Redshift are already in use. Now it is time to implement "fully-managed" for Kafka as well - with Confluent Cloud on AWS.
Building a central integration layer that doesn't care where or how much data is coming from.
Implementing scalable data stream processing to gain real-time insights
Leveraging fully managed connectors (like S3, Redshift, Kinesis, MongoDB Atlas & more) to quickly access data
Confluent Cloud in action? Let's show how ao.com made it happen!
Translated with www.DeepL.com/Translator (free version)
Introducing Apache Kafka - a visual overview. Presented at the Canberra Big Data Meetup 7 February 2019. We build a Kafka "postal service" to explain the main Kafka concepts, and explain how consumers receive different messages depending on whether there's a key or not.
Event-Driven Microservices architecture has gained a lot of attention recently. The trend in the industry is to move away from Monolithic applications to Microservices to innovate faster. While Microservices have their benefits, implementing them is hard. This talk focuses on the challenges faced and how to solve them.
It covers topics like using Domain Driven Design to break functionality into small parts. Various communication patterns among Microservices are also discussed.
One major drawback is the problem of distributed data management, as each Microservice has its own database. Event-Driven Architecture enables a way to make microservices work together and the talks show how to use architectural patterns like Event Sourcing & CQRS to implement them.
Another implementation challenge is to manage transactions that update entities owned by multiple services in an eventually consistent fashion. This challenge is solved using sagas, which can be thought of as Long running transactions that use compensating actions to handle failures.
The objective of the talk is to show how to implement highly distributed Event Driven Microservices architecture that are scalable and easy to maintain.
New Features in Confluent Platform 6.0 / Apache Kafka 2.6Kai Wähner
New Features in Confluent Platform 6.0 / Apache Kafka 2.6, including REST Proxy and API, Tiered Storage for AWS S3 and GCP GCS, Cluster Linking (On-Premise, Edge, Hybrid, Multi-Cloud), Self-Balancing Clusters), ksqlDB.
Enabling Microservices Frameworks to Solve Business ProblemsKen Owens
Opening keynote at Mesoscon 2015 with announcements on creating an ecosystem for developing solutions to business problems leveraging Mesos, Mantl.io, Mesosphere Infinity, ZoomData, and Project Calico to create Fog nodes for IoE use cases.
Au delà des brokers, un tour de l’environnement Kafka | Florent Ramièreconfluent
During the Confluent Streaming event in Paris, Florent Ramière, Technical Account Manager at Confluent, goes beyond brokers, introducing a whole new ecosystem with Kafka Streams, KSQL, Kafka Connect, Rest proxy, Schema Registry, MirrorMaker, etc.
Building realtime data applications that can seamlessly run and integrate data across On Prem, and multiple public cloud vendors. How Hybrid Cloud can help tackle regulatory requirements for Data Sovereignty, Stressed Exit, and operational resilience.
Event Streaming Architectures with Confluent and ScyllaDBScyllaDB
Jeff Bean will lead a discussion of event-driven architectures, Apache Kafka, Kafka Connect, KSQL and Confluent Cloud. Then we'll talk about some uses of Confluent and Scylla together, including a co-deployment with Lookout, ScyllaDB and Confluent in the IoT space, and the upcoming native connector.
This talk provides an architecture overview of data-centric microservices illustrated with an example application. The following Microservices concepts are illustrated - domain driven design, event-driven services, Saga transactions, Application tracing and Health monitoring with different microservices using a variety of data types supported in the database - business data, documents, spatial, graph, and events. A running example of a mobile food delivery application (called GrubDash) is used, with a hands-on-lab that is available for attendees to work through on the Oracle Cloud after these sessions. The rest of the talks will build upon this Microservices architecture framework.
Architecture patterns for distributed, hybrid, edge and global Apache Kafka d...Kai Wähner
Architecture patterns for distributed, hybrid, edge and global Apache Kafka deployments
Multi-cluster and cross-data center deployments of Apache Kafka have become the norm rather than an exception. This session gives an overview of several scenarios that may require multi-cluster solutions and discusses real-world examples with their specific requirements and trade-offs, including disaster recovery, aggregation for analytics, cloud migration, mission-critical stretched deployments and global Kafka.
Key takeaways:
In many scenarios, one Kafka cluster is not enough. Understand different architectures and alternatives for multi-cluster deployments.
Zero data loss and high availability are two key requirements. Understand how to realize this, including trade-offs.
Learn about features and limitations of Kafka for multi cluster deployments
Global Kafka and mission-critical multi-cluster deployments with zero data loss and high availability became the normal, not an exception.
Kafka Connect & Streams - the ecosystem around KafkaGuido Schmutz
After a quick overview and introduction of Apache Kafka, this session cover two components which extend the core of Apache Kafka: Kafka Connect and Kafka Streams/KSQL.
Kafka Connects role is to access data from the out-side-world and make it available inside Kafka by publishing it into a Kafka topic. On the other hand, Kafka Connect is also responsible to transport information from inside Kafka to the outside world, which could be a database or a file system. There are many existing connectors for different source and target systems available out-of-the-box, either provided by the community or by Confluent or other vendors. You simply configure these connectors and off you go.
Kafka Streams is a light-weight component which extends Kafka with stream processing functionality. By that, Kafka can now not only reliably and scalable transport events and messages through the Kafka broker but also analyse and process these event in real-time. Interestingly Kafka Streams does not provide its own cluster infrastructure and it is also not meant to run on a Kafka cluster. The idea is to run Kafka Streams where it makes sense, which can be inside a “normal” Java application, inside a Web container or on a more modern containerized (cloud) infrastructure, such as Mesos, Kubernetes or Docker. Kafka Streams has a lot of interesting features, such as reliable state handling, queryable state and much more. KSQL is a streaming engine for Apache Kafka, providing a simple and completely interactive SQL interface for processing data in Kafka.
Bee is an engine to build, deploy and manage microservices from composing event and data message handling. It supports smart scalabilities and distributed computing patterns, like mesh networks.
Set your Data in Motion with Confluent & Apache Kafka Tech Talk Series LMEconfluent
Confluent Platform is supporting London Metal Exchange’s Kafka Centre of Excellence across a number of projects with the main objective to provide a reliable, resilient, scalable and overall efficient Kafka as a Service model to the teams across the entire London Metal Exchange estate.
Modern Cloud-Native Streaming Platforms: Event Streaming Microservices with A...confluent
Microservices, events, containers, and orchestrators are dominating our vernacular today. As operations teams adapt to support these technologies in production, cloud-native platforms like Pivotal Cloud Foundry and Kubernetes have quickly risen to serve as force multipliers of automation, productivity and value.
Apache Kafka® is providing developers a critically important component as they build and modernize applications to cloud-native architecture.
This talk will explore:
• Why cloud-native platforms and why run Apache Kafka on Kubernetes?
• What kind of workloads are best suited for this combination?
• Tips to determine the path forward for legacy monoliths in your application portfolio
• Demo: Running Apache Kafka as a Streaming Platform on Kubernetes
Developing Realtime Data Pipelines With Apache KafkaJoe Stein
Developing Realtime Data Pipelines With Apache Kafka. Apache Kafka is publish-subscribe messaging rethought as a distributed commit log. A single Kafka broker can handle hundreds of megabytes of reads and writes per second from thousands of clients. Kafka is designed to allow a single cluster to serve as the central data backbone for a large organization. It can be elastically and transparently expanded without downtime. Data streams are partitioned and spread over a cluster of machines to allow data streams larger than the capability of any single machine and to allow clusters of co-ordinated consumers. Messages are persisted on disk and replicated within the cluster to prevent data loss. Each broker can handle terabytes of messages without performance impact. Kafka has a modern cluster-centric design that offers strong durability and fault-tolerance guarantees.
AI Pilot Review: The World’s First Virtual Assistant Marketing SuiteGoogle
AI Pilot Review: The World’s First Virtual Assistant Marketing Suite
👉👉 Click Here To Get More Info 👇👇
https://sumonreview.com/ai-pilot-review/
AI Pilot Review: Key Features
✅Deploy AI expert bots in Any Niche With Just A Click
✅With one keyword, generate complete funnels, websites, landing pages, and more.
✅More than 85 AI features are included in the AI pilot.
✅No setup or configuration; use your voice (like Siri) to do whatever you want.
✅You Can Use AI Pilot To Create your version of AI Pilot And Charge People For It…
✅ZERO Manual Work With AI Pilot. Never write, Design, Or Code Again.
✅ZERO Limits On Features Or Usages
✅Use Our AI-powered Traffic To Get Hundreds Of Customers
✅No Complicated Setup: Get Up And Running In 2 Minutes
✅99.99% Up-Time Guaranteed
✅30 Days Money-Back Guarantee
✅ZERO Upfront Cost
See My Other Reviews Article:
(1) TubeTrivia AI Review: https://sumonreview.com/tubetrivia-ai-review
(2) SocioWave Review: https://sumonreview.com/sociowave-review
(3) AI Partner & Profit Review: https://sumonreview.com/ai-partner-profit-review
(4) AI Ebook Suite Review: https://sumonreview.com/ai-ebook-suite-review
Exploring Innovations in Data Repository Solutions - Insights from the U.S. G...Globus
The U.S. Geological Survey (USGS) has made substantial investments in meeting evolving scientific, technical, and policy driven demands on storing, managing, and delivering data. As these demands continue to grow in complexity and scale, the USGS must continue to explore innovative solutions to improve its management, curation, sharing, delivering, and preservation approaches for large-scale research data. Supporting these needs, the USGS has partnered with the University of Chicago-Globus to research and develop advanced repository components and workflows leveraging its current investment in Globus. The primary outcome of this partnership includes the development of a prototype enterprise repository, driven by USGS Data Release requirements, through exploration and implementation of the entire suite of the Globus platform offerings, including Globus Flow, Globus Auth, Globus Transfer, and Globus Search. This presentation will provide insights into this research partnership, introduce the unique requirements and challenges being addressed and provide relevant project progress.
May Marketo Masterclass, London MUG May 22 2024.pdfAdele Miller
Can't make Adobe Summit in Vegas? No sweat because the EMEA Marketo Engage Champions are coming to London to share their Summit sessions, insights and more!
This is a MUG with a twist you don't want to miss.
Understanding Globus Data Transfers with NetSageGlobus
NetSage is an open privacy-aware network measurement, analysis, and visualization service designed to help end-users visualize and reason about large data transfers. NetSage traditionally has used a combination of passive measurements, including SNMP and flow data, as well as active measurements, mainly perfSONAR, to provide longitudinal network performance data visualization. It has been deployed by dozens of networks world wide, and is supported domestically by the Engagement and Performance Operations Center (EPOC), NSF #2328479. We have recently expanded the NetSage data sources to include logs for Globus data transfers, following the same privacy-preserving approach as for Flow data. Using the logs for the Texas Advanced Computing Center (TACC) as an example, this talk will walk through several different example use cases that NetSage can answer, including: Who is using Globus to share data with my institution, and what kind of performance are they able to achieve? How many transfers has Globus supported for us? Which sites are we sharing the most data with, and how is that changing over time? How is my site using Globus to move data internally, and what kind of performance do we see for those transfers? What percentage of data transfers at my institution used Globus, and how did the overall data transfer performance compare to the Globus users?
Accelerate Enterprise Software Engineering with PlatformlessWSO2
Key takeaways:
Challenges of building platforms and the benefits of platformless.
Key principles of platformless, including API-first, cloud-native middleware, platform engineering, and developer experience.
How Choreo enables the platformless experience.
How key concepts like application architecture, domain-driven design, zero trust, and cell-based architecture are inherently a part of Choreo.
Demo of an end-to-end app built and deployed on Choreo.
OpenFOAM solver for Helmholtz equation, helmholtzFoam / helmholtzBubbleFoamtakuyayamamoto1800
In this slide, we show the simulation example and the way to compile this solver.
In this solver, the Helmholtz equation can be solved by helmholtzFoam. Also, the Helmholtz equation with uniformly dispersed bubbles can be simulated by helmholtzBubbleFoam.
Software Engineering, Software Consulting, Tech Lead.
Spring Boot, Spring Cloud, Spring Core, Spring JDBC, Spring Security,
Spring Transaction, Spring MVC,
Log4j, REST/SOAP WEB-SERVICES.
Developing Distributed High-performance Computing Capabilities of an Open Sci...Globus
COVID-19 had an unprecedented impact on scientific collaboration. The pandemic and its broad response from the scientific community has forged new relationships among public health practitioners, mathematical modelers, and scientific computing specialists, while revealing critical gaps in exploiting advanced computing systems to support urgent decision making. Informed by our team’s work in applying high-performance computing in support of public health decision makers during the COVID-19 pandemic, we present how Globus technologies are enabling the development of an open science platform for robust epidemic analysis, with the goal of collaborative, secure, distributed, on-demand, and fast time-to-solution analyses to support public health.
Enterprise Resource Planning System includes various modules that reduce any business's workload. Additionally, it organizes the workflows, which drives towards enhancing productivity. Here are a detailed explanation of the ERP modules. Going through the points will help you understand how the software is changing the work dynamics.
To know more details here: https://blogs.nyggs.com/nyggs/enterprise-resource-planning-erp-system-modules/
In software engineering, the right architecture is essential for robust, scalable platforms. Wix has undergone a pivotal shift from event sourcing to a CRUD-based model for its microservices. This talk will chart the course of this pivotal journey.
Event sourcing, which records state changes as immutable events, provided robust auditing and "time travel" debugging for Wix Stores' microservices. Despite its benefits, the complexity it introduced in state management slowed development. Wix responded by adopting a simpler, unified CRUD model. This talk will explore the challenges of event sourcing and the advantages of Wix's new "CRUD on steroids" approach, which streamlines API integration and domain event management while preserving data integrity and system resilience.
Participants will gain valuable insights into Wix's strategies for ensuring atomicity in database updates and event production, as well as caching, materialization, and performance optimization techniques within a distributed system.
Join us to discover how Wix has mastered the art of balancing simplicity and extensibility, and learn how the re-adoption of the modest CRUD has turbocharged their development velocity, resilience, and scalability in a high-growth environment.
Into the Box Keynote Day 2: Unveiling amazing updates and announcements for modern CFML developers! Get ready for exciting releases and updates on Ortus tools and products. Stay tuned for cutting-edge innovations designed to boost your productivity.
TROUBLESHOOTING 9 TYPES OF OUTOFMEMORYERRORTier1 app
Even though at surface level ‘java.lang.OutOfMemoryError’ appears as one single error; underlyingly there are 9 types of OutOfMemoryError. Each type of OutOfMemoryError has different causes, diagnosis approaches and solutions. This session equips you with the knowledge, tools, and techniques needed to troubleshoot and conquer OutOfMemoryError in all its forms, ensuring smoother, more efficient Java applications.
First Steps with Globus Compute Multi-User EndpointsGlobus
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1. Implementing Domain Events with
Kafka
Devtalks
How DDD and Kafka help in integrating microservices
Bucharest, 12th of June 2020
,
2. Agenda
2
Challenges in integrating microservices
What is a Domain Event?
Implementation patterns for Domain Events
Kafka Delivery Guarantees & Configurations
3. Microservices benefits
• Agility in development
• Better understanding of the architecture
• Resilience in production
• Independent scalability of microservices
• Independent deployment of microservices
• Fault tolerance (failures in a microservice can be isolated from other microservices)
How can these benefits could be achieved?
✓ Ensuring microservice autonomy
✓ Defining explicit contracts
3
4. Synchronous Integration between Microservices
▪ Temporal Coupling
▪ Behavioral Coupling*
The services are not autonomous
Complex and unreliable
compensation logic
Circuit breaker, timeouts, retries
etc. are of limited use
*Reference: http://iansrobinson.com/2009/04/27/temporal-and-behavioural-coupling/
4
6. Microservices Design Rules
1 microservice = 1 bounded context = 1 deployment unit
1 operation = 1 transaction = 1 aggregate (modify in one transaction only one
aggregate instance)
Prefer asynchronous communication between microservices over synchronous
request/reply integration ➔ integration based on domain events
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7. Domain Events
• An event captures the changes in the state of a single aggregate
• Events are immutable
• They get published by the aggregates and contain the associated aggregate root id
• Each event should have a unique id that could be used for de-duplication
7
8. Integration Rules between Bounded Contexts
▪ Via a publish/subscriber messaging infrastructure
▪ The bounded contexts interested in the data maintained by other bounded
contexts subscribe to the relevant events
▪ Integration via events is applicable also between different aggregate instances from
the same bounded context
8
➔ Transactional Consistency within aggregate boundaries
➔ Eventual Consistency between different bounded contexts and between different
aggregate instances
9. µ Service µ Service µ Service µ Service
Event Bus
REST/SOAP REST/SOAP REST/SOAP REST/SOAP
UI/ API Gateway
Data DataDataData
Microservices Architecture based on DDD
Decomposing application into loosely
coupled services:
- Explicit contract/ interface
- Boundary alignment with
business capabilities
- Asynchronous communication
between microservices
- Microservices have their own
storage (they are the
authoritative source of data for
their domain)
1
2
3
4
1
2
3
4
9
10. Main use cases for domain events
10
Notifications other
bounded contexts need
to react to
Data replication over
events between
bounded contexts
CQRS (in the same
bounded context)
11. Messaging requirements in view of DDD
▪ Support for publish/subscriber pattern
▪ Support for competing consumers pattern
▪ Persistence and Durability
▪ At-least-once message delivery
▪ Partial message ordering (per aggregate)
11
12. Kafka Logical Architecture
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Replication + Fault Tolerance +
Partitioning + Elastic Scaling
Single-Leader System (all
producers/consumers
publish/fetch data to/from
leaders )
CP in terms of CAP theorem for
both write and read operations
Confluent Platform 5.4 introduces
Follower Fetching, Observers, and
Replica Placement ➔ Fetching
data from asynchronous replicas
➔ AP for read operations
Availability ZoneAvailability Zone
Broker B1 – Leader for partition 1,
follower for partitions 3 and 4
Broker B2 – Leader for 2, follower
for 1 and 4
Topic-partition1
Broker B3 – Leader for 3, follower for
1 and 2
Broker B4– Leader for 4, follower
for 2 and 3
Topic-partition2
Topic-partition3
Topic-partition4
Topic-partition1
Topic-partition1
fetch
fetch
Topic-partition2 Topic-partition2
fetchfetch
Topic-partition3
Topic-partition3
fetch
fetch
Topic-partition4 Topic-partition4
fetchfetch
Producer/
Consumer
Initial ISR: {B1, B2, B3}
Initial ISR: {B2, B3, B4}
Initial ISR: {B1, B3, B4}
Initial ISR: {B1, B2, B4}
13. Kafka Broker Configurations
min.insync.replicas: should be set to a value greater than 1
replica.lag.time.max.ms
unclean.leader.election.enable: should be false
broker.rack: should reflect data centers, regions or availability zones where
brokers are placed
replica.selector.class (since Confluent 5.4): used by the broker to find the
preferred read replica. By default it returns partition leaders.
13
* Reference: https://docs.confluent.io/current/installation/configuration/broker-configs.html
14. Kafka Offsets
14
0 1 2 cp up hw ...lc end
Begin
Offset
Last
Commited
Offset of a
Consumer
Consumer s
Current Position
Undereplicated
Partition
from a follower
non-ISR broker
High Watermark
(last index
replicated to all ISR)
Messages published with
acks 0 or 1 and not yet
replicated
Consumer s batch of
messages
15. How to choose the number of topics/partitions
▪ Number of partitions = producer throughput/ consumer throughput
▪ limit the number of partitions per broker to 100 x b x r,
Where:
b is the number of brokers in a Kafka cluster
r is the replication factor
Why?
“Kafka broker uses only a single thread to replicate data from another broker, for all
partitions that share replicas between the two brokers. (…)replicating 1000 partitions from
one broker to another can add about 20 ms latency, which implies that the end-to-end
latency is at least 20 ms.”*
15
* Reference: https://www.confluent.io/blog/how-choose-number-topics-partitions-kafka-cluster/
16. How Kafka addresses DDD messaging requirements
Support for publish/subscribers pattern ➔ topics
Support for competing consumers pattern ➔ consumer groups
Persistence and Durability ➔ replication factors, ISR, retention policies
At-least-once message delivery ➔ acks (on producer-side), default behavior of
consumers (when auto commit is enabled)
Partial message ordering (per aggregate) ➔ partitions
16
17. Application Requirements in View of DDD
▪ Domain events are self-contained messages (events capture the state changes
in domain entities and provide decision support for subscribers)
▪ Domain events are part of a microservice's public API/contract
▪ One topic per aggregate type
▪ Partitioning by aggregate/entity ID
▪ Transactional semantics when publishing events
▪ Events deduplication on subscriber-side for achieving exactly-once delivery
semantics
17
18. Aggregate Processing
18
Client Application Service Aggregate Root Entity Repository KafkaProducer
remote operation
Aggregate root method
Entity method
Publish event: event type, root id, event id,
aggregate root state change, entity state change
Find aggregate
State change
State change
Save aggregate
Bounded Context
22. Kafka Consumer
try {
while (true) {
ConsumerRecords<String, String> records = consumer.poll(MAX_VALUE);
for (ConsumerRecord<String, String> record : records) {
System.out.println(record.offset() + “: ” + record.value());
}
try {
//saving new offsets if enable.auto.commit = false
} catch (CommitFailedException e) {
// application specific failure handling
}
}
} catch (WakeupException e) {
// ignore for shutdown
} finally {
consumer.close();
}
22
Up to
max.poll.records
max.poll.interval.ms >
max.poll.records
*
record processing time
Commit if enable.auto.commit = true and
the interval between current poll and last
commit >=
auto.commit.interval.ms
23. Kafka Consumer Configurations
▪ group.id
▪ heartbeat.interval.ms & session.timeout.ms
▪ enable.auto.commit & auto.commit.interval.ms
▪ isolation.level
▪ max.poll.interval.ms
▪ max.poll.records
▪ client.rack (since Confluent 5.4; if the partition is rack aware and the replica selector is set, pick a “preferred
read replica”)
23
24. Exactly-once Stateful Processing
24
Kafka Broker(s) Kafka Consumer Aggregate Root Repository Offset Manager
Poll for events
Aggregate root method
Save aggregate
events
local transaction
Bounded Context
Offsets Table
Aggregate State
Table
Select offsets
State change
Update Offset
Same Database
loop
25. Avro Consumers & Producers
25
Schema
Registry
Kafka
Producer Consumer
Send Avro content +
Schema id
Query schema id
by schema content
Get writer s
schema by id
Read avro
content + schema Id
Apply schema
evolution
26. Avro
• Kafka records can have a Key and a Value and both can have a schema.
• There is a compatibility level (BACKWARDS, FORWARDS, FULL, NONE) setting for the Schema
Registry and an individual subject. Versions are also managed per subject
• Backward compatibility = consumers coded with a newer schema can read messages
written with older schema.
• Forward compatibility = consumers coded with a older schema can read messages written
with a newer schema.
• Full compatibility = a new schema version is both backward and forward compatible.
• None
• Avro schema evolution is an automatic transformation of messages from the schema used by
producers to write into the Kafka log to schema used by consumer to read them. The
transformation occurs at consuming time in Avro Deserializer.
26
Change Type Order of releasing into production
Backward Compatible Consumers, Producers
Forward Compatible Producers, Consumers (after they finish reading old messages)
Fully Compatible Order doesn’t matter
None Coordinated
27. Subject Name Strategy
• A DDD aggregate could publish multiple event types, each capturing a distinct business intent
• For maintaining the correct event order all the events will be published in the same topic (using
aggregate id as message key)
• Multiple event types require multiple schemas (one for each event type that could be published
in the same topic)
• Producer-side configs:
• schema registry url
• key.subject.name.strategy (which defines how to construct the subject name for message
keys) = TopicNameStrategy, RecordNameStrategy or TopicRecordNameStrategy
• value.subject.name.strategy (how to construct the subject name for message values) =
TopicNameStrategy, RecordNameStrategy or TopicRecordNameStrategy
• Consumer-side configs:
• specific.avro.reader = true (or otherwise you get Avro GenericRecord)
27
28. Conclusions
Domain Driven Design not only helps in decomposing a system into
microservices aligned with business capabilities, but also offers essential
lessons on:
✓ how to achieve resilience
✓ how to achieve scalability
by decoupling microservices using domain events
28