This document discusses best practices for running Apache Kafka on Docker containers. It describes how to design Kafka deployments using Docker to provide portability, elasticity, and multi-tenancy. Key considerations include allocating appropriate resources to different roles like brokers and Zookeepers, enabling auto-configuration, and providing security and network isolation. The document outlines an implementation using Dockerfiles, configurations, and orchestration to deploy multi-node Kafka clusters across multiple hosts.
Apache Kafka is an open-source message broker project developed by the Apache Software Foundation written in Scala. The project aims to provide a unified, high-throughput, low-latency platform for handling real-time data feeds.
In this session, we will show how to simplify the deployment, management, and operations of Kubernetes using Azure Container Service (AKS). We will demonstrate how to use Brigade - a framework for scripting together multiple tasks and executing them inside of containers and Kashti - an open source reporting dashboard web interface to easily manage and visualize their Brigade events and projects through a web browser. Additionally, we will provide comparisons of the wide variety of tools in the Kubernetes ecosystem for CI/CD, observability, storage and networking.
Disaster Recovery Options Running Apache Kafka in Kubernetes with Rema Subra...HostedbyConfluent
Active-Active, Active-Passive, and stretch clusters are hallmark patterns that have been the gold standard in Apache Kafka® disaster recovery architectures for years. Moving to Kubernetes requires unpacking these patterns and choosing a configuration that allows you to meet the same RTO and RPO requirements.
In this talk, we will cover how Active-Active/Active-Passive modes for disaster recovery have worked in the past and how the architecture evolves with deploying Apache Kafka on Kubernetes. We'll also look at how stretch clusters sitting on this architecture give a disaster recovery solution that's built-in!
Armed with this information, you will be able to architect your new Apache Kafka Kubernetes deployment (or retool your existing one) to achieve the resilience you require.
Apache kafka 모니터링을 위한 Metrics 이해 및 최적화 방안SANG WON PARK
Apache Kafak의 빅데이터 아키텍처에서 역할이 점차 커지고, 중요한 비중을 차지하게 되면서, 성능에 대한 고민도 늘어나고 있다.
다양한 프로젝트를 진행하면서 Apache Kafka를 모니터링 하기 위해 필요한 Metrics들을 이해하고, 이를 최적화 하기 위한 Configruation 설정을 정리해 보았다.
[Apache kafka 모니터링을 위한 Metrics 이해 및 최적화 방안]
Apache Kafka 성능 모니터링에 필요한 metrics에 대해 이해하고, 4가지 관점(처리량, 지연, Durability, 가용성)에서 성능을 최적화 하는 방안을 정리함. Kafka를 구성하는 3개 모듈(Producer, Broker, Consumer)별로 성능 최적화를 위한 …
[Apache Kafka 모니터링을 위한 Metrics 이해]
Apache Kafka의 상태를 모니터링 하기 위해서는 4개(System(OS), Producer, Broker, Consumer)에서 발생하는 metrics들을 살펴봐야 한다.
이번 글에서는 JVM에서 제공하는 JMX metrics를 중심으로 producer/broker/consumer의 지표를 정리하였다.
모든 지표를 정리하진 않았고, 내 관점에서 유의미한 지표들을 중심으로 이해한 내용임
[Apache Kafka 성능 Configuration 최적화]
성능목표를 4개로 구분(Throughtput, Latency, Durability, Avalibility)하고, 각 목표에 따라 어떤 Kafka configuration의 조정을 어떻게 해야하는지 정리하였다.
튜닝한 파라미터를 적용한 후, 성능테스트를 수행하면서 추출된 Metrics를 모니터링하여 현재 업무에 최적화 되도록 최적화를 수행하는 것이 필요하다.
Apache Kafka is an open-source message broker project developed by the Apache Software Foundation written in Scala. The project aims to provide a unified, high-throughput, low-latency platform for handling real-time data feeds.
In this session, we will show how to simplify the deployment, management, and operations of Kubernetes using Azure Container Service (AKS). We will demonstrate how to use Brigade - a framework for scripting together multiple tasks and executing them inside of containers and Kashti - an open source reporting dashboard web interface to easily manage and visualize their Brigade events and projects through a web browser. Additionally, we will provide comparisons of the wide variety of tools in the Kubernetes ecosystem for CI/CD, observability, storage and networking.
Disaster Recovery Options Running Apache Kafka in Kubernetes with Rema Subra...HostedbyConfluent
Active-Active, Active-Passive, and stretch clusters are hallmark patterns that have been the gold standard in Apache Kafka® disaster recovery architectures for years. Moving to Kubernetes requires unpacking these patterns and choosing a configuration that allows you to meet the same RTO and RPO requirements.
In this talk, we will cover how Active-Active/Active-Passive modes for disaster recovery have worked in the past and how the architecture evolves with deploying Apache Kafka on Kubernetes. We'll also look at how stretch clusters sitting on this architecture give a disaster recovery solution that's built-in!
Armed with this information, you will be able to architect your new Apache Kafka Kubernetes deployment (or retool your existing one) to achieve the resilience you require.
Apache kafka 모니터링을 위한 Metrics 이해 및 최적화 방안SANG WON PARK
Apache Kafak의 빅데이터 아키텍처에서 역할이 점차 커지고, 중요한 비중을 차지하게 되면서, 성능에 대한 고민도 늘어나고 있다.
다양한 프로젝트를 진행하면서 Apache Kafka를 모니터링 하기 위해 필요한 Metrics들을 이해하고, 이를 최적화 하기 위한 Configruation 설정을 정리해 보았다.
[Apache kafka 모니터링을 위한 Metrics 이해 및 최적화 방안]
Apache Kafka 성능 모니터링에 필요한 metrics에 대해 이해하고, 4가지 관점(처리량, 지연, Durability, 가용성)에서 성능을 최적화 하는 방안을 정리함. Kafka를 구성하는 3개 모듈(Producer, Broker, Consumer)별로 성능 최적화를 위한 …
[Apache Kafka 모니터링을 위한 Metrics 이해]
Apache Kafka의 상태를 모니터링 하기 위해서는 4개(System(OS), Producer, Broker, Consumer)에서 발생하는 metrics들을 살펴봐야 한다.
이번 글에서는 JVM에서 제공하는 JMX metrics를 중심으로 producer/broker/consumer의 지표를 정리하였다.
모든 지표를 정리하진 않았고, 내 관점에서 유의미한 지표들을 중심으로 이해한 내용임
[Apache Kafka 성능 Configuration 최적화]
성능목표를 4개로 구분(Throughtput, Latency, Durability, Avalibility)하고, 각 목표에 따라 어떤 Kafka configuration의 조정을 어떻게 해야하는지 정리하였다.
튜닝한 파라미터를 적용한 후, 성능테스트를 수행하면서 추출된 Metrics를 모니터링하여 현재 업무에 최적화 되도록 최적화를 수행하는 것이 필요하다.
데브시스터즈의 Cookie Run: OvenBreak 에 적용된 Kubernetes 기반 다중 개발 서버 환경 구축 시스템에 대한 발표입니다.
Container orchestration 기반 개발 환경 구축 시스템의 필요성과, 왜 Kubernetes를 선택했는지, Kubernetes의 개념과 유용한 기능들을 다룹니다. 아울러 구축한 시스템에 대한 데모와, 작업했던 항목들에 대해 리뷰합니다.
*NDC17 발표에서는 데모 동영상을 사용했으나, 슬라이드 캡쳐로 대신합니다.
ksqlDB is a stream processing SQL engine, which allows stream processing on top of Apache Kafka. ksqlDB is based on Kafka Stream and provides capabilities for consuming messages from Kafka, analysing these messages in near-realtime with a SQL like language and produce results again to a Kafka topic. By that, no single line of Java code has to be written and you can reuse your SQL knowhow. This lowers the bar for starting with stream processing significantly.
ksqlDB offers powerful capabilities of stream processing, such as joins, aggregations, time windows and support for event time. In this talk I will present how KSQL integrates with the Kafka ecosystem and demonstrate how easy it is to implement a solution using ksqlDB for most part. This will be done in a live demo on a fictitious IoT sample.
Kafka's basic terminologies, its architecture, its protocol and how it works.
Kafka at scale, its caveats, guarantees and use cases offered by it.
How we use it @ZaprMediaLabs.
Streaming all over the world Real life use cases with Kafka Streamsconfluent
Streaming all over the world Real life use cases with Kafka Streams, Dr. Benedikt Linse, Senior Solutions Architect, Confluent
https://www.meetup.com/Apache-Kafka-Germany-Munich/events/281819704/
An Introduction to Confluent Cloud: Apache Kafka as a Serviceconfluent
Business breakout during Confluent’s streaming event in Munich, presented by Hans Jespersen, VP WW Systems Engineering at Confluent. This three-day hands-on course focused on how to build, manage, and monitor clusters using industry best-practices developed by the world’s foremost Apache Kafka™ experts. The sessions focused on how Kafka and the Confluent Platform work, how their main subsystems interact, and how to set up, manage, monitor, and tune your cluster.
KSQL is an open source streaming SQL engine for Apache Kafka. Come hear how KSQL makes it easy to get started with a wide-range of stream processing applications such as real-time ETL, sessionization, monitoring and alerting, or fraud detection. We'll cover both how to get started with KSQL and some under-the-hood details of how it all works.
BYOP: Custom Processor Development with Apache NiFiDataWorks Summit
Apache NiFi, a robust, scalable, and secure tool for data flow management, ships with over 212 processors to ingest, route, manipulate, and exfil data from a variety of sources and consumers. But many users turn to NiFi to meet unusual requirements — from proprietary protocol parsing, to running inside connected cars, to offloading massive hardware metrics from oil rigs in the most remote environments. Rather than posting a community request for custom development or offloading unusual demands to unnecessary external systems, there’s an answer in NiFi. Learn how NiFi allows you to quickly prototype custom processors in the scripting language of your choice against live production data without affecting your existing flows. Easily translate prototypes to full-fledged processors to optimize performance and leverage the full provenance reporting infrastructure. Discover how the framework provides conventions to streamline your development and minimize common boilerplate code, and the robust testing framework to make testing easy, and dare we say, fun.
Expected prior knowledge / intended audience: developers and data flow managers should have passing knowledge of Apache NiFi as a platform for routing, transforming, and delivering data through systems (a brief overview will be provided). The intended audience will have experience with programming in Groovy, Ruby, Jython, ECMAScript/Javascript, or Lua.
Takeaways: Attendees will gain an understanding in writing custom processors for Apache NiFi, including the component lifecycle, unit and integration testing, quick prototyping using a scripting language of their choice, and the artifact publishing and deployment process.
클라우드 네이티브로의 전환이 확산되면서 애플리케이션을 상호 독립적인 최소 구성 요소로 쪼개는 마이크로서비스(microservices) 아키텍쳐가 각광받고 있는데요.
MSA는 애플리케이션의 확장이 쉽고 새로운 기능의 출시 기간을 단축시킬 수 있다는 장점이 있지만,
반면에 애플리케이션이 커지고 동일한 서비스의 여러 인스턴스가 동시에 실행되면 MSA간 통신이 복잡해 진다는 단점이 있습니다.
서비스 메쉬(Service Mesh)는 이러한 MSA의 트래픽 문제를 보완하기 위해 탄생한 기술로,
서비스 간의 네트워크 트래픽 관리에 초점을 맞춘 네트워킹 모델입니다.
서로 다른 애플리케이션이 얼마나 원활하게 상호작용하는지를 기록함으로써 커뮤니케이션을 최적화하고 애플리케이션 확장에 따른 다운 타임을 방지할 수 있습니다.
서비스 메쉬의 탄생 배경과 기능, 그리고 현재 오픈소스로 배포되어 있는 서비스 메쉬 솔루션에 대해 소개합니다.
Step1. Cloud Native Trail Map
Step2. Service Proxy, Discover, & Mesh
Step3. Service Mesh 솔루션
Step4. Service Mesh 구현화면 - Istio / linkerd
Step5. Multi-cluster (linkerd)
Performance Tuning RocksDB for Kafka Streams' State Stores (Dhruba Borthakur,...confluent
RocksDB is the default state store for Kafka Streams. In this talk, we will discuss how to improve single node performance of the state store by tuning RocksDB and how to efficiently identify issues in the setup. We start with a short description of the RocksDB architecture. We discuss how Kafka Streams restores the state stores from Kafka by leveraging RocksDB features for bulk loading of data. We give examples of hand-tuning the RocksDB state stores based on Kafka Streams metrics and RocksDB’s metrics. At the end, we dive into a few RocksDB command line utilities that allow you to debug your setup and dump data from a state store. We illustrate the usage of the utilities with a few real-life use cases. The key takeaway from the session is the ability to understand the internal details of the default state store in Kafka Streams so that engineers can fine-tune their performance for different varieties of workloads and operate the state stores in a more robust manner.
[APIdays INTERFACE 2021] The Evolution of API Security for Client-side Applic...WSO2
Client-side applications are becoming an increasingly popular technology to build applications owing to the advanced user experience that they provide consumers. Authentication and API authorization for these applications are also becoming equally popular topics that many developers have a hard time getting their heads around.
Check these slides, where Johann Nallathamby, Head of Solutions Architecture for IAM at WSO2, will attempt to demystify some complexities and misconceptions surrounding this topic and help you better understand the most important features to consider when choosing an authentication and API authorization solution for client-side applications.
These slides will review:
- The broader classification of client-side applications and their legacy and more recent authentication and API authorization patterns
- Sender-constrained token patterns
- Solution patterns being employed to improve user experience in client-side applications
Best Practices for Running Kafka on Docker ContainersBlueData, Inc.
Docker containers provide an ideal foundation for running Kafka-as-a-Service on-premises or in the public cloud. However, using Docker containers in production environments for Big Data workloads using Kafka poses some challenges – including container management, scheduling, network configuration and security, and performance.
In this session at Kafka Summit in August 2017, Nanda Vijyaydev of BlueData shared lessons learned from implementing Kafka-as-a-Service with Docker containers.
https://kafka-summit.org/sessions/kafka-service-docker-containers
데브시스터즈의 Cookie Run: OvenBreak 에 적용된 Kubernetes 기반 다중 개발 서버 환경 구축 시스템에 대한 발표입니다.
Container orchestration 기반 개발 환경 구축 시스템의 필요성과, 왜 Kubernetes를 선택했는지, Kubernetes의 개념과 유용한 기능들을 다룹니다. 아울러 구축한 시스템에 대한 데모와, 작업했던 항목들에 대해 리뷰합니다.
*NDC17 발표에서는 데모 동영상을 사용했으나, 슬라이드 캡쳐로 대신합니다.
ksqlDB is a stream processing SQL engine, which allows stream processing on top of Apache Kafka. ksqlDB is based on Kafka Stream and provides capabilities for consuming messages from Kafka, analysing these messages in near-realtime with a SQL like language and produce results again to a Kafka topic. By that, no single line of Java code has to be written and you can reuse your SQL knowhow. This lowers the bar for starting with stream processing significantly.
ksqlDB offers powerful capabilities of stream processing, such as joins, aggregations, time windows and support for event time. In this talk I will present how KSQL integrates with the Kafka ecosystem and demonstrate how easy it is to implement a solution using ksqlDB for most part. This will be done in a live demo on a fictitious IoT sample.
Kafka's basic terminologies, its architecture, its protocol and how it works.
Kafka at scale, its caveats, guarantees and use cases offered by it.
How we use it @ZaprMediaLabs.
Streaming all over the world Real life use cases with Kafka Streamsconfluent
Streaming all over the world Real life use cases with Kafka Streams, Dr. Benedikt Linse, Senior Solutions Architect, Confluent
https://www.meetup.com/Apache-Kafka-Germany-Munich/events/281819704/
An Introduction to Confluent Cloud: Apache Kafka as a Serviceconfluent
Business breakout during Confluent’s streaming event in Munich, presented by Hans Jespersen, VP WW Systems Engineering at Confluent. This three-day hands-on course focused on how to build, manage, and monitor clusters using industry best-practices developed by the world’s foremost Apache Kafka™ experts. The sessions focused on how Kafka and the Confluent Platform work, how their main subsystems interact, and how to set up, manage, monitor, and tune your cluster.
KSQL is an open source streaming SQL engine for Apache Kafka. Come hear how KSQL makes it easy to get started with a wide-range of stream processing applications such as real-time ETL, sessionization, monitoring and alerting, or fraud detection. We'll cover both how to get started with KSQL and some under-the-hood details of how it all works.
BYOP: Custom Processor Development with Apache NiFiDataWorks Summit
Apache NiFi, a robust, scalable, and secure tool for data flow management, ships with over 212 processors to ingest, route, manipulate, and exfil data from a variety of sources and consumers. But many users turn to NiFi to meet unusual requirements — from proprietary protocol parsing, to running inside connected cars, to offloading massive hardware metrics from oil rigs in the most remote environments. Rather than posting a community request for custom development or offloading unusual demands to unnecessary external systems, there’s an answer in NiFi. Learn how NiFi allows you to quickly prototype custom processors in the scripting language of your choice against live production data without affecting your existing flows. Easily translate prototypes to full-fledged processors to optimize performance and leverage the full provenance reporting infrastructure. Discover how the framework provides conventions to streamline your development and minimize common boilerplate code, and the robust testing framework to make testing easy, and dare we say, fun.
Expected prior knowledge / intended audience: developers and data flow managers should have passing knowledge of Apache NiFi as a platform for routing, transforming, and delivering data through systems (a brief overview will be provided). The intended audience will have experience with programming in Groovy, Ruby, Jython, ECMAScript/Javascript, or Lua.
Takeaways: Attendees will gain an understanding in writing custom processors for Apache NiFi, including the component lifecycle, unit and integration testing, quick prototyping using a scripting language of their choice, and the artifact publishing and deployment process.
클라우드 네이티브로의 전환이 확산되면서 애플리케이션을 상호 독립적인 최소 구성 요소로 쪼개는 마이크로서비스(microservices) 아키텍쳐가 각광받고 있는데요.
MSA는 애플리케이션의 확장이 쉽고 새로운 기능의 출시 기간을 단축시킬 수 있다는 장점이 있지만,
반면에 애플리케이션이 커지고 동일한 서비스의 여러 인스턴스가 동시에 실행되면 MSA간 통신이 복잡해 진다는 단점이 있습니다.
서비스 메쉬(Service Mesh)는 이러한 MSA의 트래픽 문제를 보완하기 위해 탄생한 기술로,
서비스 간의 네트워크 트래픽 관리에 초점을 맞춘 네트워킹 모델입니다.
서로 다른 애플리케이션이 얼마나 원활하게 상호작용하는지를 기록함으로써 커뮤니케이션을 최적화하고 애플리케이션 확장에 따른 다운 타임을 방지할 수 있습니다.
서비스 메쉬의 탄생 배경과 기능, 그리고 현재 오픈소스로 배포되어 있는 서비스 메쉬 솔루션에 대해 소개합니다.
Step1. Cloud Native Trail Map
Step2. Service Proxy, Discover, & Mesh
Step3. Service Mesh 솔루션
Step4. Service Mesh 구현화면 - Istio / linkerd
Step5. Multi-cluster (linkerd)
Performance Tuning RocksDB for Kafka Streams' State Stores (Dhruba Borthakur,...confluent
RocksDB is the default state store for Kafka Streams. In this talk, we will discuss how to improve single node performance of the state store by tuning RocksDB and how to efficiently identify issues in the setup. We start with a short description of the RocksDB architecture. We discuss how Kafka Streams restores the state stores from Kafka by leveraging RocksDB features for bulk loading of data. We give examples of hand-tuning the RocksDB state stores based on Kafka Streams metrics and RocksDB’s metrics. At the end, we dive into a few RocksDB command line utilities that allow you to debug your setup and dump data from a state store. We illustrate the usage of the utilities with a few real-life use cases. The key takeaway from the session is the ability to understand the internal details of the default state store in Kafka Streams so that engineers can fine-tune their performance for different varieties of workloads and operate the state stores in a more robust manner.
[APIdays INTERFACE 2021] The Evolution of API Security for Client-side Applic...WSO2
Client-side applications are becoming an increasingly popular technology to build applications owing to the advanced user experience that they provide consumers. Authentication and API authorization for these applications are also becoming equally popular topics that many developers have a hard time getting their heads around.
Check these slides, where Johann Nallathamby, Head of Solutions Architecture for IAM at WSO2, will attempt to demystify some complexities and misconceptions surrounding this topic and help you better understand the most important features to consider when choosing an authentication and API authorization solution for client-side applications.
These slides will review:
- The broader classification of client-side applications and their legacy and more recent authentication and API authorization patterns
- Sender-constrained token patterns
- Solution patterns being employed to improve user experience in client-side applications
Best Practices for Running Kafka on Docker ContainersBlueData, Inc.
Docker containers provide an ideal foundation for running Kafka-as-a-Service on-premises or in the public cloud. However, using Docker containers in production environments for Big Data workloads using Kafka poses some challenges – including container management, scheduling, network configuration and security, and performance.
In this session at Kafka Summit in August 2017, Nanda Vijyaydev of BlueData shared lessons learned from implementing Kafka-as-a-Service with Docker containers.
https://kafka-summit.org/sessions/kafka-service-docker-containers
Docker is the world's leading software containerization platform.
This is a comprehensive introduction to Docker, suitable for delivering in introductory meetups to an audience who does not know about docker.
In case you want to deliver this presentation somewhere, kindly drop me a mail at aditya.konarde@gmail.com
You can contact me at:
Connect with me onLinkedIN: https://www.linkedin.com/in/adityakonarde
Add me on Facebook: https://www.facebook.com/Aditya.Konarde
Tweet to me @aditya_konarde
Unleashing Real-time Power with Kafka.pptxKnoldus Inc.
Unlock the potential of real-time data streaming with Kafka in this session. Learn the fundamentals, architecture, and seamless integration with Scala, empowering you to elevate your data processing capabilities. Perfect for developers at all levels, this hands-on experience will equip you to harness the power of real-time data streams effectively.
The ABC of Docker: The Absolute Best Compendium of DockerAniekan Akpaffiong
This presentation is my contribution to the body of work around Docker.
It codifies my experience so far, with Docker. The goal is to provide a concise yet complete introduction to Docker and its ecosystem.
I explore various Docker objects, compare containers and virtualization, provide usage examples, and discuss critical concepts around Docker and Linux. The compendium part of this, is aspirational. I will update and add to it as I have time and my experience with the product evolves.
Let me know what you think. Feedback and Likes are always appreciated.
Intro to Docker at the 2016 Evans Developer relations conferenceMano Marks
Building large scale apps traditionally has traditionally meant building large monolithic apps to handle everything. In the new age of the cloud and on premise data centers, increasingly the world is looking to containers and microservices. This allows flexibility and agility. Individual teams can choose the tools they need and be assured they'll work in the environment they want. And it also has implications for how we do developer relations, making it easier to deploy samples without worrying about environment. This session will look at microservices and how they are changing both the enterprise, and our work in developer relations.
AMIS SIG - Introducing Apache Kafka - Scalable, reliable Event Bus & Message ...Lucas Jellema
Introduction of Apache Kafka - the open source platform for real time message queuing and reliable, scalable, distributed event handling and high volume pub/sub implementation.
see GitHub https://github.com/MaartenSmeets/kafka-workshop for the workshop resources.
Docker is a tool designed to make it easier to create, deploy, and run applications
by using containers. Containers allow a developer to package up
an application with all of the parts it needs, such as libraries and other dependencies,
and ship it all out as one package. By doing so, thanks to the
container, the developer can rest assured that the application will run on
any other Linux machine regardless of any customized settings that machine
might have that could differ from the machine used for writing and testing
the code.
In a way, Docker is a bit like a virtual machine. But unlike a virtual
machine, rather than creating a whole virtual operating system, Docker allows
applications to use the same Linux kernel as the system that they’re
running on and only requires applications be shipped with things not already
running on the host computer. This gives a significant performance boost
and reduces the size of the application.
Similar to Kafka Summit SF 2017 - Best Practices for Running Kafka on Docker Containers (20)
Catch the Wave: SAP Event-Driven and Data Streaming for the Intelligence Ente...confluent
In our exclusive webinar, you'll learn why event-driven architecture is the key to unlocking cost efficiency, operational effectiveness, and profitability. Gain insights on how this approach differs from API-driven methods and why it's essential for your organization's success.
Unlocking the Power of IoT: A comprehensive approach to real-time insightsconfluent
In today's data-driven world, the Internet of Things (IoT) is revolutionizing industries and unlocking new possibilities. Join Data Reply, Confluent, and Imply as we unveil a comprehensive solution for IoT that harnesses the power of real-time insights.
Workshop híbrido: Stream Processing con Flinkconfluent
El Stream processing es un requisito previo de la pila de data streaming, que impulsa aplicaciones y pipelines en tiempo real.
Permite una mayor portabilidad de datos, una utilización optimizada de recursos y una mejor experiencia del cliente al procesar flujos de datos en tiempo real.
En nuestro taller práctico híbrido, aprenderás cómo filtrar, unir y enriquecer fácilmente datos en tiempo real dentro de Confluent Cloud utilizando nuestro servicio Flink sin servidor.
Industry 4.0: Building the Unified Namespace with Confluent, HiveMQ and Spark...confluent
Our talk will explore the transformative impact of integrating Confluent, HiveMQ, and SparkPlug in Industry 4.0, emphasizing the creation of a Unified Namespace.
In addition to the creation of a Unified Namespace, our webinar will also delve into Stream Governance and Scaling, highlighting how these aspects are crucial for managing complex data flows and ensuring robust, scalable IIoT-Platforms.
You will learn how to ensure data accuracy and reliability, expand your data processing capabilities, and optimize your data management processes.
Don't miss out on this opportunity to learn from industry experts and take your business to the next level.
La arquitectura impulsada por eventos (EDA) será el corazón del ecosistema de MAPFRE. Para seguir siendo competitivas, las empresas de hoy dependen cada vez más del análisis de datos en tiempo real, lo que les permite obtener información y tiempos de respuesta más rápidos. Los negocios con datos en tiempo real consisten en tomar conciencia de la situación, detectar y responder a lo que está sucediendo en el mundo ahora.
Eventos y Microservicios - Santander TechTalkconfluent
Durante esta sesión examinaremos cómo el mundo de los eventos y los microservicios se complementan y mejoran explorando cómo los patrones basados en eventos nos permiten descomponer monolitos de manera escalable, resiliente y desacoplada.
Purpose of the session is to have a dive into Apache, Kafka, Data Streaming and Kafka in the cloud
- Dive into Apache Kafka
- Data Streaming
- Kafka in the cloud
Build real-time streaming data pipelines to AWS with Confluentconfluent
Traditional data pipelines often face scalability issues and challenges related to cost, their monolithic design, and reliance on batch data processing. They also typically operate under the premise that all data needs to be stored in a single centralized data source before it's put to practical use. Confluent Cloud on Amazon Web Services (AWS) provides a fully managed cloud-native platform that helps you simplify the way you build real-time data flows using streaming data pipelines and Apache Kafka.
Q&A with Confluent Professional Services: Confluent Service Meshconfluent
No matter whether you are migrating your Kafka cluster to Confluent Cloud, running a cloud-hybrid environment or are in a different situation where data protection and encryption of sensitive information is required, Confluent Service Mesh allows you to transparently encrypt your data without the need to make code changes to you existing applications.
Citi Tech Talk: Event Driven Kafka Microservicesconfluent
Microservices have become a dominant architectural paradigm for building systems in the enterprise, but they are not without their tradeoffs. Learn how to build event-driven microservices with Apache Kafka
Confluent & GSI Webinars series - Session 3confluent
An in depth look at how Confluent is being used in the financial services industry. Gain an understanding of how organisations are utilising data in motion to solve common problems and gain benefits from their real time data capabilities.
It will look more deeply into some specific use cases and show how Confluent technology is used to manage costs and mitigate risks.
This session is aimed at Solutions Architects, Sales Engineers and Pre Sales, and also the more technically minded business aligned people. Whilst this is not a deeply technical session, a level of knowledge around Kafka would be helpful.
Transforming applications built with traditional messaging solutions such as TIBCO, MQ and Solace to be scalable, reliable and ready for the move to cloud
How can applications built with traditional messaging technologies like TIBCO, Solace and IBM MQ be modernised and be made cloud ready? What are the advantages to Event Streaming approaches to pub/sub vs traditional message queues? What are the strengeths and weaknesses of both approaches, and what use cases and requirements are actually a better fit for messaging than Kafka?
This session will show why the old paradigm does not work and that a new approach to the data strategy needs to be taken. It aims to show how a Data Streaming Platform is integral to the evolution of a company’s data strategy and how Confluent is not just an integration layer but the central nervous system for an organisation
Vous apprendrez également à :
• Créer plus rapidement des produits et fonctionnalités à l’aide d’une suite complète de connecteurs et d’outils de gestion des flux, et à connecter vos environnements à des pipelines de données
• Protéger vos données et charges de travail les plus critiques grâce à des garanties intégrées en matière de sécurité, de gouvernance et de résilience
• Déployer Kafka à grande échelle en quelques minutes tout en réduisant les coûts et la charge opérationnelle associés
Confluent Partner Tech Talk with Synthesisconfluent
A discussion on the arduous planning process, and deep dive into the design/architectural decisions.
Learn more about the networking, RBAC strategies, the automation, and the deployment plan.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
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COLLEGE BUS MANAGEMENT SYSTEM PROJECT REPORT.pdfKamal Acharya
The College Bus Management system is completely developed by Visual Basic .NET Version. The application is connect with most secured database language MS SQL Server. The application is develop by using best combination of front-end and back-end languages. The application is totally design like flat user interface. This flat user interface is more attractive user interface in 2017. The application is gives more important to the system functionality. The application is to manage the student’s details, driver’s details, bus details, bus route details, bus fees details and more. The application has only one unit for admin. The admin can manage the entire application. The admin can login into the application by using username and password of the admin. The application is develop for big and small colleges. It is more user friendly for non-computer person. Even they can easily learn how to manage the application within hours. The application is more secure by the admin. The system will give an effective output for the VB.Net and SQL Server given as input to the system. The compiled java program given as input to the system, after scanning the program will generate different reports. The application generates the report for users. The admin can view and download the report of the data. The application deliver the excel format reports. Because, excel formatted reports is very easy to understand the income and expense of the college bus. This application is mainly develop for windows operating system users. In 2017, 73% of people enterprises are using windows operating system. So the application will easily install for all the windows operating system users. The application-developed size is very low. The application consumes very low space in disk. Therefore, the user can allocate very minimum local disk space for this application.
TECHNICAL TRAINING MANUAL GENERAL FAMILIARIZATION COURSEDuvanRamosGarzon1
AIRCRAFT GENERAL
The Single Aisle is the most advanced family aircraft in service today, with fly-by-wire flight controls.
The A318, A319, A320 and A321 are twin-engine subsonic medium range aircraft.
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Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
Explore the innovative world of trenchless pipe repair with our comprehensive guide, "The Benefits and Techniques of Trenchless Pipe Repair." This document delves into the modern methods of repairing underground pipes without the need for extensive excavation, highlighting the numerous advantages and the latest techniques used in the industry.
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3. Kafka – A Real-Time Messaging System
• Open source streaming platform
• Handles firehose-style events
with low latency
• Storage is a scalable pub/sub
queue
• Supports diversified consumer
groups for the same message
• Consumers pull messages at
their pace
4. Role of Producers and Consumers
• Independent services that
send/receive messages
• Can be written in many
languages
• Purpose-built for specific actions
• Mostly operate on high
frequency events and data
• Availability and scalability are
important
5. What is a Docker Container?
• Lightweight, stand-alone, executable
package of software to run specific
services
• Runs on all major Linux distributions
• On any infrastructure including VMs,
bare-metal, and in the cloud
• Package includes code, runtime,
system libraries, configurations, etc.
• Run as an isolated process in user
space
6. Docker Containers vs. Virtual Machines
• Unlike VMs, containers virtualize OS
and not hardware
• More portable and efficient
• Abstraction at the app layer that
packages app and dependencies
• Multiple containers share the base
kernel
• Take up less space and start almost
immediately
7. Containers = the Future of Apps
Infrastructure
• Agility and elasticity
• Standardized environments
(dev, test, prod)
• Portability
(on-premises and cloud)
• Higher resource utilization
Applications
• Fool-proof packaging
(configs, libraries, driver
versions, etc.)
• Repeatable builds and
orchestration
• Faster app dev cycles
8. Considerations for Kafka Deployment
• Multiple processes: Kafka, Zookeeper,
Schema Registry, and more
• Different infrastructure needs for
different roles
• Auto-configuration for multi-node
deployment
• Dev, Test, and Production in
isolated/multi-tenant environment
• Sharing resources with other processes
on same host machines
9. Prod/Cons
Prod/Cons
Kafka Server Requirements
• Broker – heavy on storage
with decent cache
• More memory is needed
for in-memory processing
• Zookeeper also needs
memory and may not need
lots of disk space
• Sequential writes are light
on CPU requirements
ZookeeperZookeeper
Zookeeper
Schema
Registry
Prod/Cons
Broker
10. How We Did It: Design Decisions I
• Run Kafka (e.g. Confluent distribution) and related
services and tools / applications unmodified
– Deploy all services that run on a single bare-metal
host in a single container
• Multi-tenancy support is key
– Network and storage security
• Clusters of containers span physical hosts
11. How We Did It: Design Decisions II
• Images built to “auto-configure” themselves at time
of instantiation
– Not all instances of a single image run the same set
of services when instantiated
• Zookeeper vs. Broker cluster nodes
– Support “reboot” of cluster
– Ability to scale on demand
12. How We Did It: Implementation
Resource Utilization
• CPU shares translated to vCPU for usability
• No over-provisioning of memory
• Node storage using device mappers, thin pools and
logical volumes
• Shared external HDFS for Hadoop storage
Noisy neighbors
13. How We Did It: Resource Allocation
• Users to choose
“flavors” while
launching containers
• Storage heavy
containers can have
more disk space
• vCPUs * n = cpu-shares
14. ① Get started with Kafka (e.g.
Confluent community edition)
② Evaluate features/configurations
simultaneously on smaller
hardware footprint
③ Prototype multiple data pipelines
quickly with dockerized producers
and consumers
① Spin up dev/test clusters with
replica image of production
② QA/UAT using production
configuration without re-
inventing the wheel
③ Offload specific users and
workloads from production
① LOB multi-tenancy with strict
resource allocations
② Bare-metal performance for
business critical workloads
③ Share data hub / data lake with
strict access controls
Kafka On Docker Use Cases
Prototyping Departmental Enterprise
Exploring
the Value of Kafka
Initial Departmental
Deployments
Enterprise-Wide,
Mission-Critical Deployments
15. Multi-Tenant Deployment
5.10 3.3
2.1
Compute Isolation
Compute Isolation
Team 1 Team 2 Team 3
Build Components End to End Testing Prod Environment
Team 1 Team 2 Team3
Multiple teams or business groups
Evaluate different Kafka use cases
(e.g. producers, consumers,
pipelines)
Use different services & tools
(e.g. Broker, Zookeeper, Schema
Registry, API Gateway)
Use different distributions of
standalone Kafka and/or Hadoop
BlueData EPIC software platform
Shared server infrastructure with
node labels
Shared data sets for HDFS access
Multiple distributions, services, tools on shared, cost-effective infrastructure
Shared Servers
Dev/QA Hardware
Shared, Centrally Managed Server Infrastructure
Confluent 3.2
Prod Hardware
Apache Kafka 0.9Apache Kafka 0.8
16. How We Did It: Security Considerations
• Security is essential since containers and host share one kernel
– Non-privileged containers
• Achieved through layered set of capabilities
• Different capabilities provide different levels of isolation and protection
• Add “capabilities” to a container based on what operations are permitted
17. How We Did It: Network Architecture
• Connect containers across
hosts
• Persistence of IP address
across container restart
• DHCP/DNS service required
for IP allocation and hostname
resolution
• Deploy VLANs and VxLAN
tunnels for tenant-level traffic
isolation
18. Data Volumes and Storage Drivers
Data Volume
• A directory on host FS
• Data not deleted when
container is deleted
Device Mapper Storage Driver
• Default – OverlayFS
• We use direct-lvm thinpool
with devicemapper
• Data is deleted with container
19. How We Did It: Sample Dockerfile
# Confluent Kafka 3.2.1 docker image
FROM bluedata/centos7:latest
#Install java 1.8
RUN yum -y install java-1.8.0-openjdk-devel
#Download and extract Kafka installation tar file
RUN mkdir /usr/lib/kafka;curl -s http://packages.confluent.io/archive/3.2/confluent-3.2.1-2.11.tar.gz | tar xz -C
/usr/lib/kafka/
##Create necessary directories for Kafka and Zookeeper to run
RUN mkdir /var/lib/zookeeper
20. BlueData Application Image (.bin file)
Application bin file
Docker
image
CentOS
Dockerfile
RHEL
Dockerfile
appconfig
conf Init.d startscript
<app>
logo file
<app>.wb
bdwb
command
clusterconfig, image, role,
appconfig, catalog, service, ..
Sources
Docker file,
logo .PNG,
Init.d
RuntimeSoftware Bits
OR
Development
(e.g. extract .bin and modify to
create new bin)
21. How We Did It: Deployment Configuration
#!/usr/bin/env bdwb
##############################################################
#
# Sample workbench instructions for building a BlueData catalog entry.
#
##############################################################
#
# YOUR_ORGANIZATION_NAME must be replaced with a valid organization name. Please
# refer to 'help builder organization' for details.
# builder organization --name YOUR_ORGANIZATION_NAME
builder organization --name BlueData
## Begin a new catalog entry
catalog new --distroid confluent-kafka --name "Confluent Kafka 3.2.1"
--desc "The free, open-source streaming platform (Enterprise edition) based on Apache
Kafka. Confluent Platform is the best way to get started
with real-time data streams."
--categories Kafka --version 4.0
## Define all node roles for the virtual
cluster.
role add broker 1+
role add zookeeper 1+
role add schemareg 1+
role add gateway 0+
## Define all services that are available in the virtual cluster.
service add --srvcid kafka-broker --name "Kafka Broker service" --port 9092
service add --srvcid zookeeper --name "Zookeeper service" --port 2181
service add --srvcid schema-registry --name "Schema-registry service" --port 8081
service add --srvcid control-center --name "Control center service" --port 9021
## Dev Configuration. Multiple services are placed on same container
clusterconfig new --configid default
clusterconfig assign --configid default --role broker --srvcids kafka-broker zookeeper schema-registry
control-center
clusterconfig assign --configid default --role zookeeper --srvcids kafka-server zookeeper
## Prod Configuration. Services run on dedicated nodes with special
attributes
clusterconfig new --configid production
clusterconfig assign --configid production --role broker --srvcids kafka-broker
clusterconfig assign --configid production --role zookeeper --srvcids zookeeper
clusterconfig assign --configid production --role schemareg --srvcids schemareg
clusterconfig assign --configid production --role gateway --srvcids control-center
22. How We Did It: Deployment Configuration
#Configure your docker nodes with appropriate values
appconfig autogen --replace /tmp/zookeeper/myid –pattern @@ID@@ --macro UNIQUE_SELF_NODE_INT
appconfig autogen --replace /usr/lib/kafka/etc/kafka/server.properties –pattern @@HOST@@ --macro GET_NODE_FQDN
appconfig autogen --replace /usr/lib/kafka/etc/kafka/server.properties –pattern @@zookeeper.connet@@ --macro
ZOOKEEPER_SERVICE_STRING
#Start services in the order specified
REGISTER_START_SERVICE_SYSV zookeeper
REGISTER_START_SERVICE_SYSV kafka-broker –wait zookeeper
REGISTER_START_SERVICE_SYSV schema-registry –wait zookeeper
29. App Store for Kafka, Spark, & More
Pre-built
images, or
author your
own Docker
app images
with our App
Workbench
Docker image + app config scripts +
metadata (e.g. name, logo)
30. Kafka on Docker: Key Takeaways
• All apps can be “Dockerized”, including stateful apps
– Docker containers enable a more flexible and agile
deployment model
– Faster application development cycles for application
developers, data scientists, and engineers
– Enables DevOps for data engineering teams
31. Kafka on Docker: Key Takeaways
• Enterprise deployment requirements:
– Docker base images should include all needed services
(Kafka, Zookeeper, Schema registry, etc.), libraries, jar files
– Container orchestration, including networking and storage,
depends on standards enforced by enterprises
– Resource-aware runtime environment, including CPU and
RAM
– Sequence-aware app deployment needs more thought
32. Kafka on Docker: Key Takeaways
• Ecosystem considerations:
– Ability to build producers and consumers on demand
– Quick instantiation and access to Kafka services
– Interoperability between services is easier with Docker
– Ability to run, rerun, and scale clusters
– Ability to deploy & integrate enterprise-ready solution
33. Kafka on Docker: Key Takeaways
• Enterprise deployment challenges:
– Access to container secured with ssh keypair or PAM
module (LDAP/AD)
– Access to Kafka from Data Science applications
– Management agents in Docker images
– Runtime injection of resource and configuration
information
• Consider a turnkey software solution (e.g. BlueData)
to accelerate time to value and avoid DIY pitfalls