This document provides an agenda and overview of an Apache Kafka integration meetup with Mulesoft 4.3. The meetup will include introductions, an overview of Kafka basics and components, a demonstration of the Mulesoft Kafka connector, and a networking session. Kafka is introduced as a distributed publish-subscribe messaging system that provides reliability, scalability, durability and high performance. Key Kafka concepts that will be covered include topics, partitions, producers, consumers, brokers and the commit log architecture. The Mulesoft Kafka connector operations for consuming, publishing and seeking messages will also be demonstrated.
Delivering: from Kafka to WebSockets | Adam Warski, SoftwareMillHostedbyConfluent
Here's the challenge: we've got a Kafka topic, where services publish messages to be delivered to browser-based clients through web sockets.
Sounds simple? It might, but we're faced with an increasing number of messages, as well as a growing count of web socket clients. How do we scale our solution? As our system contains a larger number of servers, failures become more frequent. How to ensure fault tolerance?
There’s a couple possible architectures. Each websocket node might consume all messages. Otherwise, we need an intermediary, which redistributes the messages to the proper web socket nodes.
Here, we might either use a Kafka topic, or a streaming forwarding service. However, we still need a feedback loop so that the intermediary knows where to distribute messages.
We’ll take a look at the strengths and weaknesses of each solution, as well as limitations created by the chosen technologies (Kafka and web sockets).
A Look into the Mirror: Patterns and Best Practices for MirrorMaker2 | Cliff ...HostedbyConfluent
From migrations between Apache Kafka clusters to multi-region deployments across datacenters, the introduction of MirrorMaker2 has expanded the possibilities for Apache Kafka deployments and use cases. In this session you will learn about patterns, best practices, and learnings compiled from running MirrorMaker2 in production at every scale.
Introducing Confluent labs Parallel Consumer client | Anthony Stubbes, ConfluentHostedbyConfluent
Consuming messages in parallel is what Apache Kafka® is all about, so you may well wonder, why would we want anything else? It turns out that, in practice, there are a number of situations where Kafka’s partition-level parallelism gets in the way of optimal design.
This session will go over some of these types of situations that can benefit from parallel message processing within a single application instance (aka slow consumers or competing consumers), and then introduce the new Parallel Consumer labs project from Confluent, which can improve functionality and massively improve performance in such situations.
It will cover the
- Different ordering modes of the client
- Relative performance improvements
- Usage with other components like Kafka Streams
- An introduction to the internal architecture of the project
- How it can achieve all this in a reassignment friendly manner
Mulesoft with ELK (Elastic Search, Log stash, Kibana)Gaurav Sethi
Use the Elastic Stack (ELK stack) to analyze the business data and API analytics.
You can use Logstash for Filebeat to process Anypoint Platform log files, insert them into an Elasticsearch database, and then analyze them with Kibana.
ELK stands for the three Elastic products - Elasticsearch, Logstash, and Kibana
To understand what the Elastic core products, we will use a simple architecture:
1. The logs will be created by an application and pushed into the AWS SQS Queue.
2. Logstash aggregates the logs from different sources and processes them.
3. Elasticsearch stores and indexes the data in order to search it.
4. Kibana is the visualization tool that makes sense of the data.
Delivering: from Kafka to WebSockets | Adam Warski, SoftwareMillHostedbyConfluent
Here's the challenge: we've got a Kafka topic, where services publish messages to be delivered to browser-based clients through web sockets.
Sounds simple? It might, but we're faced with an increasing number of messages, as well as a growing count of web socket clients. How do we scale our solution? As our system contains a larger number of servers, failures become more frequent. How to ensure fault tolerance?
There’s a couple possible architectures. Each websocket node might consume all messages. Otherwise, we need an intermediary, which redistributes the messages to the proper web socket nodes.
Here, we might either use a Kafka topic, or a streaming forwarding service. However, we still need a feedback loop so that the intermediary knows where to distribute messages.
We’ll take a look at the strengths and weaknesses of each solution, as well as limitations created by the chosen technologies (Kafka and web sockets).
A Look into the Mirror: Patterns and Best Practices for MirrorMaker2 | Cliff ...HostedbyConfluent
From migrations between Apache Kafka clusters to multi-region deployments across datacenters, the introduction of MirrorMaker2 has expanded the possibilities for Apache Kafka deployments and use cases. In this session you will learn about patterns, best practices, and learnings compiled from running MirrorMaker2 in production at every scale.
Introducing Confluent labs Parallel Consumer client | Anthony Stubbes, ConfluentHostedbyConfluent
Consuming messages in parallel is what Apache Kafka® is all about, so you may well wonder, why would we want anything else? It turns out that, in practice, there are a number of situations where Kafka’s partition-level parallelism gets in the way of optimal design.
This session will go over some of these types of situations that can benefit from parallel message processing within a single application instance (aka slow consumers or competing consumers), and then introduce the new Parallel Consumer labs project from Confluent, which can improve functionality and massively improve performance in such situations.
It will cover the
- Different ordering modes of the client
- Relative performance improvements
- Usage with other components like Kafka Streams
- An introduction to the internal architecture of the project
- How it can achieve all this in a reassignment friendly manner
Mulesoft with ELK (Elastic Search, Log stash, Kibana)Gaurav Sethi
Use the Elastic Stack (ELK stack) to analyze the business data and API analytics.
You can use Logstash for Filebeat to process Anypoint Platform log files, insert them into an Elasticsearch database, and then analyze them with Kibana.
ELK stands for the three Elastic products - Elasticsearch, Logstash, and Kibana
To understand what the Elastic core products, we will use a simple architecture:
1. The logs will be created by an application and pushed into the AWS SQS Queue.
2. Logstash aggregates the logs from different sources and processes them.
3. Elasticsearch stores and indexes the data in order to search it.
4. Kibana is the visualization tool that makes sense of the data.
Kafka at the core of an AIOps pipeline | Sunanda Kommula, Selector.ai and Ala...HostedbyConfluent
Large networks consist of a diverse range of equipment, across private, public, hybrid clouds and partner networks. A hierarchical network has layers of infrastructure, catering to access, core, or distribution roles, managed by different organizations specialized to architect the right network hardware, software, and features for that network layer. The nature of data generated by each component can vary in type and form, including logs, events, metrics, or alarms.
The diversity of data generated by a large network is beyond human scale. Apache Kafka® is a critical hub in large networks, empowering AIOps to enhance decision making, improve analysis and insights by contextualizing large volumes of operational data. Kafka solved the big problem of collecting, processing, storing and normalizing data at scale, allowing us to focus on building the AIOps pipeline.
Our platform connects the dots across relevant operations data and provides operations teams with simple and powerful access to insights, from within increasingly popular collaboration environments like Slack and Microsoft teams. The pipeline must also integrate with automation solutions.
This session will cover how large volumes of streaming messages can be received by parallel Kafka consumers, and turned into action by network operations teams, dramatically reducing downtime and improving performance.
Lessons from the field: Catalog of Kafka Deployments | Joseph Niemiec, ClouderaHostedbyConfluent
Streaming architectures have been on the rise steadily and as a result, we have seen the adoption of Kafka go up too. With the diverse spread of use cases across multiple industries, we have seen a variety of Kafka deployments across our hundreds of Kafka customers. Along the way, we have learnt some best practices as well as what not to do in mission-critical architectures. Join Joe Niemiec, Sr. Product Manager at Cloudera, as he shares these insights in this session that covers topics such as - The many ways that Kafka has been deployed in the field Standalone clusters, multiple clusters in a single data center and multiple clusters geographically distributed performing replication Clusters of all sizes small and large, few messages to hundreds of thousands per second Discussion about architecture failure domains Configurations tuned and used in specific deployments
Apache Kafka is a distributed publish subscribe messaging system which was originally developed at LinkedIn and later on became a part of the Apache project.
Kafka is fast, agile, scalable and distributed by design.
Aaron Lieberman, a MuleSoft Practice Manager and Lead Consultant at Big Compass will walk us through on how Runtime Fabric can deploy and manage applications deployed to AWS. He will also demonstrate on how a Mule 3 and Mule 4 application can run in parallel in the same Runtime Fabric. With any public API, it has never been more important to enhance your security posture and provide deep visibility with logging and monitoring techniques. Aaron will also talk about how security and logging can work seamlessly with your distributed application network to make supporting any application better.
Finally, any modern application must be highly available and provide fault tolerance. We will have some fun with wreaking havoc on our Runtime Fabric infrastructure, and see how the highly available architecture holds up against potential infrastructure outages and attacks.
Event-driven Applications with Kafka, Micronaut, and AWS Lambda | Dave Klein,...HostedbyConfluent
One of the great things about running applications in the cloud is that you only pay for the resources that you use. But that also makes it more important than ever for our applications to be resource-efficient. This becomes even more critical when we use serverless functions.
Micronaut is an application framework that provides dependency injection, developer productivity features, and excellent support for Apache Kafka. By performing dependency injection, AOP, and other productivity-enhancing magic at compile time, Micronaut allows us to build smaller, more efficient microservices and serverless functions.
In this session, we'll explore the ways that Apache Kafka and Micronaut work together to enable us to build fast, efficient, event-driven applications. Then we'll see it in action, using the AWS Lambda Sink Connector for Confluent Cloud.
Don't Cross the Streams! (or do, we got you)Caito Scherr
Ghostbusters better get ready, because it's time to cross (ok, join) some streams! This talk will include easy-to-follow steps to set up and maximize a powerful, streaming data pipeline with the newest features from Apache Flink. This talk is for anyone using (or interested in) stream processing who wants to minimize their development overhead, and particularly for those who want to do so while leveraging available Open Source tools.
MuleSoft Deployment Strategies (RTF vs Hybrid vs CloudHub)Prashanth Kurimella
Differences between MuleSoft Deployment Strategies (RTF vs Hybrid vs CloudHub)
For additional information, read https://www.linkedin.com/pulse/mulesoft-deployment-strategies-rtf-vs-hybrid-cloudhub-kurimella/
Organic Growth and A Good Night Sleep: Effective Kafka Operations at Pinteres...confluent
Even though Kafka is scalable by design, proper handling of over one petabyte of data a day requires much more than Kafka’s scalability. Several challenges present themselves in a data centric business at this scale. These challenges include capacity planning, provisioning, message auditing, monitoring and alerting, rebalancing workloads with changes in traffic patterns, data lineage, handling service degradation and system outages, optimizing cost, upgrades, etc. In this talk we describe how at Pinterest we tackle some of these challenges and share some of the key lessons that we learned in the process. Specifically we will share how we:
* Automate Kafka cluster maintenance
* Manage over 150K partitions
* Manage upgrade lifecycle
* Track / troubleshoot thousands of data pipelines
Function Mesh: Complex Streaming Jobs Made Simple - Pulsar Summit NA 2021StreamNative
Pulsar Function is a succinct computing abstraction Apache Pulsar provides users to express simple ETL and streaming tasks. The simplicity comes in two folds: Simple Interface and Simple Deployment. As it has been adopted, we realized that the native support of organizing multiple functions into integrity will be very beneficial. With such support, people can express and manage multi-stage jobs easily. In addition, this support also provides the possibility of higher-level abstraction DSL to further simplify the job composition. We call this new feature -- Function Mesh.
This talk aims to provide a thorough walkthrough of this new Function Mesh Feature, including its design, implementation, use cases, and examples, to help people seeking simple streaming solutions understand this newly created powerful tool in Apache Pulsar.
Building Stream Infrastructure across Multiple Data Centers with Apache KafkaGuozhang Wang
To manage the ever-increasing volume and velocity of data within your company, you have successfully made the transition from single machines and one-off solutions to large distributed stream infrastructures in your data center, powered by Apache Kafka. But what if one data center is not enough? I will describe building resilient data pipelines with Apache Kafka that span multiple data centers and points of presence, and provide an overview of best practices and common patterns while covering key areas such as architecture guidelines, data replication, and mirroring as well as disaster scenarios and failure handling.
Salesforce enabling real time scenarios at scale using kafkaThomas Alex
Nishant Gupta from Salesforce talked about Ajna, a service for monitoring system health across global data centers in real time, and how Kafka is at the center of this system. The talk covers the scenario, key challenges, learnings and best practices.
Kafka Excellence at Scale – Cloud, Kubernetes, Infrastructure as Code (Vik Wa...HostedbyConfluent
Cloud is changing the world; Kubernetes is changing the world; real-time event streaming is changing the world. In this talk we explore some of best practices to synergistically combine the power of these paradigm shifts to achieve a much greater return on your Kafka investments. From declarative deployments, zero-downtime upgrades, elastic scaling to self-healing and automated governance, learn how you can bring the next level of speed, agility, resilience, and security to your Kafka implementations.
Kafka at the core of an AIOps pipeline | Sunanda Kommula, Selector.ai and Ala...HostedbyConfluent
Large networks consist of a diverse range of equipment, across private, public, hybrid clouds and partner networks. A hierarchical network has layers of infrastructure, catering to access, core, or distribution roles, managed by different organizations specialized to architect the right network hardware, software, and features for that network layer. The nature of data generated by each component can vary in type and form, including logs, events, metrics, or alarms.
The diversity of data generated by a large network is beyond human scale. Apache Kafka® is a critical hub in large networks, empowering AIOps to enhance decision making, improve analysis and insights by contextualizing large volumes of operational data. Kafka solved the big problem of collecting, processing, storing and normalizing data at scale, allowing us to focus on building the AIOps pipeline.
Our platform connects the dots across relevant operations data and provides operations teams with simple and powerful access to insights, from within increasingly popular collaboration environments like Slack and Microsoft teams. The pipeline must also integrate with automation solutions.
This session will cover how large volumes of streaming messages can be received by parallel Kafka consumers, and turned into action by network operations teams, dramatically reducing downtime and improving performance.
Lessons from the field: Catalog of Kafka Deployments | Joseph Niemiec, ClouderaHostedbyConfluent
Streaming architectures have been on the rise steadily and as a result, we have seen the adoption of Kafka go up too. With the diverse spread of use cases across multiple industries, we have seen a variety of Kafka deployments across our hundreds of Kafka customers. Along the way, we have learnt some best practices as well as what not to do in mission-critical architectures. Join Joe Niemiec, Sr. Product Manager at Cloudera, as he shares these insights in this session that covers topics such as - The many ways that Kafka has been deployed in the field Standalone clusters, multiple clusters in a single data center and multiple clusters geographically distributed performing replication Clusters of all sizes small and large, few messages to hundreds of thousands per second Discussion about architecture failure domains Configurations tuned and used in specific deployments
Apache Kafka is a distributed publish subscribe messaging system which was originally developed at LinkedIn and later on became a part of the Apache project.
Kafka is fast, agile, scalable and distributed by design.
Aaron Lieberman, a MuleSoft Practice Manager and Lead Consultant at Big Compass will walk us through on how Runtime Fabric can deploy and manage applications deployed to AWS. He will also demonstrate on how a Mule 3 and Mule 4 application can run in parallel in the same Runtime Fabric. With any public API, it has never been more important to enhance your security posture and provide deep visibility with logging and monitoring techniques. Aaron will also talk about how security and logging can work seamlessly with your distributed application network to make supporting any application better.
Finally, any modern application must be highly available and provide fault tolerance. We will have some fun with wreaking havoc on our Runtime Fabric infrastructure, and see how the highly available architecture holds up against potential infrastructure outages and attacks.
Event-driven Applications with Kafka, Micronaut, and AWS Lambda | Dave Klein,...HostedbyConfluent
One of the great things about running applications in the cloud is that you only pay for the resources that you use. But that also makes it more important than ever for our applications to be resource-efficient. This becomes even more critical when we use serverless functions.
Micronaut is an application framework that provides dependency injection, developer productivity features, and excellent support for Apache Kafka. By performing dependency injection, AOP, and other productivity-enhancing magic at compile time, Micronaut allows us to build smaller, more efficient microservices and serverless functions.
In this session, we'll explore the ways that Apache Kafka and Micronaut work together to enable us to build fast, efficient, event-driven applications. Then we'll see it in action, using the AWS Lambda Sink Connector for Confluent Cloud.
Don't Cross the Streams! (or do, we got you)Caito Scherr
Ghostbusters better get ready, because it's time to cross (ok, join) some streams! This talk will include easy-to-follow steps to set up and maximize a powerful, streaming data pipeline with the newest features from Apache Flink. This talk is for anyone using (or interested in) stream processing who wants to minimize their development overhead, and particularly for those who want to do so while leveraging available Open Source tools.
MuleSoft Deployment Strategies (RTF vs Hybrid vs CloudHub)Prashanth Kurimella
Differences between MuleSoft Deployment Strategies (RTF vs Hybrid vs CloudHub)
For additional information, read https://www.linkedin.com/pulse/mulesoft-deployment-strategies-rtf-vs-hybrid-cloudhub-kurimella/
Organic Growth and A Good Night Sleep: Effective Kafka Operations at Pinteres...confluent
Even though Kafka is scalable by design, proper handling of over one petabyte of data a day requires much more than Kafka’s scalability. Several challenges present themselves in a data centric business at this scale. These challenges include capacity planning, provisioning, message auditing, monitoring and alerting, rebalancing workloads with changes in traffic patterns, data lineage, handling service degradation and system outages, optimizing cost, upgrades, etc. In this talk we describe how at Pinterest we tackle some of these challenges and share some of the key lessons that we learned in the process. Specifically we will share how we:
* Automate Kafka cluster maintenance
* Manage over 150K partitions
* Manage upgrade lifecycle
* Track / troubleshoot thousands of data pipelines
Function Mesh: Complex Streaming Jobs Made Simple - Pulsar Summit NA 2021StreamNative
Pulsar Function is a succinct computing abstraction Apache Pulsar provides users to express simple ETL and streaming tasks. The simplicity comes in two folds: Simple Interface and Simple Deployment. As it has been adopted, we realized that the native support of organizing multiple functions into integrity will be very beneficial. With such support, people can express and manage multi-stage jobs easily. In addition, this support also provides the possibility of higher-level abstraction DSL to further simplify the job composition. We call this new feature -- Function Mesh.
This talk aims to provide a thorough walkthrough of this new Function Mesh Feature, including its design, implementation, use cases, and examples, to help people seeking simple streaming solutions understand this newly created powerful tool in Apache Pulsar.
Building Stream Infrastructure across Multiple Data Centers with Apache KafkaGuozhang Wang
To manage the ever-increasing volume and velocity of data within your company, you have successfully made the transition from single machines and one-off solutions to large distributed stream infrastructures in your data center, powered by Apache Kafka. But what if one data center is not enough? I will describe building resilient data pipelines with Apache Kafka that span multiple data centers and points of presence, and provide an overview of best practices and common patterns while covering key areas such as architecture guidelines, data replication, and mirroring as well as disaster scenarios and failure handling.
Salesforce enabling real time scenarios at scale using kafkaThomas Alex
Nishant Gupta from Salesforce talked about Ajna, a service for monitoring system health across global data centers in real time, and how Kafka is at the center of this system. The talk covers the scenario, key challenges, learnings and best practices.
Kafka Excellence at Scale – Cloud, Kubernetes, Infrastructure as Code (Vik Wa...HostedbyConfluent
Cloud is changing the world; Kubernetes is changing the world; real-time event streaming is changing the world. In this talk we explore some of best practices to synergistically combine the power of these paradigm shifts to achieve a much greater return on your Kafka investments. From declarative deployments, zero-downtime upgrades, elastic scaling to self-healing and automated governance, learn how you can bring the next level of speed, agility, resilience, and security to your Kafka implementations.
In this session you will learn:
1. Kafka Overview
2. Need for Kafka
3. Kafka Architecture
4. Kafka Components
5. ZooKeeper Overview
6. Leader Node
For more information, visit: https://www.mindsmapped.com/courses/big-data-hadoop/hadoop-developer-training-a-step-by-step-tutorial/
Unlocking the Power of Apache Kafka: How Kafka Listeners Facilitate Real-time...Denodo
Watch full webinar here: https://buff.ly/43PDVsz
In today's fast-paced, data-driven world, organizations need real-time data pipelines and streaming applications to make informed decisions. Apache Kafka, a distributed streaming platform, provides a powerful solution for building such applications and, at the same time, gives the ability to scale without downtime and to work with high volumes of data. At the heart of Apache Kafka lies Kafka Topics, which enable communication between clients and brokers in the Kafka cluster.
Join us for this session with Pooja Dusane, Data Engineer at Denodo where we will explore the critical role that Kafka listeners play in enabling connectivity to Kafka Topics. We'll dive deep into the technical details, discussing the key concepts of Kafka listeners, including their role in enabling real-time communication between consumers and producers. We'll also explore the various configuration options available for Kafka listeners and demonstrate how they can be customized to suit specific use cases.
Attend and Learn:
- The critical role that Kafka listeners play in enabling connectivity in Apache Kafka.
- Key concepts of Kafka listeners and how they enable real-time communication between clients and brokers.
- Configuration options available for Kafka listeners and how they can be customized to suit specific use cases.
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.
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.
Introduction to Kafka Streams PresentationKnoldus Inc.
Kafka Streams is a client library providing organizations with a particularly efficient framework for processing streaming data. It offers a streamlined method for creating applications and microservices that must process data in real-time to be effective. Using the Streams API within Apache Kafka, the solution fundamentally transforms input Kafka topics into output Kafka topics. The benefits are important: Kafka Streams pairs the ease of utilizing standard Java and Scala application code on the client end with the strength of Kafka’s robust server-side cluster architecture.
Fundamentals and Architecture of Apache KafkaAngelo Cesaro
Fundamentals and Architecture of Apache Kafka.
This presentation explains Apache Kafka's architecture and internal design giving an overview of Kafka internal functions, including:
Brokers, Replication, Partitions, Producers, Consumers, Commit log, comparison over traditional message queues.
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.
Salesforce integration best practices columbus meetupMuleSoft Meetup
onnectivity Overview
Connectivity to Salesforce Clouds
Connectors and Salesforce APIs
Connector interacting with Salesforce core
Composite Connector
Triggers
Establishing a connected app for MuleSoft Connectors
Salesforce Integration Best Practices
When to move data into SFDC
Appropriate use of APEX
Salesforce integration technologies and considerations
Data Virtualization/Live Read
Data Manipulation and Migration
Real-time changes, events and Streaming
Resources
Salesforce Accelerators for Service Cloud and Commerce Cloud
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
5. 5
● Apache Kafka started at LinkedIn and later it became an open-sourced project.
● Kafka is a distributed publish-subscribe messaging system.
● Apache Kafka is a java application, and can run on many operating systems like Windows,
MacOS, Linux, and others.
● Kafka is fast, scalable, and durable and fault tolerant messaging system.
● Kafka is suitable for both offline and online message consumption.
● Kafka messages are persisted on the disk and replicated within the cluster to prevent data
loss.
● Kafka can be used as a distributed enterprise messaging system or stream processing
platform via Kafka stream API.
Apache KAFKA
6. Advantages of Apache KAFKA
● Reliability −
Since Kafka is distributed, partitioned, replicated and fault tolerant, it is very Reliable.
● Scalability −
Apache Kafka can handle scalability in all the four dimensions, i.e. event producers, event
processors, event consumers and event connectors.
Kafka messaging system scales easily without down time.
● Durability −
Kafka uses Distributed commit log which means messages persists on disk as fast as
possible, hence it is durable.
Once consumed , it does not disappear from the topic.
● Performance −
Kafka has high throughput for both publishing and subscribing messages.
It maintains stable performance even many TB of messages are stored.
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7. Use Cases of Apache KAFKA
● Messaging System
● Stream Processing
● Website Activity Tracking (Event Tracking)
● Log Aggregation
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8. KAFKA Architecture
Data in Kafka is organized by topics. Each topic is partitioned, and each partition can have
multiple replicas. Those replicas are stored on brokers, and each broker typically stores hundreds
or even thousands of replicas belonging to different topics and partitions.
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10. Component of Apache KAFKA
● Topic is the name of the category or feed where records have been published.
Topics are always multi-subscriber as they can have zero or more consumers
that subscribe to the data written to them.
● Producers publish data to topics of their choice. It can publish data to one or
more Kafka topics.
● Consumers consume data from topics. Consumers subscribe to one or more
topics and consume published messages by pulling data from the brokers.
● Partition: Topics may have many partitions, so they can handle an arbitrary
amount of data.
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11. Component of Apache KAFKA
● Partition offset: Each partitioned message has a unique id and it is known as
an offset.
● Brokers are simple system responsible for maintaining the published data.
Each broker may have zero or more partitions per topic.
● Kafka Cluster: Kafka's server has one or more brokers, called Kafka Cluster.
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13. 13
● Commit: Commits the offsets associated to a message or batch of messages
consumed in a Message Listener
● Consume: This operation is used to receive the message from one or more
kafka topic.
● Message Listener: This source supports the consumption of messages from a
Kafka Cluster, producing a single message to the flow, it works similar to
consume
● Publish: This operation is used to publish message to specified kafka topic,
publish operation support the transactions
● Seek: Sets the current offset value for the given topic, partition and consumer
group of the listener
KAFKA CONNECTOR IN MULE
