1) The document discusses using stateful stream processing to build lightweight microservices that evolve a shared narrative. It outlines various tools from the stream processing toolkit like Kafka, KStreams, KTables, state stores, and transactions that can be used.
2) Various patterns for building stateless, stateful, and joined streaming services are presented, including gates, sidecars and stream-asides. These can be combined to process events and build views.
3) An evolutionary approach is suggested where services start small and stateless, becoming stateful if needed, and layering contexts within contexts. This allows systems to balance sunk costs and future flexibility.
Building distributed systems is challenging. Luckily, Apache Kafka provides a powerful toolkit for putting together big services as a set of scalable, decoupled components. In this talk, I'll describe some of the design tradeoffs when building microservices, and how Kafka's powerful abstractions can help. I'll also talk a little bit about what the community has been up to with Kafka Streams, Kafka Connect, and exactly-once semantics.
Presentation by Colin McCabe, Confluent, Big Data Day LA
Leveraging Microservice Architectures & Event-Driven Systems for Global APIsconfluent
Speaker: Ben Stopford, Technologist, Office of the CTO, Confluent
Are events the new API? Event driven systems provide some unique properties, particularly for microservice architectures, as they can be used both for notification as well as for state transfer. This lets systems run in a broad range of use cases that cross geographies, clouds and devices.
In this talk we will look at what event driven systems are; how they provide a unique contract for services to communicate and share data and how stream processing tools can be used to simplify the interaction between different services, be them closely coupled or largely disconnected.
Ben is a technologist working in the Office of the CTO at Confluent Inc (the company behind Apache Kafka®). He’s worked on a wide range of projects, from implementing the latest version of Kafka’s replication protocol through to developing strategies for streaming applications. Before Confluent Ben led the design and build of a company-wide data platform for a large investment bank. His earlier career spanned a variety of projects at ThoughtWorks and UK-based enterprise companies. He is the author of the book “Designing Event Driven Systems,” O’Reilly, 2018.
Watch the recording: https://videos.confluent.io/watch/8MLuNHnE3uSZPgstdzSk4Q?.
Bravo Six, Going Realtime. Transitioning Activision Data Pipeline to Streamin...HostedbyConfluent
Activision Data team has been running a data pipeline for a variety of Activision games for many years. Historically we used a mix of micro-batch microservices coupled with classic Big Data tools like Hadoop and Hive for ETL. As a result, it could take up to 4-6 hours for data to be available to the end customers.
In the last few years, the adoption of data in the organization skyrocketed. We needed to de-legacy our data pipeline and provide near-realtime access to data in order to improve reporting, gather insights faster, power web and mobile applications. I want to tell a story about heavily leveraging Kafka Streams and Kafka Connect to reduce the end latency to minutes, at the same time making the pipeline easier and cheaper to run. We were able to successfully validate the new data pipeline by launching two massive games just 4 weeks apart.
user Behavior Analysis with Session Windows and Apache Kafka's Streams APIconfluent
For many industries the need to group together related events based on a period of activity or inactivity is key. Advertising businesses, content producers are just a few examples of where session windows can be used to better understand user behavior.
While such sessionization has been possible in Apache Kafka up to this point, implementing it has been rather complex and required leveraging low-level APIs. In the most recent release of Kafka, however, new capabilities have been added making session windows much easier to implement.
In this online talk, we’ll introduce the concept of a session window, talk about common use cases, and walk through how Apache Kafka can be used for session-oriented use cases.
Real-Time Dynamic Data Export Using the Kafka Ecosystemconfluent
(Preston Thompson, Braze) Kafka Summit SF 2018
If you collect billions of data points every day and create billions more sending and tracking messages, then you know you need to get your infrastructure right. Our clients use Braze to engage their users over their lifecycle via push notifications, emails, in-app messages and more. Using our Currents product, clients can enable multiple configurable integrations to export this event data in real time to a variety of third-party systems, allowing them to tightly integrate with the rest of their operations and understand the impacts of their engagement strategy.
We use Kafka and the Kafka ecosystem to power this high volume real-time export. As you’d expect in a big data environment, we take data collected from a variety of sources—our SDKs, email partner APIs, our own systems—and produce it to Kafka, with topics for each type of event (about 30 types). Kafka Streams filters and transforms this data according to the configurations set by our clients. Clients can choose which types of events should be sent to which third-party systems. Kafka Connect helps to export the data to third-party systems in real time using custom developed connectors. We run a connector instance for each integration for each customer that consumes from the integration-specific topic. On top of it all, we built a service to manage the pipeline. The service provides configurations to the Streams application and also creates topics for new integrations and uses the Connect REST API to create and manage connectors.
In this talk, I will discuss:
-How we started our journey in designing this large-scale streaming architecture
-Why streaming technologies were necessary to solve our technology and business issues
-The lessons we learned along the way that can help you with your Kafka-based architecture
Building distributed systems is challenging. Luckily, Apache Kafka provides a powerful toolkit for putting together big services as a set of scalable, decoupled components. In this talk, I'll describe some of the design tradeoffs when building microservices, and how Kafka's powerful abstractions can help. I'll also talk a little bit about what the community has been up to with Kafka Streams, Kafka Connect, and exactly-once semantics.
Presentation by Colin McCabe, Confluent, Big Data Day LA
Leveraging Microservice Architectures & Event-Driven Systems for Global APIsconfluent
Speaker: Ben Stopford, Technologist, Office of the CTO, Confluent
Are events the new API? Event driven systems provide some unique properties, particularly for microservice architectures, as they can be used both for notification as well as for state transfer. This lets systems run in a broad range of use cases that cross geographies, clouds and devices.
In this talk we will look at what event driven systems are; how they provide a unique contract for services to communicate and share data and how stream processing tools can be used to simplify the interaction between different services, be them closely coupled or largely disconnected.
Ben is a technologist working in the Office of the CTO at Confluent Inc (the company behind Apache Kafka®). He’s worked on a wide range of projects, from implementing the latest version of Kafka’s replication protocol through to developing strategies for streaming applications. Before Confluent Ben led the design and build of a company-wide data platform for a large investment bank. His earlier career spanned a variety of projects at ThoughtWorks and UK-based enterprise companies. He is the author of the book “Designing Event Driven Systems,” O’Reilly, 2018.
Watch the recording: https://videos.confluent.io/watch/8MLuNHnE3uSZPgstdzSk4Q?.
Bravo Six, Going Realtime. Transitioning Activision Data Pipeline to Streamin...HostedbyConfluent
Activision Data team has been running a data pipeline for a variety of Activision games for many years. Historically we used a mix of micro-batch microservices coupled with classic Big Data tools like Hadoop and Hive for ETL. As a result, it could take up to 4-6 hours for data to be available to the end customers.
In the last few years, the adoption of data in the organization skyrocketed. We needed to de-legacy our data pipeline and provide near-realtime access to data in order to improve reporting, gather insights faster, power web and mobile applications. I want to tell a story about heavily leveraging Kafka Streams and Kafka Connect to reduce the end latency to minutes, at the same time making the pipeline easier and cheaper to run. We were able to successfully validate the new data pipeline by launching two massive games just 4 weeks apart.
user Behavior Analysis with Session Windows and Apache Kafka's Streams APIconfluent
For many industries the need to group together related events based on a period of activity or inactivity is key. Advertising businesses, content producers are just a few examples of where session windows can be used to better understand user behavior.
While such sessionization has been possible in Apache Kafka up to this point, implementing it has been rather complex and required leveraging low-level APIs. In the most recent release of Kafka, however, new capabilities have been added making session windows much easier to implement.
In this online talk, we’ll introduce the concept of a session window, talk about common use cases, and walk through how Apache Kafka can be used for session-oriented use cases.
Real-Time Dynamic Data Export Using the Kafka Ecosystemconfluent
(Preston Thompson, Braze) Kafka Summit SF 2018
If you collect billions of data points every day and create billions more sending and tracking messages, then you know you need to get your infrastructure right. Our clients use Braze to engage their users over their lifecycle via push notifications, emails, in-app messages and more. Using our Currents product, clients can enable multiple configurable integrations to export this event data in real time to a variety of third-party systems, allowing them to tightly integrate with the rest of their operations and understand the impacts of their engagement strategy.
We use Kafka and the Kafka ecosystem to power this high volume real-time export. As you’d expect in a big data environment, we take data collected from a variety of sources—our SDKs, email partner APIs, our own systems—and produce it to Kafka, with topics for each type of event (about 30 types). Kafka Streams filters and transforms this data according to the configurations set by our clients. Clients can choose which types of events should be sent to which third-party systems. Kafka Connect helps to export the data to third-party systems in real time using custom developed connectors. We run a connector instance for each integration for each customer that consumes from the integration-specific topic. On top of it all, we built a service to manage the pipeline. The service provides configurations to the Streams application and also creates topics for new integrations and uses the Connect REST API to create and manage connectors.
In this talk, I will discuss:
-How we started our journey in designing this large-scale streaming architecture
-Why streaming technologies were necessary to solve our technology and business issues
-The lessons we learned along the way that can help you with your Kafka-based architecture
The Data Dichotomy- Rethinking the Way We Treat Data and Servicesconfluent
Presenter: Ben Stopford, Engineer, Confluent
Services come with a problem: they’re not well suited to sharing data. This talk will examine the underlying dichotomy we all face as we piece such systems together. One that is not well served today. The solution lies in blending the old with the new and Apache Kafka plays a central role.
Enhancing Apache Kafka for Large Scale Real-Time Data Pipeline at Tencent | K...HostedbyConfluent
In this session we share our experience of building a real-time data pipelines at Tencent PCG - one that handles 20 trillion daily messages with 700 clusters and 100Gb/s bursting traffic from a single app. We discuss our roadmap of enhancing Kafka to break its limits in terms of scalability, robustness and cost of operation.
We first built a proxy layer that aggregates physical clusters in a way agnostic to the clients. While this architecture solves many operational problems, it requires significant development to stay future-proof. With retrospection with our customer and careful study of the ongoing work from the community, we then designed a region federation solution in the broker layer, which allows us to deploy clusters at a much larger scale than previously possible, while at the same time providing better failure recovery and operability. We discuss how we make this development compatible with KIP-500 and KIP-405, and the two KIP (693, 694) that we submitted for discussion.
How Zillow Unlocked Kafka to 50 Teams in 8 months | Shahar Cizer Kobrinsky, Z...HostedbyConfluent
As an AWS shop, Zillow engineering teams have been using various messaging and streaming services for years. As Zillow 2.0 piled through, new requirements and pain points made us rethink our streaming stack. The need for high data quality, decoupling producers & consumers and real time homes data called for a new platform which would empower developers, enable data governance and reduce incidents caused by bad data. In this session, you will learn why Zillow decided to go with Kafka for that platform, what tools we built to meet developers where they are and what common challenges you could face as you migrate other streaming solutions to Kafka.
Stream Processing Live Traffic Data with Kafka StreamsTom Van den Bulck
In this workshop we will set up a streaming framework which will process realtime data of traffic sensors installed within the Belgian road system.
Starting with the intake of the data, you will learn best practices and the recommended approach to split the information into events in a way that won't come back to haunt you.
With some basic stream operations (count, filter, ... ) you will get to know the data and experience how easy it is to get things done with Spring Boot & Spring Cloud Stream.
But since simple data processing is not enough to fulfill all your streaming needs, we will also let you experience the power of windows.
After this workshop, tumbling, sliding and session windows hold no more mysteries and you will be a true streaming wizard.
Building Retry Architectures in Kafka with Compacted Topics | Matthew Zhou, V...HostedbyConfluent
In this talk, we'll discuss how VillageMD is able to use Kafka topic compaction for rapidly scaling our reprocessing pipelines to encompass hundreds of feeds. Within healthcare data ecosystems, privacy and data minimalism are key design priorities. Being able to handle data deletion in a reliable, timely manner within event-driven architectures is becoming more and more necessary with key governance frameworks like the GDPR and HIPAA.
We'll be giving an overview of the building and governance of dead-letter queues for streaming data processing.
We'll discuss:
1. How to architect a data sink for failed records.
2. How topic compaction can reduce duplicate data and enable idempotency.
3. Building a tombstoning system for removing successfully reprocessed records from the queues.
4. Considerations for monitoring a reprocessing system in production -- what metrics, dataops, and SLAs are useful?
Capital One Delivers Risk Insights in Real Time with Stream Processingconfluent
Speakers: Ravi Dubey, Senior Manager, Software Engineering, Capital One + Jeff Sharpe, Software Engineer, Capital One
Capital One supports interactions with real-time streaming transactional data using Apache Kafka®. Kafka helps deliver information to internal operation teams and bank tellers to assist with assessing risk and protect customers in a myriad of ways.
Inside the bank, Kafka allows Capital One to build a real-time system that takes advantage of modern data and cloud technologies without exposing customers to unnecessary data breaches, or violating privacy regulations. These examples demonstrate how a streaming platform enables Capital One to act on their visions faster and in a more scalable way through the Kafka solution, helping establish Capital One as an innovator in the banking space.
Join us for this online talk on lessons learned, best practices and technical patterns of Capital One’s deployment of Apache Kafka.
-Find out how Kafka delivers on a 5-second service-level agreement (SLA) for inside branch tellers.
-Learn how to combine and host data in-memory and prevent personally identifiable information (PII) violations of in-flight transactions.
-Understand how Capital One manages Kafka Docker containers using Kubernetes.
Watch the recording: https://videos.confluent.io/watch/6e6ukQNnmASwkf9Gkdhh69?.
How did we move the mountain? - Migrating 1 trillion+ messages per day across...HostedbyConfluent
Have you ever migrated Kafka clusters from one data center to another being completely transparent to client applications?
At PayPal, as part of a massive datacenter migration initiative, Kafka team successfully moved all PayPal Kafka traffic across data centers. This initiative involved migrating 20+ Kafka clusters (1000+ broker and zookeeper nodes), as well as 60+ mirrormaker groups which seamlessly handle Kafka traffic volumes as high as 1 trillion messages per day. Throughout the course of this migration, applications required no modification, encountered 0% service outage, 0% message loss and duplicated messages. The whole migration process was fully transparent to Kafka applications.
In this session, you will learn the strategies, techniques and tools the PayPal Kafka team has utilized for managing the migration process. You will also learn the lessons and pitfalls they experienced during this exercise, as well as the secret sauce of making the migration successful.
How Yelp Leapt to Microservices with More than a Message Queueconfluent
Without seeing what’s wrong with today’s messaging queues, it can be initially confusing to view Apache Kafka as more. By adding additional functionality, true storage, and guarantees it opens opportunities to take full advantage of a publish/subscribe model.
Joined by Yelp’s Justin Cunningham we’ll see how their infrastructure has quickly evolved. Powered by Kafka, Yelp has made the leap to microservices and is seeing the benefits of efficiency and performance.
Speakers:
Justin Cunningham
Technical Lead, Software Engineer, Yelp
Gehrig Kunz
Technical Product Marketing Manager, Confluent
ETL as a Platform: Pandora Plays Nicely Everywhere with Real-Time Data Pipelinesconfluent
ETL can be painful with dirty data and outdated batch processes slowing you down; there has to be a better way. In this talk we’ll discuss the benefits of introducing a streaming platform to your architecture including how it can greatly simplify complexity, speed up performance, and help your team deliver the features they need with real-time data integration.
Pandora’s Lawrence Weikum will discuss what they’ve done to bring real-time data integration to the team. We’ll review their Kafka-powered data pipelines and how they make the most of Kafka’s Connect API to make it surprisingly system to keep systems in sync.
Presented by:
Lawrence Weikum, Senior Software Engineer, Pandora
Gehrig Kunz, Technical Product Marketing Manager, Confluent
Introduction to Apache Kafka and Confluent... and why they matterconfluent
Milano Apache Kafka Meetup by Confluent (First Italian Kafka Meetup) on Wednesday, November 29th 2017.
Il talk introduce Apache Kafka (incluse le APIs Kafka Connect e Kafka Streams), Confluent (la società creata dai creatori di Kafka) e spiega perché Kafka è un'ottima e semplice soluzione per la gestione di stream di dati nel contesto di due delle principali forze trainanti e trend industriali: Internet of Things (IoT) e Microservices.
A stream processing platform is not an island unto itself; it must be connected to all of your existing data systems, applications, and sources. In this talk we will provide different options for integrating systems and applications with Apache Kafka, with a focus on the Kafka Connect framework and the ecosystem of Kafka connectors. We will discuss the intended use cases for Kafka Connect and share our experience and best practices for building large-scale data pipelines using Apache Kafka.
Kafka for Real-Time Event Processing in Serverless Environmentsconfluent
(Jeff Sharpe + Alex Srisuwan, Capital One) Kafka Summit SF 2018
Using Kafka as a platform messaging bus is common, but bridging communication between real-time and asynchronous components can become complicated, especially when dealing with serverless environments. This has become increasingly common in modern banking where events need to be processed at near-real-time speed. Serverless environments are well-suited to address these needs, and Kafka remains an excellent solution for providing the reliable, resilient communication layer between serverless components and dedicated stream processing services.
In this talk, we will examine some of the strengths and weaknesses of using Kafka for real-time communication, some tips for efficient interactions with Kafka and AWS Lambda, and a number of useful patterns for maximizing the strengths of Kafka and serverless components.
What is Apache Kafka and What is an Event Streaming Platform?confluent
Speaker: Gabriel Schenker, Lead Curriculum Developer, Confluent
Streaming platforms have emerged as a popular, new trend, but what exactly is a streaming platform? Part messaging system, part Hadoop made fast, part fast ETL and scalable data integration. With Apache Kafka® at the core, event streaming platforms offer an entirely new perspective on managing the flow of data. This talk will explain what an event streaming platform such as Apache Kafka is and some of the use cases and design patterns around its use—including several examples of where it is solving real business problems. New developments in this area such as KSQL will also be discussed.
Streaming Transformations - Putting the T in Streaming ETLconfluent
Speaker: Nick Dearden, Director of Engineering, Confluent
We’ll discuss how to leverage some of the more advanced transformation capabilities available in both KSQL and Kafka Connect, including how to chain them together into powerful combinations for handling tasks such as data-masking, restructuring and aggregations. Using KSQL, you can deliver the streaming transformation capability easily and quickly.
This is part 3 of 3 in Streaming ETL - The New Data Integration series.
Watch the recording: https://videos.confluent.io/watch/en56Qt3KAdrpQ4JE5EZNHj?.
Kafka, Killer of Point-to-Point Integrations, Lucian Litaconfluent
With 60+ products and over 24% of the US GDP flowing through it, system integration is a tough problem for Intuit. Seasonality, scale, and massive peaks in products like TurboTax, QuickBooks, and Mint.com add extra layers of difficulty when building shared data services around transaction and user graphs, clickstream processing, a/b testing, and personalization. To reduce complexity and latency, we’ve implemented Kafka as the backbone across these data services. This allows us to asynchronously trigger relevant processing, elegantly scaling up and down as needed around peaks, all without the need for point-to-point integrations.
In this talk, we share what we’ve learned about Kafka at Intuit and describe our data services architecture. We found that Kafka is invaluable in achieving a scalable, clean architecture, allowing engineering teams to focus less on integration and more on product development.
Utilizing Kafka Connect to Integrate Classic Monoliths into Modern Microservi...HostedbyConfluent
Having started with classic monolith applications in the late 90s and adopting a new microservice architecture in 2015, our organization needed a convenient, reliable, and low-cost way to push changes back and forth between them. One that preferably utilized technology already on hand and could exchange information between multiple data stores.
In this session we will explore how Kafka Connect and its various connectors satisfied this need. We will review the two disparate tech stacks we needed to integrate, and the strategies and connectors we used to exchange information. Finally, we will cover some enhancements we made to our own processes including integrating Kafka Connect and its connectors into our CI/CD pipeline and writing tools to monitor connectors in our production environment.
Analyzing Petabyte Scale Financial Data with Apache Pinot and Apache Kafka | ...HostedbyConfluent
At Stripe, we operate a general ledger modeled as double-entry bookkeeping for all financial transactions. Warehousing such data is challenging due to its high volume and high cardinality of unique accounts.
aFurthermore, it is financially critical to get up-to-date, accurate analytics over all records. Due to the changing nature of real time transactions, it is impossible to pre-compute the analytics as a fixed time series. We have overcome the challenge by creating a real time key-value store inside Pinot that can sustain half million QPS with all the financial transactions.
We will talk about the details of our solution and the interesting technical challenges faced.
The Data Dichotomy- Rethinking the Way We Treat Data and Servicesconfluent
Presenter: Ben Stopford, Engineer, Confluent
Services come with a problem: they’re not well suited to sharing data. This talk will examine the underlying dichotomy we all face as we piece such systems together. One that is not well served today. The solution lies in blending the old with the new and Apache Kafka plays a central role.
Enhancing Apache Kafka for Large Scale Real-Time Data Pipeline at Tencent | K...HostedbyConfluent
In this session we share our experience of building a real-time data pipelines at Tencent PCG - one that handles 20 trillion daily messages with 700 clusters and 100Gb/s bursting traffic from a single app. We discuss our roadmap of enhancing Kafka to break its limits in terms of scalability, robustness and cost of operation.
We first built a proxy layer that aggregates physical clusters in a way agnostic to the clients. While this architecture solves many operational problems, it requires significant development to stay future-proof. With retrospection with our customer and careful study of the ongoing work from the community, we then designed a region federation solution in the broker layer, which allows us to deploy clusters at a much larger scale than previously possible, while at the same time providing better failure recovery and operability. We discuss how we make this development compatible with KIP-500 and KIP-405, and the two KIP (693, 694) that we submitted for discussion.
How Zillow Unlocked Kafka to 50 Teams in 8 months | Shahar Cizer Kobrinsky, Z...HostedbyConfluent
As an AWS shop, Zillow engineering teams have been using various messaging and streaming services for years. As Zillow 2.0 piled through, new requirements and pain points made us rethink our streaming stack. The need for high data quality, decoupling producers & consumers and real time homes data called for a new platform which would empower developers, enable data governance and reduce incidents caused by bad data. In this session, you will learn why Zillow decided to go with Kafka for that platform, what tools we built to meet developers where they are and what common challenges you could face as you migrate other streaming solutions to Kafka.
Stream Processing Live Traffic Data with Kafka StreamsTom Van den Bulck
In this workshop we will set up a streaming framework which will process realtime data of traffic sensors installed within the Belgian road system.
Starting with the intake of the data, you will learn best practices and the recommended approach to split the information into events in a way that won't come back to haunt you.
With some basic stream operations (count, filter, ... ) you will get to know the data and experience how easy it is to get things done with Spring Boot & Spring Cloud Stream.
But since simple data processing is not enough to fulfill all your streaming needs, we will also let you experience the power of windows.
After this workshop, tumbling, sliding and session windows hold no more mysteries and you will be a true streaming wizard.
Building Retry Architectures in Kafka with Compacted Topics | Matthew Zhou, V...HostedbyConfluent
In this talk, we'll discuss how VillageMD is able to use Kafka topic compaction for rapidly scaling our reprocessing pipelines to encompass hundreds of feeds. Within healthcare data ecosystems, privacy and data minimalism are key design priorities. Being able to handle data deletion in a reliable, timely manner within event-driven architectures is becoming more and more necessary with key governance frameworks like the GDPR and HIPAA.
We'll be giving an overview of the building and governance of dead-letter queues for streaming data processing.
We'll discuss:
1. How to architect a data sink for failed records.
2. How topic compaction can reduce duplicate data and enable idempotency.
3. Building a tombstoning system for removing successfully reprocessed records from the queues.
4. Considerations for monitoring a reprocessing system in production -- what metrics, dataops, and SLAs are useful?
Capital One Delivers Risk Insights in Real Time with Stream Processingconfluent
Speakers: Ravi Dubey, Senior Manager, Software Engineering, Capital One + Jeff Sharpe, Software Engineer, Capital One
Capital One supports interactions with real-time streaming transactional data using Apache Kafka®. Kafka helps deliver information to internal operation teams and bank tellers to assist with assessing risk and protect customers in a myriad of ways.
Inside the bank, Kafka allows Capital One to build a real-time system that takes advantage of modern data and cloud technologies without exposing customers to unnecessary data breaches, or violating privacy regulations. These examples demonstrate how a streaming platform enables Capital One to act on their visions faster and in a more scalable way through the Kafka solution, helping establish Capital One as an innovator in the banking space.
Join us for this online talk on lessons learned, best practices and technical patterns of Capital One’s deployment of Apache Kafka.
-Find out how Kafka delivers on a 5-second service-level agreement (SLA) for inside branch tellers.
-Learn how to combine and host data in-memory and prevent personally identifiable information (PII) violations of in-flight transactions.
-Understand how Capital One manages Kafka Docker containers using Kubernetes.
Watch the recording: https://videos.confluent.io/watch/6e6ukQNnmASwkf9Gkdhh69?.
How did we move the mountain? - Migrating 1 trillion+ messages per day across...HostedbyConfluent
Have you ever migrated Kafka clusters from one data center to another being completely transparent to client applications?
At PayPal, as part of a massive datacenter migration initiative, Kafka team successfully moved all PayPal Kafka traffic across data centers. This initiative involved migrating 20+ Kafka clusters (1000+ broker and zookeeper nodes), as well as 60+ mirrormaker groups which seamlessly handle Kafka traffic volumes as high as 1 trillion messages per day. Throughout the course of this migration, applications required no modification, encountered 0% service outage, 0% message loss and duplicated messages. The whole migration process was fully transparent to Kafka applications.
In this session, you will learn the strategies, techniques and tools the PayPal Kafka team has utilized for managing the migration process. You will also learn the lessons and pitfalls they experienced during this exercise, as well as the secret sauce of making the migration successful.
How Yelp Leapt to Microservices with More than a Message Queueconfluent
Without seeing what’s wrong with today’s messaging queues, it can be initially confusing to view Apache Kafka as more. By adding additional functionality, true storage, and guarantees it opens opportunities to take full advantage of a publish/subscribe model.
Joined by Yelp’s Justin Cunningham we’ll see how their infrastructure has quickly evolved. Powered by Kafka, Yelp has made the leap to microservices and is seeing the benefits of efficiency and performance.
Speakers:
Justin Cunningham
Technical Lead, Software Engineer, Yelp
Gehrig Kunz
Technical Product Marketing Manager, Confluent
ETL as a Platform: Pandora Plays Nicely Everywhere with Real-Time Data Pipelinesconfluent
ETL can be painful with dirty data and outdated batch processes slowing you down; there has to be a better way. In this talk we’ll discuss the benefits of introducing a streaming platform to your architecture including how it can greatly simplify complexity, speed up performance, and help your team deliver the features they need with real-time data integration.
Pandora’s Lawrence Weikum will discuss what they’ve done to bring real-time data integration to the team. We’ll review their Kafka-powered data pipelines and how they make the most of Kafka’s Connect API to make it surprisingly system to keep systems in sync.
Presented by:
Lawrence Weikum, Senior Software Engineer, Pandora
Gehrig Kunz, Technical Product Marketing Manager, Confluent
Introduction to Apache Kafka and Confluent... and why they matterconfluent
Milano Apache Kafka Meetup by Confluent (First Italian Kafka Meetup) on Wednesday, November 29th 2017.
Il talk introduce Apache Kafka (incluse le APIs Kafka Connect e Kafka Streams), Confluent (la società creata dai creatori di Kafka) e spiega perché Kafka è un'ottima e semplice soluzione per la gestione di stream di dati nel contesto di due delle principali forze trainanti e trend industriali: Internet of Things (IoT) e Microservices.
A stream processing platform is not an island unto itself; it must be connected to all of your existing data systems, applications, and sources. In this talk we will provide different options for integrating systems and applications with Apache Kafka, with a focus on the Kafka Connect framework and the ecosystem of Kafka connectors. We will discuss the intended use cases for Kafka Connect and share our experience and best practices for building large-scale data pipelines using Apache Kafka.
Kafka for Real-Time Event Processing in Serverless Environmentsconfluent
(Jeff Sharpe + Alex Srisuwan, Capital One) Kafka Summit SF 2018
Using Kafka as a platform messaging bus is common, but bridging communication between real-time and asynchronous components can become complicated, especially when dealing with serverless environments. This has become increasingly common in modern banking where events need to be processed at near-real-time speed. Serverless environments are well-suited to address these needs, and Kafka remains an excellent solution for providing the reliable, resilient communication layer between serverless components and dedicated stream processing services.
In this talk, we will examine some of the strengths and weaknesses of using Kafka for real-time communication, some tips for efficient interactions with Kafka and AWS Lambda, and a number of useful patterns for maximizing the strengths of Kafka and serverless components.
What is Apache Kafka and What is an Event Streaming Platform?confluent
Speaker: Gabriel Schenker, Lead Curriculum Developer, Confluent
Streaming platforms have emerged as a popular, new trend, but what exactly is a streaming platform? Part messaging system, part Hadoop made fast, part fast ETL and scalable data integration. With Apache Kafka® at the core, event streaming platforms offer an entirely new perspective on managing the flow of data. This talk will explain what an event streaming platform such as Apache Kafka is and some of the use cases and design patterns around its use—including several examples of where it is solving real business problems. New developments in this area such as KSQL will also be discussed.
Streaming Transformations - Putting the T in Streaming ETLconfluent
Speaker: Nick Dearden, Director of Engineering, Confluent
We’ll discuss how to leverage some of the more advanced transformation capabilities available in both KSQL and Kafka Connect, including how to chain them together into powerful combinations for handling tasks such as data-masking, restructuring and aggregations. Using KSQL, you can deliver the streaming transformation capability easily and quickly.
This is part 3 of 3 in Streaming ETL - The New Data Integration series.
Watch the recording: https://videos.confluent.io/watch/en56Qt3KAdrpQ4JE5EZNHj?.
Kafka, Killer of Point-to-Point Integrations, Lucian Litaconfluent
With 60+ products and over 24% of the US GDP flowing through it, system integration is a tough problem for Intuit. Seasonality, scale, and massive peaks in products like TurboTax, QuickBooks, and Mint.com add extra layers of difficulty when building shared data services around transaction and user graphs, clickstream processing, a/b testing, and personalization. To reduce complexity and latency, we’ve implemented Kafka as the backbone across these data services. This allows us to asynchronously trigger relevant processing, elegantly scaling up and down as needed around peaks, all without the need for point-to-point integrations.
In this talk, we share what we’ve learned about Kafka at Intuit and describe our data services architecture. We found that Kafka is invaluable in achieving a scalable, clean architecture, allowing engineering teams to focus less on integration and more on product development.
Utilizing Kafka Connect to Integrate Classic Monoliths into Modern Microservi...HostedbyConfluent
Having started with classic monolith applications in the late 90s and adopting a new microservice architecture in 2015, our organization needed a convenient, reliable, and low-cost way to push changes back and forth between them. One that preferably utilized technology already on hand and could exchange information between multiple data stores.
In this session we will explore how Kafka Connect and its various connectors satisfied this need. We will review the two disparate tech stacks we needed to integrate, and the strategies and connectors we used to exchange information. Finally, we will cover some enhancements we made to our own processes including integrating Kafka Connect and its connectors into our CI/CD pipeline and writing tools to monitor connectors in our production environment.
Analyzing Petabyte Scale Financial Data with Apache Pinot and Apache Kafka | ...HostedbyConfluent
At Stripe, we operate a general ledger modeled as double-entry bookkeeping for all financial transactions. Warehousing such data is challenging due to its high volume and high cardinality of unique accounts.
aFurthermore, it is financially critical to get up-to-date, accurate analytics over all records. Due to the changing nature of real time transactions, it is impossible to pre-compute the analytics as a fixed time series. We have overcome the challenge by creating a real time key-value store inside Pinot that can sustain half million QPS with all the financial transactions.
We will talk about the details of our solution and the interesting technical challenges faced.
Running Apache Kafka in production is only the first step in the Kafka operations journey. Professional Kafka users are ready to handle all possible disasters - because for most businesses having a disaster recovery plan is not optional.
In this session, we’ll discuss disaster scenarios that can take down entire Kafka clusters and share advice on how to plan, prepare and handle these events. This is a technical session full of best practices - we want to make sure you are ready to handle the worst mayhem that nature and auditors can cause.
Visit www.confluent.io for more information.
Common Patterns of Multi Data-Center Architectures with Apache Kafkaconfluent
Whether you know you want to run Apache Kafka in multiple data centers and need practical advice or you are wondering why some organizations even need more than one cluster, this online talk is for you.
In this short session, we’ll discuss the basic patterns of multi-datacenter Kafka architectures, explore some of the use-cases enabled by each architecture and show how Confluent Enterprise products make these patterns easy to implement.
Visit www.confluent.io for more information.
Apache Kafka's rise in popularity as a streaming platform has demanded a revisit of its traditional at-least-once message delivery semantics.
In this talk, we present the recent additions to Kafka to achieve exactly-once semantics (EoS) including support for idempotence and transactions in the Kafka clients. The main focus will be the specific semantics that Kafka distributed transactions enable and the underlying mechanics which allow them to scale efficiently.
Metrics Are Not Enough: Monitoring Apache Kafka and Streaming Applicationsconfluent
When you are running systems in production, clearly you want to make sure they are up and running at all times. But in a distributed system such as Apache Kafka… what does “up and running” even mean?
Experienced Apache Kafka users know what is important to monitor, which alerts are critical and how to respond to them. They don’t just collect metrics - they go the extra mile and use additional tools to validate availability and performance on both the Kafka cluster and their entire data pipelines.
In this presentation we’ll discuss best practices of monitoring Apache Kafka. We’ll look at which metrics are critical to alert on, which are useful in troubleshooting and what may actually be misleading. We’ll review a few “worst practices” - common mistakes that you should avoid. We’ll then look at what metrics don’t tell you - and how to cover those essential gaps.
Introducing Events and Stream Processing into Nationwide Building Society (Ro...confluent
Facing Open Banking regulation, rapidly increasing transaction volumes and increasing customer expectations, Nationwide took the decision to take load off their back-end systems through real-time streaming of data changes into Kafka. Hear about how Nationwide started their journey with Kafka, from their initial use case of creating a real-time data cache using Change Data Capture, Kafka and Microservices to how Kafka allowed them to build a stream processing backbone used to reengineer the entire banking experience including online banking, payment processing and mortgage applications. See a working demo of the system and what happens to the system when the underlying infrastructure breaks. Technologies covered include: Change Data Capture, Kafka (Avro, partitioning and replication) and using KSQL and Kafka Streams Framework to join topics and process data.
Real Time Insights for Advertising TechApache Apex
A leading Ad Tech company serves billions of advertising impressions and collects terabytes of data from several Data centers across the world. In batch data processing, data is collected at different geographic locations and processed at regular intervals. This system brings delay of at least 1 hour before an event is accounted for.
The goal of having real time streaming was to provide publishers, Demand Side Platforms (DSP's) and agencies actionable insights in a few minutes from the time of event generation.
This Ad Tech company uses DataTorrent RTS powered by Apex for:
• Real time reporting
• Resource monitoring
• Real time learning
• Allocation engine
Tushar Gosavi from DataTorrent will take the audience through the architecture, custom operators developed, use cases for real time and the challenges involved in implementing streaming systems at scale where multiple data centers are in play.
Tushar is a Senior Engineer at DataTorrent and has worked in distributed systems and storage domains.
Over 100 million subscribers from over 190 countries enjoy the Netflix service. This leads to over a trillion events, amounting to 3 PB, flowing through the Keystone infrastructure to help improve customer experience and glean business insights. The self-serve Keystone stream processing service processes these messages in near real-time with at-least once semantics in the cloud. This enables the users to focus on extracting insights, and not worry about building out scalable infrastructure. I’ll share the details about this platform, and our experience building it.
Real-Time Data Pipelines with Kafka, Spark, and Operational DatabasesSingleStore
Eric Frenkiel, MemSQL CEO and co-founder and Gartner Catalyst. August 11, 2015, San Diego, CA. Watch the Pinterest Demo Video here: https://youtu.be/KXelkQFVz4E
The presentation explains the reasons we picked Kafka as Streaming Hub and the use of Kafka Streams to avoid common anti-patterns, streamline development experience, improve resilience, enhance performances and enable experimentation. A step-by-step example will be presented to introduce the Kafka Streams DSL and understand what happens under the hood of a stateful streaming application.
10 Principals for Effective Event-Driven Microservices with Apache KafkaBen Stopford
This talk includes an introduction to the Kafka ecosystem as well as event-driven microserivces, culminating with 10 rules that help with the design of such systems:
1. Don’t use Kafka for shopping carts!
2. Pick Topics with Business Significance
3. Decouple publishers from subscribers
4. Use the log to regenerate state
5. Apply the Single Writer Principal
6. Leverage keeping datasets inside the broker
7. Prefer stream processing over maintaining historic views
8. Sometimes you need historic views. => Replicate Read Only
9. Use Schemas
10. Consider “Stream Management” Services
Building Event-Driven Services with Apache Kafkaconfluent
Should you use REST to sew services together? Is it better to use a richer, brokered protocol? This practical talk will dig into how we piece services together in event driven systems, how we we use a distributed log to create a central, persistent narrative and what benefits we reap from doing so.
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.
Ingestion and Dimensions Compute and Enrich using Apache ApexApache Apex
Presenter: Devendra Tagare - DataTorrent Engineer, Contributor to Apex, Data Architect experienced in building high scalability big data platforms.
This talk will be a deep dive into ingesting unbounded file data and streaming data from Kafka into Hadoop. We will also cover data enrichment and dimensional compute. Customer use-case and reference architecture.
Event Driven Services Part 2: Building Event-Driven Services with Apache KafkaBen Stopford
The second online talk in the Confluent Event Driven Services series covers how we build a shared narrative, using an event driven architecture, to conflate communication protocol and state transfer. We investigate how this leads to CQRS and discuss patterns for attaching Read-centric views to our canonical set of streams.
10 Principals for Effective Event Driven MicroservicesBen Stopford
This talk includes an introduction to the Kafka ecosystem as well as event-driven microserivces, culminating with 10 rules that help with the design of such systems:
1. Don’t use Kafka for shopping carts!
2. Pick Topics with Business Significance
3. Decouple publishers from subscribers
4. Use the log to regenerate state
5. Apply the Single Writer Principal
6. Leverage keeping datasets inside the broker
7. Prefer stream processing over maintaining historic views
8. Sometimes you need historic views. => Replicate Read Only
9. Use Schemas
10. Consider “Stream Management” Services
SingleStore & Kafka: Better Together to Power Modern Real-Time Data Architect...HostedbyConfluent
To remain competitive, organizations need to democratize access to fast analytics, not only to gain real-time insights on their business but also to power smart apps that need to react in the moment. In this session, you will learn how Kafka and SingleStore enable modern, yet simple data architecture to analyze both fast paced incoming data as well as large historical datasets. In particular, you will understand why SingleStore is well suited process data streams coming from Kafka.
Kafka and event driven architecture -apacoug20Vinay Kumar
Event-driven architecture in APIs and microservice are very important topics if you are developing modern applications with new technology, platforms. This session explains what is Kafka and how we can use in event-driven architecture. This session explains the basic concepts of publisher, subscriber, streams, and connect. Explain how Kafka works. The session covers developing different functions with different programming languages and shows how they can share messages by using Kafka. What are the options we have in Oracle stack? Which tool make it possible event-driven architecture in Oracle stack. Speaker will also explain Oracle Event HUB, OCI streaming, and Oracle AQ implementation.
(Joseph deBuzna + Zulfikar Quereshi, HVR) Kafka Summit SF 2018
This presentation is a customer story about France-based regional airline HOP! and their need to make better use of data that was contained in various applications. They also needed this information to be available in real time. As one can imagine, airlines manage a wide variety of information such as weather, customer information, flight plans, sensor data from planes and much more.
In this presentation, Joe will discuss how HOP! was delivering their data before and the limitations associated with delivering this data. Joe will then talk about HOP!’s selection of Kafka and HVR as a solution to enabling data availability and real-time information for analysis and action.
In this session, attendees will learn:
-How Kafka was selected and chosen as a solution for HOP!’s complex challenges
-Architecture and capabilities implemented that enabled data feeding from multiple sources to Kafka
-Considerations and challenges with this approach
-Business results and future plans
Thomas Weise, Apache Apex PMC Member and Architect/Co-Founder, DataTorrent - ...Dataconomy Media
Thomas Weise, Apache Apex PMC Member and Architect/Co-Founder of DataTorrent presented "Streaming Analytics with Apache Apex" as part of the Big Data, Berlin v 8.0 meetup organised on the 14th of July 2016 at the WeWork headquarters.
Similar to Putting the Micro into Microservices with Stateful Stream Processing (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.
Enhancing Research Orchestration Capabilities at ORNL.pdfGlobus
Cross-facility research orchestration comes with ever-changing constraints regarding the availability and suitability of various compute and data resources. In short, a flexible data and processing fabric is needed to enable the dynamic redirection of data and compute tasks throughout the lifecycle of an experiment. In this talk, we illustrate how we easily leveraged Globus services to instrument the ACE research testbed at the Oak Ridge Leadership Computing Facility with flexible data and task orchestration capabilities.
Multiple Your Crypto Portfolio with the Innovative Features of Advanced Crypt...Hivelance Technology
Cryptocurrency trading bots are computer programs designed to automate buying, selling, and managing cryptocurrency transactions. These bots utilize advanced algorithms and machine learning techniques to analyze market data, identify trading opportunities, and execute trades on behalf of their users. By automating the decision-making process, crypto trading bots can react to market changes faster than human traders
Hivelance, a leading provider of cryptocurrency trading bot development services, stands out as the premier choice for crypto traders and developers. Hivelance boasts a team of seasoned cryptocurrency experts and software engineers who deeply understand the crypto market and the latest trends in automated trading, Hivelance leverages the latest technologies and tools in the industry, including advanced AI and machine learning algorithms, to create highly efficient and adaptable crypto trading bots
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.
Your Digital Assistant.
Making complex approach simple. Straightforward process saves time. No more waiting to connect with people that matter to you. Safety first is not a cliché - Securely protect information in cloud storage to prevent any third party from accessing data.
Would you rather make your visitors feel burdened by making them wait? Or choose VizMan for a stress-free experience? VizMan is an automated visitor management system that works for any industries not limited to factories, societies, government institutes, and warehouses. A new age contactless way of logging information of visitors, employees, packages, and vehicles. VizMan is a digital logbook so it deters unnecessary use of paper or space since there is no requirement of bundles of registers that is left to collect dust in a corner of a room. Visitor’s essential details, helps in scheduling meetings for visitors and employees, and assists in supervising the attendance of the employees. With VizMan, visitors don’t need to wait for hours in long queues. VizMan handles visitors with the value they deserve because we know time is important to you.
Feasible Features
One Subscription, Four Modules – Admin, Employee, Receptionist, and Gatekeeper ensures confidentiality and prevents data from being manipulated
User Friendly – can be easily used on Android, iOS, and Web Interface
Multiple Accessibility – Log in through any device from any place at any time
One app for all industries – a Visitor Management System that works for any organisation.
Stress-free Sign-up
Visitor is registered and checked-in by the Receptionist
Host gets a notification, where they opt to Approve the meeting
Host notifies the Receptionist of the end of the meeting
Visitor is checked-out by the Receptionist
Host enters notes and remarks of the meeting
Customizable Components
Scheduling Meetings – Host can invite visitors for meetings and also approve, reject and reschedule meetings
Single/Bulk invites – Invitations can be sent individually to a visitor or collectively to many visitors
VIP Visitors – Additional security of data for VIP visitors to avoid misuse of information
Courier Management – Keeps a check on deliveries like commodities being delivered in and out of establishments
Alerts & Notifications – Get notified on SMS, email, and application
Parking Management – Manage availability of parking space
Individual log-in – Every user has their own log-in id
Visitor/Meeting Analytics – Evaluate notes and remarks of the meeting stored in the system
Visitor Management System is a secure and user friendly database manager that records, filters, tracks the visitors to your organization.
"Secure Your Premises with VizMan (VMS) – Get It Now"
Innovating Inference - Remote Triggering of Large Language Models on HPC Clus...Globus
Large Language Models (LLMs) are currently the center of attention in the tech world, particularly for their potential to advance research. In this presentation, we'll explore a straightforward and effective method for quickly initiating inference runs on supercomputers using the vLLM tool with Globus Compute, specifically on the Polaris system at ALCF. We'll begin by briefly discussing the popularity and applications of LLMs in various fields. Following this, we will introduce the vLLM tool, and explain how it integrates with Globus Compute to efficiently manage LLM operations on Polaris. Attendees will learn the practical aspects of setting up and remotely triggering LLMs from local machines, focusing on ease of use and efficiency. This talk is ideal for researchers and practitioners looking to leverage the power of LLMs in their work, offering a clear guide to harnessing supercomputing resources for quick and effective LLM inference.
Experience our free, in-depth three-part Tendenci Platform Corporate Membership Management workshop series! In Session 1 on May 14th, 2024, we began with an Introduction and Setup, mastering the configuration of your Corporate Membership Module settings to establish membership types, applications, and more. Then, on May 16th, 2024, in Session 2, we focused on binding individual members to a Corporate Membership and Corporate Reps, teaching you how to add individual members and assign Corporate Representatives to manage dues, renewals, and associated members. Finally, on May 28th, 2024, in Session 3, we covered questions and concerns, addressing any queries or issues you may have.
For more Tendenci AMS events, check out www.tendenci.com/events
A Comprehensive Look at Generative AI in Retail App Testing.pdfkalichargn70th171
Traditional software testing methods are being challenged in retail, where customer expectations and technological advancements continually shape the landscape. Enter generative AI—a transformative subset of artificial intelligence technologies poised to revolutionize software testing.
Designing for Privacy in Amazon Web ServicesKrzysztofKkol1
Data privacy is one of the most critical issues that businesses face. This presentation shares insights on the principles and best practices for ensuring the resilience and security of your workload.
Drawing on a real-life project from the HR industry, the various challenges will be demonstrated: data protection, self-healing, business continuity, security, and transparency of data processing. This systematized approach allowed to create a secure AWS cloud infrastructure that not only met strict compliance rules but also exceeded the client's expectations.
How Does XfilesPro Ensure Security While Sharing Documents in Salesforce?XfilesPro
Worried about document security while sharing them in Salesforce? Fret no more! Here are the top-notch security standards XfilesPro upholds to ensure strong security for your Salesforce documents while sharing with internal or external people.
To learn more, read the blog: https://www.xfilespro.com/how-does-xfilespro-make-document-sharing-secure-and-seamless-in-salesforce/
Gamify Your Mind; The Secret Sauce to Delivering Success, Continuously Improv...Shahin Sheidaei
Games are powerful teaching tools, fostering hands-on engagement and fun. But they require careful consideration to succeed. Join me to explore factors in running and selecting games, ensuring they serve as effective teaching tools. Learn to maintain focus on learning objectives while playing, and how to measure the ROI of gaming in education. Discover strategies for pitching gaming to leadership. This session offers insights, tips, and examples for coaches, team leads, and enterprise leaders seeking to teach from simple to complex concepts.
SOCRadar Research Team: Latest Activities of IntelBrokerSOCRadar
The European Union Agency for Law Enforcement Cooperation (Europol) has suffered an alleged data breach after a notorious threat actor claimed to have exfiltrated data from its systems. Infamous data leaker IntelBroker posted on the even more infamous BreachForums hacking forum, saying that Europol suffered a data breach this month.
The alleged breach affected Europol agencies CCSE, EC3, Europol Platform for Experts, Law Enforcement Forum, and SIRIUS. Infiltration of these entities can disrupt ongoing investigations and compromise sensitive intelligence shared among international law enforcement agencies.
However, this is neither the first nor the last activity of IntekBroker. We have compiled for you what happened in the last few days. To track such hacker activities on dark web sources like hacker forums, private Telegram channels, and other hidden platforms where cyber threats often originate, you can check SOCRadar’s Dark Web News.
Stay Informed on Threat Actors’ Activity on the Dark Web with SOCRadar!
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.
Code reviews are vital for ensuring good code quality. They serve as one of our last lines of defense against bugs and subpar code reaching production.
Yet, they often turn into annoying tasks riddled with frustration, hostility, unclear feedback and lack of standards. How can we improve this crucial process?
In this session we will cover:
- The Art of Effective Code Reviews
- Streamlining the Review Process
- Elevating Reviews with Automated Tools
By the end of this presentation, you'll have the knowledge on how to organize and improve your code review proces
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.
We describe the deployment and use of Globus Compute for remote computation. This content is aimed at researchers who wish to compute on remote resources using a unified programming interface, as well as system administrators who will deploy and operate Globus Compute services on their research computing infrastructure.
How to Position Your Globus Data Portal for Success Ten Good PracticesGlobus
Science gateways allow science and engineering communities to access shared data, software, computing services, and instruments. Science gateways have gained a lot of traction in the last twenty years, as evidenced by projects such as the Science Gateways Community Institute (SGCI) and the Center of Excellence on Science Gateways (SGX3) in the US, The Australian Research Data Commons (ARDC) and its platforms in Australia, and the projects around Virtual Research Environments in Europe. A few mature frameworks have evolved with their different strengths and foci and have been taken up by a larger community such as the Globus Data Portal, Hubzero, Tapis, and Galaxy. However, even when gateways are built on successful frameworks, they continue to face the challenges of ongoing maintenance costs and how to meet the ever-expanding needs of the community they serve with enhanced features. It is not uncommon that gateways with compelling use cases are nonetheless unable to get past the prototype phase and become a full production service, or if they do, they don't survive more than a couple of years. While there is no guaranteed pathway to success, it seems likely that for any gateway there is a need for a strong community and/or solid funding streams to create and sustain its success. With over twenty years of examples to draw from, this presentation goes into detail for ten factors common to successful and enduring gateways that effectively serve as best practices for any new or developing gateway.
9. 9
What is this talk about?
Using SSP to build lightweight services that
evolve the shared narrative
1. What’s in the SSP toolbox?
2. Patterns for Streaming Services
3. Sewing together the Big & the Small
4. The tricky parts
11. 11
Aside: What is Stateful Stream
Processing?
A machine for converting a set of
streams into something more useful.
Output a stream
or output a table
14. 14
Kafka
Producer
(1) Producer/Consumer APIs
Consumer
• Simple & easy to
understand
• Native in many
languages
• Easy to integrate to
existing services
• No joins, predicates,
groupings or tables
• Includes transactions
in Kafka-11.
15. 15
(2) KStreams DSL
• Joins (including
windowing)
• Predicates
• Groupings
• Processor API
Kafka
KTable
Inbound
Stream
(Windowed)
State Store Outbound Stream
Changelog
Stream
DSL
16. 16
(3) The KTable
• Recast a stream as a
table via a defined key
• Tends to be backed
by compacted topic.
• Materialized locally in
RocksDB
• Provides guaranteed
join semantics (i.e. no
windowing concerns)
Kafka
KTable
(in RocksDB)
DSL
JVM boundary
17. 17
The KTable
• Behaves like a regular stream but
retains the latest value for each key,
from offset 0
• Partitions spread over all available
instances
• Partition counts must match for joins
• Triggers processing
• Initializes lazily.
18. 18
(4) Global KTable
• All partitions replicated
to all nodes
• Supports N-way join
• Blocks until initialized
• Doesn’t trigger
processing (reference
resource)
Kafka
Global KTable
(in RocksDB)
DSL
19. 19
Global-KTables vs. KTables
P1,3 P6,8 P2,7 P4,5
P1,2,
3,4,5,
6,7,8
P1,2,
3,4,5,
6,7,8
P1,2,
3,4,5,
6,7,8
P1,2,
3,4,5,
6,7,8
KTable
(Sharded)
Global KTable
(Replicated)
Given a service with four instances:
20. 20
(5) State Store
• RocksDB instance
• Mutable
• Backed by Kafka
• Integrated into DSL
• Can be instantiated
independently
• Queryable via
Queryable state
• Useful for custom
views or stateful
services.Kafka
State Store
(RocksDB)
Changelog
Stream
DSL
21. 21
(6) Hot Replicas
- By default KStreams
keeps state backed up on
a secondary node.
- Single machine failure
doesn’t result in a re-
hydration pause.
=> Highly available, stateful
services.
Service instance 1 Service instance 2
22. 22
(7) Transactions
Two problems:
1. Failures create duplicates
• Each message gets a unique ID
• Deduplicate on write
2. You need to write, atomically, to 2
or more topics
• Chandy-Lamport Algorithm
NB: Transactions will be in the next Kafka release
25. 25
State-Stores & Transactions
• In KStreams Transactions
span stream and
changelog
• Processing of stream &
state is atomic
• Example: counting orders.
Kafka
State Store Stream
DSL
Atomic Transaction
Changelog
Stream
26. 26
All these tools scale horizontally and avoid
single points of failure
Sources Kafka KafkaService Layer Service Layer Kafka
29. 29
(2) Stateless, Data Enabled Processor
Similar to star schema
• Facts are stream
• Dimensions are GKTables
e.g. Enrich Orders with Customer Info & Account details
Stream
GKTable
GKTable
Stream-Table
Join
30. 30
(3) Gates
e.g. rules engines or event correlation
When Order & Payment then …
Stream 1
Window
Window Stream-Stream
Join
Stream 2
31. 31
(4) Stateful Processor
e.g.
- Average order amount by region
- Posting Engine (reverse replace of previous position)
State store
backed up to Kafka
38. 38
Event Driven First
• Build on an event driven backbone
• Add Request-Driven interfaces where
needed.
• Prefer intra-context
REST
Kafka
39. 39
Putting the Micro into Microservices
- Combine simple services that develop
the shared narrative
- Start stateless, make stateful if
necessary
40. 40
Contexts within Contexts
Where services are
tiny, they will typically
be grouped together.
• Shared domain?
• Shared deployment?
• Shared streams?
Several small
services
Single bounded
context
41. 41
Layering within multi-service contexts
Kafka
Data layer:
-Stream enrichment
-View creation
-Streams maintenance
Business layer
encapsulates logic,
integrates with legacy
code etc.
Request-Driven layer
provides REST / RPC /
websotcket etc for UIs
etc
42. 42
Kafka
Different levels of data
visibility within Kafka
Kafka
Contexts within Contexts
Accounting
Fulfillment
Online Services
47. 47
Part 1: Data dichotomy
Request-Driven Services don’t handle
shared data well
- God service problem
- The data erosion problem
Request-Driven Services struggle with
complex flows
- the service entanglement problem
- the batch processing problem.
48. 48
Part 2: Building Event Driven Services
Solution is to part 1:
- Event source facts into a Cascading
Narrative
- Communication & State transfer
converge
- Decentralize the role of turning facts
into queryable views
- Keep these views as lightweight and
disposable as possible.
49. 49
Putting the Micro in Microservices
- Start lightweight, stateless functions
which compose and evolve
- Leverage the stateful stream processing
toolkit to blend streams, state, views,
transactions.
- Use layered contexts that segregate
groups of streams and services.
51. 51
All order logic,
stateless service
Orders-by-Customer view
(KStreams + Queryable State API)
UI Service
Stream
Maintenance &
Monitoring
Highly available, stateful service
UI uses Orders-by-
Customer View directly
via Queryable State
History Service pushes data to
a local Elastic Search Instance
Orders
Service
Derived
View
UI joins data
Tables & Streams
Fulfillment Service
Analytics
OrdersProduct Custo-
mers KTables
KTables KTables
Schemas
52. 52
Tips & Tricky parts
• Reasoning about service choreography
• Debugging multi-stage topologies
• Monitoring/debug via the narrative
• Segregate facts from intermediary streams
• Retaining data comes at a cost
• Schema migration etc
• Accuracy over non trivial topologies
• Often requires transactions
• Transactions, causality & the flow of events
• Avoid side effects
54. 54
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