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Presented by Ralf Gebhardt at the MariaDB Roadshow Germany: 4.7.2014 in Hamburg, 8.7.2014 in Berlin and 11.7.2014 in Frankfurt.
Kafka error handling patterns and best practices | Hemant Desale and Aruna Ka...HostedbyConfluent
Transaction Banking from Goldman Sachs is a high volume, latency sensitive digital banking platform offering. We have chosen an event driven architecture to build highly decoupled and independent microservices in a cloud native manner and are designed to meet the objectives of Security, Availability Latency and Scalability. Kafka was a natural choice – to decouple producers and consumers and to scale easily for high volume processing. However, there are certain aspects that require careful consideration – handling errors and partial failures, managing downtime of consumers, secure communication between brokers and producers / consumers. In this session, we will present the patterns and best practices that helped us build robust event driven applications. We will also present our solution approach that has been reused across multiple application domains. We hope that by sharing our experience, we can establish a reference implementation that application developers can benefit from.
The first presentation for Kafka Meetup @ Linkedin (Bangalore) held on 2015/12/5
It provides a brief introduction to the motivation for building Kafka and how it works from a high level.
Please download the presentation if you wish to see the animated slides.
Kafka error handling patterns and best practices | Hemant Desale and Aruna Ka...HostedbyConfluent
Transaction Banking from Goldman Sachs is a high volume, latency sensitive digital banking platform offering. We have chosen an event driven architecture to build highly decoupled and independent microservices in a cloud native manner and are designed to meet the objectives of Security, Availability Latency and Scalability. Kafka was a natural choice – to decouple producers and consumers and to scale easily for high volume processing. However, there are certain aspects that require careful consideration – handling errors and partial failures, managing downtime of consumers, secure communication between brokers and producers / consumers. In this session, we will present the patterns and best practices that helped us build robust event driven applications. We will also present our solution approach that has been reused across multiple application domains. We hope that by sharing our experience, we can establish a reference implementation that application developers can benefit from.
The first presentation for Kafka Meetup @ Linkedin (Bangalore) held on 2015/12/5
It provides a brief introduction to the motivation for building Kafka and how it works from a high level.
Please download the presentation if you wish to see the animated slides.
Building High-Throughput, Low-Latency Pipelines in Kafkaconfluent
William Hill is one of the UK’s largest, most well-established gaming companies with a global presence across 9 countries with over 16,000 employees. In recent years the gaming industry and in particular sports betting, has been revolutionised by technology. Customers now demand a wide range of events and markets to bet on both pre-game and in-play 24/7. This has driven out a business need to process more data, provide more updates and offer more markets and prices in real time.
At William Hill, we have invested in a completely new trading platform using Apache Kafka. We process vast quantities of data from a variety of feeds, this data is fed through a variety of odds compilation models, before being piped out to UI apps for use by our trading teams to provide events, markets and pricing data out to various end points across the whole of William Hill. We deal with thousands of sporting events, each with sometimes hundreds of betting markets, each market receiving hundreds of updates. This scales up to vast numbers of messages flowing through our system. We have to process, transform and route that data in real time. Using Apache Kafka, we have built a high throughput, low latency pipeline, based on Cloud hosted Microservices. When we started, we were on a steep learning curve with Kafka, Microservices and associated technologies. This led to fast learnings and fast failings.
In this session, we will tell the story of what we built, what went well, what didn’t go so well and what we learnt. This is a story of how a team of developers learnt (and are still learning) how to use Kafka. We hope that you will be able to take away lessons and learnings of how to build a data processing pipeline with Apache Kafka.
Webinar | Better Together: Apache Cassandra and Apache KafkaDataStax
In this webinar, you’ll also be introduced to DataStax Apache Kafka Connector, and get a brief demonstration of this groundbreaking technology. You’ll directly experience how this tool can help you stream data from Kafka topics into DataStax Enterprise versions of Cassandra. The future of your organization won’t wait. Register now to reserve your spot in this exciting new webinar.
Youtube: https://youtu.be/HmkNb8twUNk
Real time Messages at Scale with Apache Kafka and CouchbaseWill Gardella
Kafka is a scalable, distributed publish subscribe messaging system that's used as a data transmission backbone in many data intensive digital businesses. Couchbase Server is a scalable, flexible document database that's fast, agile, and elastic. Because they both appeal to the same type of customers, Couchbase and Kafka are often used together.
This presentation from a meetup in Mountain View describes Kafka's design and why people use it, Couchbase Server and its uses, and the use cases for both together. Also covered is a description and demo of Couchbase Server writing documents to a Kafka topic and consuming messages from a Kafka topic. using the Couchbase Kafka Connector.
This is the slide deck which was used for a talk 'Change Data Capture using Kafka' at Kafka Meetup at Linkedin (Bangalore) held on 11th June 2016.
The talk describes the need for CDC and why it's a good use case for Kafka.
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.
Tales from the four-comma club: Managing Kafka as a service at Salesforce | L...HostedbyConfluent
Apache Kafka is a key part of the Big Data infrastructure at Salesforce, enabling publish/subscribe and data transport in near real-time at enterprise scale handling trillions of messages per day. In this session, hear from the teams at Salesforce that manage Kafka as a service, running over a hundred clusters across on-premise and public cloud environments with over 99.9% availability. Hear about best practices and innovations, including:
* How to manage multi-tenant clusters in a hybrid environment
* High volume data pipelines with Mirus replicating data to Kafka and blob storage
* Kafka Fault Injection Framework built on Trogdor and Kibosh
* Automated recovery without data loss
* Using Envoy as an SNI-routing Kafka gateway
We hope the audience will have practical takeaways for building, deploying, operating, and managing Kafka at scale in the enterprise.
Watch this talk here: https://www.confluent.io/online-talks/how-apache-kafka-works-on-demand
Pick up best practices for developing applications that use Apache Kafka, beginning with a high level code overview for a basic producer and consumer. From there we’ll cover strategies for building powerful stream processing applications, including high availability through replication, data retention policies, producer design and producer guarantees.
We’ll delve into the details of delivery guarantees, including exactly-once semantics, partition strategies and consumer group rebalances. The talk will finish with a discussion of compacted topics, troubleshooting strategies and a security overview.
This session is part 3 of 4 in our Fundamentals for Apache Kafka series.
My team at Zalando fell in love with KStreams and their programming model straight out of the gate. However, as a small team of developers, building out and supporting our infrastructure while still trying to deliver solutions for our business has not always resulted in a smooth journey. Can a small team of a couple of developers run their own Kafka infrastructure confidently and still spend most of their time developing code? In this talk, we will dive into some of the problems we experienced while running Kafka brokers and Kafka streams applications, as well as the consultations we had with other teams around this matter. We will outline some of the pragmatic decisions we made regarding backups, monitoring and operations to minimize our time spent administering our Kafka brokers and various stream applications.
Understanding Kafka Produce and Fetch api calls for high throughtput applicat...HostedbyConfluent
The data team at Cloudflare uses Kafka to process tens of petabytes a day. All this data is moved using the 2 foundational Kafka api calls: Produce (api key 0) and Fetch (api key 1). Understanding the structure of these calls (and of the underlying RecordSet structure) is key to building high throughput clients.
The talk describes the basics of the Kafka wire protocol (api keys, correlation id), and the structure of the Produce and Fetch calls. It shows how the asynchronous nature of the wire protocol can combine with the structure of the Produce and Fetch calls to increase latency and reduce client throughput; a solution is offered through use of synchronous single-partition calls.
The RecordSet structure, which is used to encode and store sets (batches) of records is described, and its implications on Fetch requests are discussed. The relationship between Fetch api calls and ""consume"" operations is discussed, as is the impact of offset alignment to RecordSet boundaries.
Sharing is Caring: Toward Creating Self-tuning Multi-tenant Kafka (Anna Povzn...HostedbyConfluent
Deploying Kafka to support multiple teams or even an entire company has many benefits. It reduces operational costs, simplifies onboarding of new applications as your adoption grows, and consolidates all your data in one place. However, this makes applications sharing the cluster vulnerable to any one or few of them taking all cluster resources. The combined cluster load also becomes less predictable, increasing the risk of overloading the cluster and data unavailability.
In this talk, we will describe how to use quota framework in Apache Kafka to ensure that a misconfigured client or unexpected increase in client load does not monopolize broker resources. You will get a deeper understanding of bandwidth and request quotas, how they get enforced, and gain intuition for setting the limits for your use-cases.
While quotas limit individual applications, there must be enough cluster capacity to support the combined application load. Onboarding new applications or scaling the usage of existing applications may require manual quota adjustments and upfront capacity planning to ensure high availability.
We will describe the steps we took toward solving this problem in Confluent Cloud, where we must immediately support unpredictable load with high availability. We implemented a custom broker quota plugin (KIP-257) to replace static per broker quota allocation with dynamic and self-tuning quotas based on the available capacity (which we also detect dynamically). By learning our journey, you will have more insights into the relevant problems and techniques to address them.
Hello, kafka! (an introduction to apache kafka)Timothy Spann
Hello ApacheKafka
An Introduction to Apache Kafka with Timothy Spann and Carolyn Duby Cloudera Principal engineers.
We also demo Flink SQL, SMM, SSB, Schema Registry, Apache Kafka, Apache NiFi and Public Cloud - AWS.
Automatisierung & Verwaltung von Datenbank - Clustern mit Severalnines - Mari...MariaDB Corporation
Automatisierung & Verwaltung von Datenbank - Clustern mit Severalnines
Presented by Jean-Jérôme Schmidt 8.7.2014 at the MariaDB Roadshow in Berlin, Germany.
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At William Hill, we have invested in a completely new trading platform using Apache Kafka. We process vast quantities of data from a variety of feeds, this data is fed through a variety of odds compilation models, before being piped out to UI apps for use by our trading teams to provide events, markets and pricing data out to various end points across the whole of William Hill. We deal with thousands of sporting events, each with sometimes hundreds of betting markets, each market receiving hundreds of updates. This scales up to vast numbers of messages flowing through our system. We have to process, transform and route that data in real time. Using Apache Kafka, we have built a high throughput, low latency pipeline, based on Cloud hosted Microservices. When we started, we were on a steep learning curve with Kafka, Microservices and associated technologies. This led to fast learnings and fast failings.
In this session, we will tell the story of what we built, what went well, what didn’t go so well and what we learnt. This is a story of how a team of developers learnt (and are still learning) how to use Kafka. We hope that you will be able to take away lessons and learnings of how to build a data processing pipeline with Apache Kafka.
Webinar | Better Together: Apache Cassandra and Apache KafkaDataStax
In this webinar, you’ll also be introduced to DataStax Apache Kafka Connector, and get a brief demonstration of this groundbreaking technology. You’ll directly experience how this tool can help you stream data from Kafka topics into DataStax Enterprise versions of Cassandra. The future of your organization won’t wait. Register now to reserve your spot in this exciting new webinar.
Youtube: https://youtu.be/HmkNb8twUNk
Real time Messages at Scale with Apache Kafka and CouchbaseWill Gardella
Kafka is a scalable, distributed publish subscribe messaging system that's used as a data transmission backbone in many data intensive digital businesses. Couchbase Server is a scalable, flexible document database that's fast, agile, and elastic. Because they both appeal to the same type of customers, Couchbase and Kafka are often used together.
This presentation from a meetup in Mountain View describes Kafka's design and why people use it, Couchbase Server and its uses, and the use cases for both together. Also covered is a description and demo of Couchbase Server writing documents to a Kafka topic and consuming messages from a Kafka topic. using the Couchbase Kafka Connector.
This is the slide deck which was used for a talk 'Change Data Capture using Kafka' at Kafka Meetup at Linkedin (Bangalore) held on 11th June 2016.
The talk describes the need for CDC and why it's a good use case for Kafka.
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Tales from the four-comma club: Managing Kafka as a service at Salesforce | L...HostedbyConfluent
Apache Kafka is a key part of the Big Data infrastructure at Salesforce, enabling publish/subscribe and data transport in near real-time at enterprise scale handling trillions of messages per day. In this session, hear from the teams at Salesforce that manage Kafka as a service, running over a hundred clusters across on-premise and public cloud environments with over 99.9% availability. Hear about best practices and innovations, including:
* How to manage multi-tenant clusters in a hybrid environment
* High volume data pipelines with Mirus replicating data to Kafka and blob storage
* Kafka Fault Injection Framework built on Trogdor and Kibosh
* Automated recovery without data loss
* Using Envoy as an SNI-routing Kafka gateway
We hope the audience will have practical takeaways for building, deploying, operating, and managing Kafka at scale in the enterprise.
Watch this talk here: https://www.confluent.io/online-talks/how-apache-kafka-works-on-demand
Pick up best practices for developing applications that use Apache Kafka, beginning with a high level code overview for a basic producer and consumer. From there we’ll cover strategies for building powerful stream processing applications, including high availability through replication, data retention policies, producer design and producer guarantees.
We’ll delve into the details of delivery guarantees, including exactly-once semantics, partition strategies and consumer group rebalances. The talk will finish with a discussion of compacted topics, troubleshooting strategies and a security overview.
This session is part 3 of 4 in our Fundamentals for Apache Kafka series.
My team at Zalando fell in love with KStreams and their programming model straight out of the gate. However, as a small team of developers, building out and supporting our infrastructure while still trying to deliver solutions for our business has not always resulted in a smooth journey. Can a small team of a couple of developers run their own Kafka infrastructure confidently and still spend most of their time developing code? In this talk, we will dive into some of the problems we experienced while running Kafka brokers and Kafka streams applications, as well as the consultations we had with other teams around this matter. We will outline some of the pragmatic decisions we made regarding backups, monitoring and operations to minimize our time spent administering our Kafka brokers and various stream applications.
Understanding Kafka Produce and Fetch api calls for high throughtput applicat...HostedbyConfluent
The data team at Cloudflare uses Kafka to process tens of petabytes a day. All this data is moved using the 2 foundational Kafka api calls: Produce (api key 0) and Fetch (api key 1). Understanding the structure of these calls (and of the underlying RecordSet structure) is key to building high throughput clients.
The talk describes the basics of the Kafka wire protocol (api keys, correlation id), and the structure of the Produce and Fetch calls. It shows how the asynchronous nature of the wire protocol can combine with the structure of the Produce and Fetch calls to increase latency and reduce client throughput; a solution is offered through use of synchronous single-partition calls.
The RecordSet structure, which is used to encode and store sets (batches) of records is described, and its implications on Fetch requests are discussed. The relationship between Fetch api calls and ""consume"" operations is discussed, as is the impact of offset alignment to RecordSet boundaries.
Sharing is Caring: Toward Creating Self-tuning Multi-tenant Kafka (Anna Povzn...HostedbyConfluent
Deploying Kafka to support multiple teams or even an entire company has many benefits. It reduces operational costs, simplifies onboarding of new applications as your adoption grows, and consolidates all your data in one place. However, this makes applications sharing the cluster vulnerable to any one or few of them taking all cluster resources. The combined cluster load also becomes less predictable, increasing the risk of overloading the cluster and data unavailability.
In this talk, we will describe how to use quota framework in Apache Kafka to ensure that a misconfigured client or unexpected increase in client load does not monopolize broker resources. You will get a deeper understanding of bandwidth and request quotas, how they get enforced, and gain intuition for setting the limits for your use-cases.
While quotas limit individual applications, there must be enough cluster capacity to support the combined application load. Onboarding new applications or scaling the usage of existing applications may require manual quota adjustments and upfront capacity planning to ensure high availability.
We will describe the steps we took toward solving this problem in Confluent Cloud, where we must immediately support unpredictable load with high availability. We implemented a custom broker quota plugin (KIP-257) to replace static per broker quota allocation with dynamic and self-tuning quotas based on the available capacity (which we also detect dynamically). By learning our journey, you will have more insights into the relevant problems and techniques to address them.
Hello, kafka! (an introduction to apache kafka)Timothy Spann
Hello ApacheKafka
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We also demo Flink SQL, SMM, SSB, Schema Registry, Apache Kafka, Apache NiFi and Public Cloud - AWS.
Automatisierung & Verwaltung von Datenbank - Clustern mit Severalnines - Mari...MariaDB Corporation
Automatisierung & Verwaltung von Datenbank - Clustern mit Severalnines
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Skalierbarkeit mit MariaDB und MaxScale - MariaDB Roadshow Summer 2014 Hambur...MariaDB Corporation
Skalierbarkeit mit MariaDB und MaxScale
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Migrating applications to the cloud requires both a sound strategy and a clear understanding of technical considerations. In addition, you will want to ensure portability between clouds to avoid lock-in. This session will define strategic approaches to cloud migration and how to assess application suitability for migration.
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VMworld 2013: Three Advantages of Running Cloud Foundry in a VMware Private C...VMworld
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Severalnines’ ClusterControl vs. Continuent Tungsten Clusters for MySQL
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ClusterControl uses Galera to handle the MySQL clustering, which means it uses synchronous replication. Learn in this webinar!
You may use Tungsten Clustering with native MySQL, MariaDB or Percona Server for MySQL in GCP, AWS, Azure, and/or on-premises data centers for better technological capabilities, control, and flexibility. But learn about the pros and cons!
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- Goals for the High Noon Webinar Series
- High Noon Series: Tungsten Clustering vs Others
- Oracle InnoDB Cluster
- Key Characteristics
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- InnoDB Cluster Multi-Site Requirements
- Limitations Using InnoDB Cluster
- How to do better MySQL HA / DR / Geo-Distribution?
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- About Continuent & Its Solutions
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Matthew Lang - Customer Success Director – Americas, Continuent - has over 25 years of experience in database administration, database programming, and system architecture, including the creation of a database replication product that is still in use today. He has designed highly available, scaleable systems that have allowed startups to quickly become enterprise organizations, utilizing a variety of technologies including open source projects, virtualization and cloud.
Webinar Slides: MySQL HA/DR/Geo-Scale - High Noon #2: Galera ClusterContinuent
Galera Cluster vs. Continuent Tungsten Clusters
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Watch this webinar to learn how to do better MySQL HA / DR / Geo-Scale.
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- Goals for the High Noon Webinar Series
- High Noon Series: Tungsten Clustering vs Others
- Galera Cluster (aka MariaDB Cluster & Percona XtraDB Cluster)
- Key Characteristics
- Certification-based Replication
- Galera Multi-Site Requirements
- Limitations Using Galera Cluster
- How to do better MySQL HA / DR / Geo-Scale?
- Galera Cluster vs Tungsten Clustering
- About Continuent & Its Solutions
PRESENTER
Matthew Lang - Customer Success Director – Americas, Continuent - has over 25 years of experience in database administration, database programming, and system architecture, including the creation of a database replication product that is still in use today. He has designed highly available, scaleable systems that have allowed startups to quickly become enterprise organizations, utilizing a variety of technologies including open source projects, virtualization and cloud.
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Maria DB Galera Cluster for High AvailabilityOSSCube
Want to understand how to set high availability solutions for MySQL using MariaDB Galera Cluster? Join this webinar, and learn from experts. During this webinar, you will also get guidance on how to implement MariaDB Galera Cluster.
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This presentation emphasizes the importance of data security and legal compliance for Nidhi companies in India. It highlights how online Nidhi software solutions, like Vector Nidhi Software, offer advanced features tailored to these needs. Key aspects include encryption, access controls, and audit trails to ensure data security. The software complies with regulatory guidelines from the MCA and RBI and adheres to Nidhi Rules, 2014. With customizable, user-friendly interfaces and real-time features, these Nidhi software solutions enhance efficiency, support growth, and provide exceptional member services. The presentation concludes with contact information for further inquiries.
Unleash Unlimited Potential with One-Time Purchase
BoxLang is more than just a language; it's a community. By choosing a Visionary License, you're not just investing in your success, you're actively contributing to the ongoing development and support of BoxLang.
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.
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.
Understanding Globus Data Transfers with NetSageGlobus
NetSage is an open privacy-aware network measurement, analysis, and visualization service designed to help end-users visualize and reason about large data transfers. NetSage traditionally has used a combination of passive measurements, including SNMP and flow data, as well as active measurements, mainly perfSONAR, to provide longitudinal network performance data visualization. It has been deployed by dozens of networks world wide, and is supported domestically by the Engagement and Performance Operations Center (EPOC), NSF #2328479. We have recently expanded the NetSage data sources to include logs for Globus data transfers, following the same privacy-preserving approach as for Flow data. Using the logs for the Texas Advanced Computing Center (TACC) as an example, this talk will walk through several different example use cases that NetSage can answer, including: Who is using Globus to share data with my institution, and what kind of performance are they able to achieve? How many transfers has Globus supported for us? Which sites are we sharing the most data with, and how is that changing over time? How is my site using Globus to move data internally, and what kind of performance do we see for those transfers? What percentage of data transfers at my institution used Globus, and how did the overall data transfer performance compare to the Globus users?
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.