Presentation by Ewen Cheslack-Postava, Engineer, Apache Kafka Committer, Confluent
In streaming workloads, often times data produced at the source is not useful down the pipeline or it requires some transformation to get it into usable shape. Similarly, where sensitive data is concerned, filtering of topics is helpful to ensure that the wrong data doesn't get to the wrong place.
The newest release of Apache Kafka now offers the ability to do transformations on individual messages, making is possible to implement finer grained transformations customized to your unique needs. In this session we’ll talk about the new single message transform capabilities, how to use them to implement things like data masking and advanced partitioning, and when you’ll need to use more complex tools like the Kafka Streams API instead.
Monitoring Apache Kafka with Confluent Control Center confluent
Presentation by Nick Dearden, Direct, Product and Engineering, Confluent
It’s 3 am. Do you know how your Kafka cluster is doing?
With over 150 metrics to think about, operating a Kafka cluster can be daunting, particularly as a deployment grows. Confluent Control Center is the only complete monitoring and administration product for Apache Kafka and is designed specifically for making the Kafka operators life easier.
Join Confluent as we cover how Control Center is used to simplify deployment, operability, and ensure message delivery.
Watch the recording: https://www.confluent.io/online-talk/monitoring-and-alerting-apache-kafka-with-confluent-control-center/
Presentation by Gwen Shapira, Product Manager, Confluent.
With the rapid increase of Apache Kafka use within organizations, issues of data governance and data quality take center stage. When more and more disparate departments and teams depend on the data in Apache Kafka, it’s important to provide a way to make sure "bad data" does not make its way into critical topics. Every organization that uses Kafka at large scale realize they need a way to deliver these guarantees.
In this talk, Kafka committer, Gwen Shapira will review the benefits of a schema registry for large-scale Kafka deployments and will give high-level overview of how the Confluent schema registry is being used in enterprise architectures across industry.
0-60: Tesla's Streaming Data Platform ( Jesse Yates, Tesla) Kafka Summit SF 2019confluent
Tesla ingests trillions of events every day from hundreds of unique data sources through our streaming data platform. Find out how we developed a set of high-throughput, non-blocking primitives that allow us to transform and ingest data into a variety of data stores with minimal development time. Additionally, we will discuss how these primitives allowed us to completely migrate the streaming platform in just a few months. Finally, we will talk about how we scale team size sub-linearly to data volumes, while continuing to onboard new use cases.
Neha Narkhede talks about the experience at LinkedIn moving from batch-oriented ETL to real-time streams using Apache Kafka and how the design and implementation of Kafka was driven by this goal of acting as a real-time platform for event data. She covers some of the challenges of scaling Kafka to hundreds of billions of events per day at Linkedin, supporting thousands of engineers, etc.
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
Monitoring Apache Kafka with Confluent Control Center confluent
Presentation by Nick Dearden, Direct, Product and Engineering, Confluent
It’s 3 am. Do you know how your Kafka cluster is doing?
With over 150 metrics to think about, operating a Kafka cluster can be daunting, particularly as a deployment grows. Confluent Control Center is the only complete monitoring and administration product for Apache Kafka and is designed specifically for making the Kafka operators life easier.
Join Confluent as we cover how Control Center is used to simplify deployment, operability, and ensure message delivery.
Watch the recording: https://www.confluent.io/online-talk/monitoring-and-alerting-apache-kafka-with-confluent-control-center/
Presentation by Gwen Shapira, Product Manager, Confluent.
With the rapid increase of Apache Kafka use within organizations, issues of data governance and data quality take center stage. When more and more disparate departments and teams depend on the data in Apache Kafka, it’s important to provide a way to make sure "bad data" does not make its way into critical topics. Every organization that uses Kafka at large scale realize they need a way to deliver these guarantees.
In this talk, Kafka committer, Gwen Shapira will review the benefits of a schema registry for large-scale Kafka deployments and will give high-level overview of how the Confluent schema registry is being used in enterprise architectures across industry.
0-60: Tesla's Streaming Data Platform ( Jesse Yates, Tesla) Kafka Summit SF 2019confluent
Tesla ingests trillions of events every day from hundreds of unique data sources through our streaming data platform. Find out how we developed a set of high-throughput, non-blocking primitives that allow us to transform and ingest data into a variety of data stores with minimal development time. Additionally, we will discuss how these primitives allowed us to completely migrate the streaming platform in just a few months. Finally, we will talk about how we scale team size sub-linearly to data volumes, while continuing to onboard new use cases.
Neha Narkhede talks about the experience at LinkedIn moving from batch-oriented ETL to real-time streams using Apache Kafka and how the design and implementation of Kafka was driven by this goal of acting as a real-time platform for event data. She covers some of the challenges of scaling Kafka to hundreds of billions of events per day at Linkedin, supporting thousands of engineers, etc.
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
Paolo Castagna is a Senior Sales Engineer at Confluent. His background is on 'big data' and he has, first hand, saw the shift happening in the industry from batch to stream processing and from big data to fast data. His talk will introduce Kafka Streams and explain why Apache Kafka is a great option and simplification for stream processing.
Confluent building a real-time streaming platform using kafka streams and k...Thomas Alex
Jeremy Custenborder from Confluent talked about how Kafka brings an event-centric approach to building streaming applications, and how to use Kafka Connect and Kafka Streams to build them.
Kafka streams - From pub/sub to a complete stream processing platformPaolo Castagna
A presentation on Kafka Streams APIs (part of Apache Kafka) and the innovative capabilities which brings in the world of open source stream processing engines. Simplicity (but powerful) and focus on developers being the biggest innovation.
Enabling Insight to Support World-Class Supercomputing (Stefan Ceballos, Oak ...confluent
The Oak Ridge Leadership Facility (OLCF) in the National Center for Computational Sciences (NCCS) division at Oak Ridge National Laboratory (ORNL) houses world-class high-performance computing (HPC) resources and has a history of operating top-ranked supercomputers on the TOP500 list, including the world's current fastest, Summit, an IBM AC922 machine with a peak of 200 petaFLOPS. With the exascale era rapidly approaching, the need for a robust and scalable big data platform for operations data is more important than ever. In the past when a new HPC resource was added to the facility, pipelines from data sources spanned multiple data sinks which oftentimes resulted in data silos, slow operational data onboarding, and non-scalable data pipelines for batch processing. Using Apache Kafka as the message bus of the division's new big data platform has allowed for easier decoupling of scalable data pipelines, faster data onboarding, and stream processing with the goal to continuously improve insight into the HPC resources and their supporting systems. This talk will focus on the NCCS division's transition to Apache Kafka over the past few years to enhance the OLCF's current capabilities and prepare for Frontier, OLCF's future exascale system; including the development and deployment of a full big data platform in a Kubernetes environment from both a technical and cultural shift perspective. This talk will also cover the mission of the OLCF, the operational data insights related to high-performance computing that the organization strives for, and several use-cases that exist in production today.
Five Fabulous Sinks for Your Kafka Data. #3 will surprise you! (Rachel Pedres...confluent
Apache Kafka has come the modern central point for a fast and scalable streaming platform. Now, thanks to the open source explosion over the last decade, there are now numerous data stores available as sinks for Kafka-brokered data, from search to document stores, columnular DBs, time series DBs and more. While many claim they are the swiss army knife, in reality each is designed for specific types of data and analytics approaches. In this talk, we will cover the taxonomy of various data sinks, delve into each categories pros, cons and ideal use cases, so you can select the right ones and tie them together with Kafka into a well-considered architecture.
How Credit Karma Makes Real-Time Decisions For 60 Million Users With Akka Str...Lightbend
In this webinar, Engineering Manager at Credit Karma, Dustin Lyons, discusses how not long ago his team was facing a common challenge shared by many financial services architects and engineering leaders: not only how to move from the offline, batch-mode processing of Big Data to streaming, Fast Data, and how to enable real-time decision making based on the information flowing in from over 60 million members.
Dustin reviews how his team migrated away from PHP and successfully implemented Akka Streams with Apache Kafka to ingest, process and route real-time events throughout their data ecosystem. At the end of this presentation, you’ll better understand:
* The design considerations for new Fast Data architectures, from streaming to microservices to real-time analysis.
* Some lessons learned when it comes to progressing from batch to streaming using Akka, Spark and Kafka
* Why Akka’s self-healing actor model and the resilience that it provides is actually what matters most when delivering real-time customer experiences
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.
We share our experience with Apache Kafka for event-driven collaboration in microservices-based architecture. Talk was a part of Meetup: https://www.meetup.com/de-DE/Apache-Kafka-Germany-Munich/events/236402498/
Streaming Data Integration - For Women in Big Data MeetupGwen (Chen) Shapira
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.
Building Realtim Data Pipelines with Kafka Connect and Spark StreamingGuozhang Wang
Spark Streaming makes it easy to build scalable, robust stream processing applications — but only once you’ve made your data accessible to the framework. Spark Streaming solves the realtime data processing problem, but to build large scale data pipeline we need to combine it with another tool that addresses data integration challenges. The Apache Kafka project recently introduced a new tool, Kafka Connect, to make data import/export to and from Kafka easier.
Landoop presentation in the Athens Big Data meetup, about streaming technologies on Apache Kafka. Introduction to the Lenses SQL engine and the Lenses platform and our open-source projects.
A Practical Guide to Selecting a Stream Processing Technology confluent
Presented by Michael Noll, Product Manager, Confluent.
Why are there so many stream processing frameworks that each define their own terminology? Are the components of each comparable? Why do you need to know about spouts or DStreams just to process a simple sequence of records? Depending on your application’s requirements, you may not need a full framework at all.
Processing and understanding your data to create business value is the ultimate goal of a stream data platform. In this talk we will survey the stream processing landscape, the dimensions along which to evaluate stream processing technologies, and how they integrate with Apache Kafka. Particularly, we will learn how Kafka Streams, the built-in stream processing engine of Apache Kafka, compares to other stream processing systems that require a separate processing infrastructure.
Event-Driven Stream Processing and Model Deployment with Apache Kafka, Kafka ...Kai Wähner
Talk from Kafka Summit San Francisco 2019 (https://kafka-summit.org/sessions/event-driven-model-serving-stream-processing-vs-rpc-kafka-tensorflow/). Video recording will be available for free on the Summit website.
Event-based stream processing is a modern paradigm to continuously process incoming data feeds, e.g. for IoT sensor analytics, payment and fraud detection, or logistics. Machine Learning / Deep Learning models can be leveraged in different ways to do predictions and improve the business processes. Either analytic models are deployed natively in the application or they are hosted in a remote model server. In the latter you combine stream processing with RPC / Request-Response paradigm instead of direct doing direct inference within the application. This talk discusses the pros and cons of both approaches and shows examples of stream processing vs. RPC model serving using Kubernetes, Apache Kafka, Kafka Streams, gRPC and TensorFlow Serving. The trade-offs of using a public cloud service like AWS or GCP for model deployment are also discussed and compared to local hosting for offline predictions directly “at the edge”.
Key takeaways
• Machine Learning / Deep Learning models can be used in different ways to do predictions. Scalability and loose coupling are important success factors
• Stream processing vs. RPC / Request-Response for model serving has many trade-offs – learn about alternatives and best practices for your different scenarios
• Understand the alternatives and trade-offs of model deployment in modern infrastructures like Kubernetes or Cloud Services like AWS or GCP
• See live demos with Java, gRPC, Apache Kafka, KSQL and TensorFlow Serving to understand the trade-offs
Flink at netflix paypal speaker seriesMonal Daxini
* Over 100 million subscribers from over 190 countries enjoy the Netflix service. This leads to over a trillion events flowing through the Keystone stream processing infrastructure to help glean business insights and improve customer experience. The self-serve infrastructure enables the users to focus on extracting insights, and not worry about building out scalable infrastructure. I’ll share our experience building building this platform with Flink, and lessons learnt.
Select Star: Flink SQL for Pulsar Folks - Pulsar Summit NA 2021StreamNative
SQL is eating the world (again!), and stream processing is no exception. As Flink SQL evolves to power business-critical applications at companies like Yelp, Airbnb or Uber, the Flink and Pulsar communities have been working in close collaboration to bring you the best of both worlds. But where do we stand today?
In this talk, we’ll get you up to speed with the latest in streaming SQL with Flink and demo how you can integrate with Apache Pulsar to build unified, elastic data processing pipelines.
Apache Kafka 0.8 basic training - VerisignMichael Noll
Apache Kafka 0.8 basic training (120 slides) covering:
1. Introducing Kafka: history, Kafka at LinkedIn, Kafka adoption in the industry, why Kafka
2. Kafka core concepts: topics, partitions, replicas, producers, consumers, brokers
3. Operating Kafka: architecture, hardware specs, deploying, monitoring, P&S tuning
4. Developing Kafka apps: writing to Kafka, reading from Kafka, testing, serialization, compression, example apps
5. Playing with Kafka using Wirbelsturm
Audience: developers, operations, architects
Created by Michael G. Noll, Data Architect, Verisign, https://www.verisigninc.com/
Verisign is a global leader in domain names and internet security.
Tools mentioned:
- Wirbelsturm (https://github.com/miguno/wirbelsturm)
- kafka-storm-starter (https://github.com/miguno/kafka-storm-starter)
Blog post at:
http://www.michael-noll.com/blog/2014/08/18/apache-kafka-training-deck-and-tutorial/
Many thanks to the LinkedIn Engineering team (the creators of Kafka) and the Apache Kafka open source community!
Strata+Hadoop 2017 San Jose - The Rise of Real Time: Apache Kafka and the Str...confluent
The move to streaming architectures from batch processing is a revolution in how companies use data. But what is the state of the union for stream processing, and what gaps remain in the technology we have? How will this technology impact the architectures and applications of the future? Jay Kreps explores the future of Apache Kafka and the stream processing ecosystem.
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.
Paolo Castagna is a Senior Sales Engineer at Confluent. His background is on 'big data' and he has, first hand, saw the shift happening in the industry from batch to stream processing and from big data to fast data. His talk will introduce Kafka Streams and explain why Apache Kafka is a great option and simplification for stream processing.
Confluent building a real-time streaming platform using kafka streams and k...Thomas Alex
Jeremy Custenborder from Confluent talked about how Kafka brings an event-centric approach to building streaming applications, and how to use Kafka Connect and Kafka Streams to build them.
Kafka streams - From pub/sub to a complete stream processing platformPaolo Castagna
A presentation on Kafka Streams APIs (part of Apache Kafka) and the innovative capabilities which brings in the world of open source stream processing engines. Simplicity (but powerful) and focus on developers being the biggest innovation.
Enabling Insight to Support World-Class Supercomputing (Stefan Ceballos, Oak ...confluent
The Oak Ridge Leadership Facility (OLCF) in the National Center for Computational Sciences (NCCS) division at Oak Ridge National Laboratory (ORNL) houses world-class high-performance computing (HPC) resources and has a history of operating top-ranked supercomputers on the TOP500 list, including the world's current fastest, Summit, an IBM AC922 machine with a peak of 200 petaFLOPS. With the exascale era rapidly approaching, the need for a robust and scalable big data platform for operations data is more important than ever. In the past when a new HPC resource was added to the facility, pipelines from data sources spanned multiple data sinks which oftentimes resulted in data silos, slow operational data onboarding, and non-scalable data pipelines for batch processing. Using Apache Kafka as the message bus of the division's new big data platform has allowed for easier decoupling of scalable data pipelines, faster data onboarding, and stream processing with the goal to continuously improve insight into the HPC resources and their supporting systems. This talk will focus on the NCCS division's transition to Apache Kafka over the past few years to enhance the OLCF's current capabilities and prepare for Frontier, OLCF's future exascale system; including the development and deployment of a full big data platform in a Kubernetes environment from both a technical and cultural shift perspective. This talk will also cover the mission of the OLCF, the operational data insights related to high-performance computing that the organization strives for, and several use-cases that exist in production today.
Five Fabulous Sinks for Your Kafka Data. #3 will surprise you! (Rachel Pedres...confluent
Apache Kafka has come the modern central point for a fast and scalable streaming platform. Now, thanks to the open source explosion over the last decade, there are now numerous data stores available as sinks for Kafka-brokered data, from search to document stores, columnular DBs, time series DBs and more. While many claim they are the swiss army knife, in reality each is designed for specific types of data and analytics approaches. In this talk, we will cover the taxonomy of various data sinks, delve into each categories pros, cons and ideal use cases, so you can select the right ones and tie them together with Kafka into a well-considered architecture.
How Credit Karma Makes Real-Time Decisions For 60 Million Users With Akka Str...Lightbend
In this webinar, Engineering Manager at Credit Karma, Dustin Lyons, discusses how not long ago his team was facing a common challenge shared by many financial services architects and engineering leaders: not only how to move from the offline, batch-mode processing of Big Data to streaming, Fast Data, and how to enable real-time decision making based on the information flowing in from over 60 million members.
Dustin reviews how his team migrated away from PHP and successfully implemented Akka Streams with Apache Kafka to ingest, process and route real-time events throughout their data ecosystem. At the end of this presentation, you’ll better understand:
* The design considerations for new Fast Data architectures, from streaming to microservices to real-time analysis.
* Some lessons learned when it comes to progressing from batch to streaming using Akka, Spark and Kafka
* Why Akka’s self-healing actor model and the resilience that it provides is actually what matters most when delivering real-time customer experiences
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.
We share our experience with Apache Kafka for event-driven collaboration in microservices-based architecture. Talk was a part of Meetup: https://www.meetup.com/de-DE/Apache-Kafka-Germany-Munich/events/236402498/
Streaming Data Integration - For Women in Big Data MeetupGwen (Chen) Shapira
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.
Building Realtim Data Pipelines with Kafka Connect and Spark StreamingGuozhang Wang
Spark Streaming makes it easy to build scalable, robust stream processing applications — but only once you’ve made your data accessible to the framework. Spark Streaming solves the realtime data processing problem, but to build large scale data pipeline we need to combine it with another tool that addresses data integration challenges. The Apache Kafka project recently introduced a new tool, Kafka Connect, to make data import/export to and from Kafka easier.
Landoop presentation in the Athens Big Data meetup, about streaming technologies on Apache Kafka. Introduction to the Lenses SQL engine and the Lenses platform and our open-source projects.
A Practical Guide to Selecting a Stream Processing Technology confluent
Presented by Michael Noll, Product Manager, Confluent.
Why are there so many stream processing frameworks that each define their own terminology? Are the components of each comparable? Why do you need to know about spouts or DStreams just to process a simple sequence of records? Depending on your application’s requirements, you may not need a full framework at all.
Processing and understanding your data to create business value is the ultimate goal of a stream data platform. In this talk we will survey the stream processing landscape, the dimensions along which to evaluate stream processing technologies, and how they integrate with Apache Kafka. Particularly, we will learn how Kafka Streams, the built-in stream processing engine of Apache Kafka, compares to other stream processing systems that require a separate processing infrastructure.
Event-Driven Stream Processing and Model Deployment with Apache Kafka, Kafka ...Kai Wähner
Talk from Kafka Summit San Francisco 2019 (https://kafka-summit.org/sessions/event-driven-model-serving-stream-processing-vs-rpc-kafka-tensorflow/). Video recording will be available for free on the Summit website.
Event-based stream processing is a modern paradigm to continuously process incoming data feeds, e.g. for IoT sensor analytics, payment and fraud detection, or logistics. Machine Learning / Deep Learning models can be leveraged in different ways to do predictions and improve the business processes. Either analytic models are deployed natively in the application or they are hosted in a remote model server. In the latter you combine stream processing with RPC / Request-Response paradigm instead of direct doing direct inference within the application. This talk discusses the pros and cons of both approaches and shows examples of stream processing vs. RPC model serving using Kubernetes, Apache Kafka, Kafka Streams, gRPC and TensorFlow Serving. The trade-offs of using a public cloud service like AWS or GCP for model deployment are also discussed and compared to local hosting for offline predictions directly “at the edge”.
Key takeaways
• Machine Learning / Deep Learning models can be used in different ways to do predictions. Scalability and loose coupling are important success factors
• Stream processing vs. RPC / Request-Response for model serving has many trade-offs – learn about alternatives and best practices for your different scenarios
• Understand the alternatives and trade-offs of model deployment in modern infrastructures like Kubernetes or Cloud Services like AWS or GCP
• See live demos with Java, gRPC, Apache Kafka, KSQL and TensorFlow Serving to understand the trade-offs
Flink at netflix paypal speaker seriesMonal Daxini
* Over 100 million subscribers from over 190 countries enjoy the Netflix service. This leads to over a trillion events flowing through the Keystone stream processing infrastructure to help glean business insights and improve customer experience. The self-serve infrastructure enables the users to focus on extracting insights, and not worry about building out scalable infrastructure. I’ll share our experience building building this platform with Flink, and lessons learnt.
Select Star: Flink SQL for Pulsar Folks - Pulsar Summit NA 2021StreamNative
SQL is eating the world (again!), and stream processing is no exception. As Flink SQL evolves to power business-critical applications at companies like Yelp, Airbnb or Uber, the Flink and Pulsar communities have been working in close collaboration to bring you the best of both worlds. But where do we stand today?
In this talk, we’ll get you up to speed with the latest in streaming SQL with Flink and demo how you can integrate with Apache Pulsar to build unified, elastic data processing pipelines.
Apache Kafka 0.8 basic training - VerisignMichael Noll
Apache Kafka 0.8 basic training (120 slides) covering:
1. Introducing Kafka: history, Kafka at LinkedIn, Kafka adoption in the industry, why Kafka
2. Kafka core concepts: topics, partitions, replicas, producers, consumers, brokers
3. Operating Kafka: architecture, hardware specs, deploying, monitoring, P&S tuning
4. Developing Kafka apps: writing to Kafka, reading from Kafka, testing, serialization, compression, example apps
5. Playing with Kafka using Wirbelsturm
Audience: developers, operations, architects
Created by Michael G. Noll, Data Architect, Verisign, https://www.verisigninc.com/
Verisign is a global leader in domain names and internet security.
Tools mentioned:
- Wirbelsturm (https://github.com/miguno/wirbelsturm)
- kafka-storm-starter (https://github.com/miguno/kafka-storm-starter)
Blog post at:
http://www.michael-noll.com/blog/2014/08/18/apache-kafka-training-deck-and-tutorial/
Many thanks to the LinkedIn Engineering team (the creators of Kafka) and the Apache Kafka open source community!
Strata+Hadoop 2017 San Jose - The Rise of Real Time: Apache Kafka and the Str...confluent
The move to streaming architectures from batch processing is a revolution in how companies use data. But what is the state of the union for stream processing, and what gaps remain in the technology we have? How will this technology impact the architectures and applications of the future? Jay Kreps explores the future of Apache Kafka and the stream processing ecosystem.
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.
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.
Strata+Hadoop 2017 San Jose: Lessons from a year of supporting Apache Kafkaconfluent
The number of deployments of Apache Kafka at enterprise scale has greatly increased in the years since Kafka’s original development in 2010. Along with this rapid growth has come a wide variety of use cases and deployment strategies that transcend what Kafka’s creators imagined when they originally developed the technology. As the scope and reach of streaming data platforms based on Apache Kafka has grown, the need to understand monitoring and troubleshooting strategies has as well.
Dustin Cote and Ryan Pridgeon share their experience supporting Apache Kafka at enterprise-scale and explore monitoring and troubleshooting techniques to help you avoid pitfalls when scaling large-scale Kafka deployments.
Topics include:
- Effective use of JMX for Kafka
- Tools for preventing small problems from becoming big ones
- Efficient architectures proven in the wild
- Finding and storing the right information when it all goes wrong
Visit www.confluent.io for more information.
In the last few years, Apache Kafka has been used extensively in enterprises for real-time data collecting, delivering, and processing. In this presentation, Jun Rao, Co-founder, Confluent, gives a deep dive on some of the key internals that help make Kafka popular.
- Companies like LinkedIn are now sending more than 1 trillion messages per day to Kafka. Learn about the underlying design in Kafka that leads to such high throughput.
- Many companies (e.g., financial institutions) are now storing mission critical data in Kafka. Learn how Kafka supports high availability and durability through its built-in replication mechanism.
- One common use case of Kafka is for propagating updatable database records. Learn how a unique feature called compaction in Apache Kafka is designed to solve this kind of problem more naturally.
Distributed stream processing with Apache Kafkaconfluent
A modern business operates 24/7 and generates data continuously. Shouldn’t we process it continuously too?
A rich ecosystem of real-time data-processing frameworks, tools and systems has been forming around Apache Kafka that allows data to be processed continuously as it occurs. Jay Kreps will introduce Kafka and explain why it has become the de facto standard for streaming data. He will draw on practical experience building stream-processing applications to discuss the difference between architectures and the challenges each presents. Jay will then outline the Kafka Streams API, which offers new stream processing functionality in Kafka, and explain how it helps tame some of the complexity in real-time architectures.
Visit www.confluent.io for more information
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.
What's new in Confluent 3.2 and Apache Kafka 0.10.2 confluent
With the introduction of connect and streams API in 2016, Apache Kafka is becoming the defacto solution for anyone looking to build a streaming platform. The community continues to add additional capabilities to make it the complete solution for streaming data.
Join us as we review the latest additions in Apache Kafka 0.10.2. In addition, we’ll cover what’s new in Confluent Enterprise 3.2 that makes it possible for running Kafka at scale.
Power of the Log: LSM & Append Only Data Structuresconfluent
This talk is about the beauty of sequential access and append-only data structures. We'll do this in the context of a little-known paper entitled “Log Structured Merge Trees”. LSM describes a surprisingly counterintuitive approach to storing and accessing data in a sequential fashion. It came to prominence in Google's Big Table paper and today, the use of Logs, LSM and append-only data structures drive many of the world's most influential storage systems: Cassandra, HBase, RocksDB, Kafka and more. Finally, we'll look at how the beauty of sequential access goes beyond database internals, right through to how applications communicate, share data and scale.
Demystifying Stream Processing with Apache Kafkaconfluent
www.confluent.io/online-talks/
This talk will introduce Kafka Streams and help you understand how to map practical data problems to stream processing and how to write applications that process streams of data at scale using Kafka Streams. We will also cover what is stream processing, why one should care about stream processing, where Apache Kafka and Kafka Streams fit in, the hard parts of stream processing, and how Kafka Streams solves those problems; along with a concrete example of how these ideas tie together in Kafka Streams and in the big picture of your data center.
JustGiving – Serverless Data Pipelines, API, Messaging and Stream ProcessingLuis Gonzalez
What to Expect from the Session
• Recap of some AWS services
• Event-driven data platform at JustGiving
• Serverless computing
• Six serverless patterns
• Serverless recommendations and best practices
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.
Leveraging Mainframe Data for Modern Analyticsconfluent
“The mainframe is going away” is as true now as it was 10, 20 and 30 years ago. Mainframes are still crucial in handling critical business transactions, they were however built for an era where batch data movement was the norm and can be difficult to integrate into today’s data-driven, real-time, analytics-focused business processes as well as the environments that support them. Until now.
Join experts from Confluent, Attunity, and Capgemini for a one-hour online talk session where you’ll learn how to:
Unlock your mainframe data with unique change data capture (CDC) functionality without incurring the complexity and expense that come with sending ongoing queries into the mainframe database
How using CDC benefits advanced analytics approaches such as deep machine learning and predictive analytics
Deliver ongoing streams of data in real-time to the most demanding analytics environments
Ensure that your analytics environment includes the broadest possible range of data sources and destinations while ensuring true enterprise-grade functionality
Identify use cases that can help you get started delivering value to the business moving from POC to Pilot to Production
Spark and MapR Streams: A Motivating ExampleIan Downard
Businesses are discovering the untapped potential of large datasets and data streams through the use of technologies for big data processing and storage. By leveraging these assets they’re creating a new generation of applications that derive value from data they used to throw away. In this presentation Ian Downard shows how to build operational environments for these types of applications with the MapR Converged Data Platform and he describes examples of a next-generation applications that use Java APIs for MapR Streams, Apache Spark, Apache Hive, and MapR-DB. He shows how these technologies can be used to join and transform unbounded datasets to find signals and derive new data streams for a financial scenario involving real-time algorithmic trading and historical analysis using SQL. He also discusses how MapR enables you to run real-time data applications with the speed, reliability, and security you need for a production environment.
Modern businesses have data at their core, and this data is changing continuously. How can we harness this torrent of information in real-time? The answer is stream processing, and the technology that has since become the core platform for streaming data is Apache Kafka. Among the thousands of companies that use Kafka to transform and reshape their industries are the likes of Netflix, Uber, PayPal, and AirBnB, but also established players such as Goldman Sachs, Cisco, and Oracle.
Unfortunately, today’s common architectures for real-time data processing at scale suffer from complexity: there are many technologies that need to be stitched and operated together, and each individual technology is often complex by itself. This has led to a strong discrepancy between how we, as engineers, would like to work vs. how we actually end up working in practice.
In this session we talk about how Apache Kafka helps you to radically simplify your data processing architectures. We cover how you can now build normal applications to serve your real-time processing needs — rather than building clusters or similar special-purpose infrastructure — and still benefit from properties such as high scalability, distributed computing, and fault-tolerance, which are typically associated exclusively with cluster technologies. Notably, we introduce Kafka’s Streams API, its abstractions for streams and tables, and its recently introduced Interactive Queries functionality. As we will see, Kafka makes such architectures equally viable for small, medium, and large scale use cases.
Introduction To Streaming Data and Stream Processing with Apache Kafkaconfluent
Modern businesses have data at their core, and this data is changing continuously. How can we harness this torrent of continuously changing data in real time? The answer is stream processing, and one system that has become a core hub for streaming data is Apache Kafka.
This presentation will give a brief introduction to Apache Kafka and describe its usage as a platform for streaming data. It will explain how Kafka serves as a foundation for both streaming data pipelines and applications that consume and process real-time data streams. It will introduce some of the newer components of Kafka that help make this possible, including Kafka Connect, a framework for capturing continuous data streams, and Kafka Streams, a lightweight stream processing library.
This is talk 1 out of 6 from the Kafka Talk Series.
http://www.confluent.io/apache-kafka-talk-series/introduction-to-stream-processing-with-apache-kafka
Streaming in Practice - Putting Apache Kafka in Productionconfluent
This presentation focuses on how to integrate all these components into an enterprise environment and what things you need to consider as you move into production.
We will touch on the following topics:
- Patterns for integrating with existing data systems and applications
- Metadata management at enterprise scale
- Tradeoffs in performance, cost, availability and fault tolerance
- Choosing which cross-datacenter replication patterns fit with your application
- Considerations for operating Kafka-based data pipelines in production
Data Streaming with Apache Kafka & MongoDBconfluent
Explore the use-cases and architecture for Apache Kafka, and how it integrates with MongoDB to build sophisticated data-driven applications that exploit new sources of data.
Hopsworks - The Platform for Data-Intensive AIQAware GmbH
Cloud Native Night July 2019, Munich: Talk by Steffen Srohsschmiedt (@grohsschmiedt, Head of Cloud at LogicalClocks)
=== Please download slides if blurred! ===
Abstract: Machine Learning (ML) pipelines are the fundamental building block for productionizing ML code. Building such pipelines with Big Data is a complex process. The different stages in ML pipelines also need to be orchestrated, from data ingestion and data transformation, to feature engineering, to model training, serving and monitoring.
Hopsworks is an open-source data platform that can be used to both develop and operate horizontally scalable machine learning (ML) pipelines. A key part of our pipelines is the world's first open-source Feature Store, that acts as a data warehouse for features, providing a natural API between data engineers - who write feature engineering code - and Data Scientists, who select features from the feature store to generate training/test data for models.
Join us next time: https://www.meetup.com/Cloud-Native-muc/events
Microservices, Kafka Streams and KafkaEsqueconfluent
Speakers: Patrick Schuh, Bearing Point + Patrik Kleindl, Bearing Point
Abstract:
- Managing topic configurations and dependencies in a microservice deployment
- Managing Kafka Streams configurations
- KafkaEsque: an open source support tool for Apache Kafka® development (https://github.com/patschuh/KafkaEsque)
Hopsworks at Google AI Huddle, SunnyvaleJim Dowling
Hopsworks is a platform for designing and operating End to End Machine Learning using PySpark and TensorFlow/PyTorch. Early access is now available on GCP. Hopsworks includes the industry's first Feature Store. Hopsworks is open-source.
ApacheCon 2021 Apache Deep Learning 302Timothy Spann
ApacheCon 2021 Apache Deep Learning 302
Tuesday 18:00 UTC
Apache Deep Learning 302
Timothy Spann
This talk will discuss and show examples of using Apache Hadoop, Apache Kudu, Apache Flink, Apache Hive, Apache MXNet, Apache OpenNLP, Apache NiFi and Apache Spark for deep learning applications. This is the follow up to previous talks on Apache Deep Learning 101 and 201 and 301 at ApacheCon, Dataworks Summit, Strata and other events. As part of this talk, the presenter will walk through using Apache MXNet Pre-Built Models, integrating new open source Deep Learning libraries with Python and Java, as well as running real-time AI streams from edge devices to servers utilizing Apache NiFi and Apache NiFi - MiNiFi. This talk is geared towards Data Engineers interested in the basics of architecting Deep Learning pipelines with open source Apache tools in a Big Data environment. The presenter will also walk through source code examples available in github and run the code live on Apache NiFi and Apache Flink clusters.
Tim Spann is a Developer Advocate @ StreamNative where he works with Apache NiFi, Apache Pulsar, Apache Flink, Apache MXNet, TensorFlow, Apache Spark, big data, the IoT, machine learning, and deep learning. Tim has over a decade of experience with the IoT, big data, distributed computing, streaming technologies, and Java programming. Previously, he was a Principal Field Engineer at Cloudera, a senior solutions architect at AirisData and a senior field engineer at Pivotal. He blogs for DZone, where he is the Big Data Zone leader, and runs a popular meetup in Princeton on big data, the IoT, deep learning, streaming, NiFi, the blockchain, and Spark. Tim is a frequent speaker at conferences such as IoT Fusion, Strata, ApacheCon, Data Works Summit Berlin, DataWorks Summit Sydney, and Oracle Code NYC. He holds a BS and MS in computer science.
* https://github.com/tspannhw/ApacheDeepLearning302/
* https://github.com/tspannhw/nifi-djl-processor
* https://github.com/tspannhw/nifi-djlsentimentanalysis-processor
* https://github.com/tspannhw/nifi-djlqa-processor
* https://www.linkedin.com/pulse/2021-schedule-tim-spann/
Kafka as your Data Lake - is it Feasible?Guido Schmutz
For a long time we discuss how much data we can keep in Kafka. Can we store data forever or do we remove data after a while and maybe having the history in a data lake on Object Storage or HDFS? With the advent of Tiered Storage in Confluent Enterprise Platform, storing data much longer in Kafka is much very feasible. So can we replace a traditional data lake with just Kafka? Maybe at least for the raw data? But what about accessing the data, for example using SQL?
KSQL allows for processing data in a streaming fashion using an SQL like dialect. But what about reading all data of a topic? You can reset the offset and still use KSQL. But there is another family of products, so-called query engines for Big Data. They originate from the idea of reading Big Data sources such as HDFS, object storage or HBase, using the SQL language. Presto, Apache Drill and Dremio are the most popular solutions in that space. Lately these query engines also added support for Kafka topics as a source of data. With that you can read a topic as a table and join it with information available in other data sources. The idea of course is not real-time streaming analytics but batch analytics directly on the Kafka topic, without having to store it in a big data storage.
This talk answers, how well these tools support Kafka as a data source. What serialization formats do they support? Is there some form of predicate push-down supported or do we have to always read the complete topic? How performant is a query against a topic, compared to a query against the same data sitting in HDFS or an object store? And finally, will this allow us to replace our data lake or at least part of it by Apache Kafka?
Owning time series with team apache Strata San Jose 2015Patrick McFadin
Break out your laptops for this hands-on tutorial is geared around understanding the basics of how Apache Cassandra stores and access time series data. We’ll start with an overview of how Cassandra works and how that can be a perfect fit for time series. Then we will add in Apache Spark as a perfect analytics companion. There will be coding as a part of the hands on tutorial. The goal will be to take a example application and code through the different aspects of working with this unique data pattern. The final section will cover the building of an end-to-end data pipeline to ingest, process and store high speed, time series data.
Scaling People, Not Just Systems, to Take On Big Data ChallengesMatthew Vaughn
Here, I describe how the Texas Advanced Computing Center has shifted its focus from traditional modeling and simulation towards fully embracing big data analytics performed by users with diverse technical backgrounds.
High-Performance and Scalable Designs of Programming Models for Exascale Systemsinside-BigData.com
DK Panda from Ohio State University presented this deck at the Switzerland HPC Conference.
"This talk will focus on challenges in designing programming models and runtime environments for Exascale systems with millions of processors and accelerators to support various programming models. We will focus on MPI+X (PGAS - OpenSHMEM/UPC/CAF/UPC++, OpenMP, and CUDA) programming models by taking into account support for multi-core systems (KNL and OpenPower), high-performance networks, GPGPUs (including GPUDirect RDMA), and energy-awareness. Features and sample performance numbers from the MVAPICH2 libraries, will be presented."
Watch the video: http://wp.me/p3RLHQ-gCb
Learn more: http://hpcadvisorycouncil.com/events/2017/swiss-workshop/agenda.php
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Video and slides synchronized, mp3 and slide download available at URL https://bit.ly/2UkZRIC.
Monal Daxini presents a blueprint for streaming data architectures and a review of desirable features of a streaming engine. He also talks about streaming application patterns and anti-patterns, and use cases and concrete examples using Apache Flink. Filmed at qconsf.com.
Monal Daxini is the Tech Lead for Stream Processing platform for business insights at Netflix. He helped build the petabyte scale Keystone pipeline running on the Flink powered platform. He introduced Flink to Netflix, and also helped define the vision for this platform. He has over 17 years of experience building scalable distributed systems.
Running cost effective big data workloads with Azure Synapse and ADLS (MS Ign...Michael Rys
Presentation by James Baker and myself on Running cost effective big data workloads with Azure Synapse and Azure Datalake Storage (ADLS) at Microsoft Ignite 2020. Covers Modern Data warehouse architecture supported by Azure Synapse, integration benefits with ADLS and some features that reduce cost such as Query Acceleration, integration of Spark and SQL processing with integrated meta data and .NET For Apache Spark support.
Kafka as your Data Lake - is it Feasible? (Guido Schmutz, Trivadis) Kafka Sum...HostedbyConfluent
For a long time we discuss how much data we can keep in Kafka. Can we store data forever or do we remove data after a while and maybe having the history in a data lake on Object Storage or HDFS? With the advent of Tiered Storage in Confluent Enterprise Platform, storing data much longer in Kafka is much very feasible. So can we replace a traditional data lake with just Kafka? Maybe at least for the raw data? But what about accessing the data, for example using SQL?
KSQL allows for processing data in a streaming fashion using an SQL like dialect. But what about reading all data of a topic? You can reset the offset and still use KSQL. But there is another family of products, so-called query engines for Big Data. They originate from the idea of reading Big Data sources such as HDFS, object storage or HBase, using the SQL language. Presto, Apache Drill and Dremio are the most popular solutions in that space. Lately these query engines also added support for Kafka topics as a source of data. With that you can read a topic as a table and join it with information available in other data sources. The idea of course is not real-time streaming analytics but batch analytics directly on the Kafka topic, without having to store it in a big data storage.
This talk answers, how well these tools support Kafka as a data source. What serialization formats do they support? Is there some form of predicate push-down supported or do we have to always read the complete topic? How performant is a query against a topic, compared to a query against the same data sitting in HDFS or an object store? And finally, will this allow us to replace our data lake or at least part of it by Apache Kafka?
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.
Prosigns: Transforming Business with Tailored Technology SolutionsProsigns
Unlocking Business Potential: Tailored Technology Solutions by Prosigns
Discover how Prosigns, a leading technology solutions provider, partners with businesses to drive innovation and success. Our presentation showcases our comprehensive range of services, including custom software development, web and mobile app development, AI & ML solutions, blockchain integration, DevOps services, and Microsoft Dynamics 365 support.
Custom Software Development: Prosigns specializes in creating bespoke software solutions that cater to your unique business needs. Our team of experts works closely with you to understand your requirements and deliver tailor-made software that enhances efficiency and drives growth.
Web and Mobile App Development: From responsive websites to intuitive mobile applications, Prosigns develops cutting-edge solutions that engage users and deliver seamless experiences across devices.
AI & ML Solutions: Harnessing the power of Artificial Intelligence and Machine Learning, Prosigns provides smart solutions that automate processes, provide valuable insights, and drive informed decision-making.
Blockchain Integration: Prosigns offers comprehensive blockchain solutions, including development, integration, and consulting services, enabling businesses to leverage blockchain technology for enhanced security, transparency, and efficiency.
DevOps Services: Prosigns' DevOps services streamline development and operations processes, ensuring faster and more reliable software delivery through automation and continuous integration.
Microsoft Dynamics 365 Support: Prosigns provides comprehensive support and maintenance services for Microsoft Dynamics 365, ensuring your system is always up-to-date, secure, and running smoothly.
Learn how our collaborative approach and dedication to excellence help businesses achieve their goals and stay ahead in today's digital landscape. From concept to deployment, Prosigns is your trusted partner for transforming ideas into reality and unlocking the full potential of your business.
Join us on a journey of innovation and growth. Let's partner for success with Prosigns.
Developing Distributed High-performance Computing Capabilities of an Open Sci...Globus
COVID-19 had an unprecedented impact on scientific collaboration. The pandemic and its broad response from the scientific community has forged new relationships among public health practitioners, mathematical modelers, and scientific computing specialists, while revealing critical gaps in exploiting advanced computing systems to support urgent decision making. Informed by our team’s work in applying high-performance computing in support of public health decision makers during the COVID-19 pandemic, we present how Globus technologies are enabling the development of an open science platform for robust epidemic analysis, with the goal of collaborative, secure, distributed, on-demand, and fast time-to-solution analyses to support public health.
Listen to the keynote address and hear about the latest developments from Rachana Ananthakrishnan and Ian Foster who review the updates to the Globus Platform and Service, and the relevance of Globus to the scientific community as an automation platform to accelerate scientific discovery.
AI Pilot Review: The World’s First Virtual Assistant Marketing SuiteGoogle
AI Pilot Review: The World’s First Virtual Assistant Marketing Suite
👉👉 Click Here To Get More Info 👇👇
https://sumonreview.com/ai-pilot-review/
AI Pilot Review: Key Features
✅Deploy AI expert bots in Any Niche With Just A Click
✅With one keyword, generate complete funnels, websites, landing pages, and more.
✅More than 85 AI features are included in the AI pilot.
✅No setup or configuration; use your voice (like Siri) to do whatever you want.
✅You Can Use AI Pilot To Create your version of AI Pilot And Charge People For It…
✅ZERO Manual Work With AI Pilot. Never write, Design, Or Code Again.
✅ZERO Limits On Features Or Usages
✅Use Our AI-powered Traffic To Get Hundreds Of Customers
✅No Complicated Setup: Get Up And Running In 2 Minutes
✅99.99% Up-Time Guaranteed
✅30 Days Money-Back Guarantee
✅ZERO Upfront Cost
See My Other Reviews Article:
(1) TubeTrivia AI Review: https://sumonreview.com/tubetrivia-ai-review
(2) SocioWave Review: https://sumonreview.com/sociowave-review
(3) AI Partner & Profit Review: https://sumonreview.com/ai-partner-profit-review
(4) AI Ebook Suite Review: https://sumonreview.com/ai-ebook-suite-review
Quarkus Hidden and Forbidden ExtensionsMax Andersen
Quarkus has a vast extension ecosystem and is known for its subsonic and subatomic feature set. Some of these features are not as well known, and some extensions are less talked about, but that does not make them less interesting - quite the opposite.
Come join this talk to see some tips and tricks for using Quarkus and some of the lesser known features, extensions and development techniques.
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
Climate Science Flows: Enabling Petabyte-Scale Climate Analysis with the Eart...Globus
The Earth System Grid Federation (ESGF) is a global network of data servers that archives and distributes the planet’s largest collection of Earth system model output for thousands of climate and environmental scientists worldwide. Many of these petabyte-scale data archives are located in proximity to large high-performance computing (HPC) or cloud computing resources, but the primary workflow for data users consists of transferring data, and applying computations on a different system. As a part of the ESGF 2.0 US project (funded by the United States Department of Energy Office of Science), we developed pre-defined data workflows, which can be run on-demand, capable of applying many data reduction and data analysis to the large ESGF data archives, transferring only the resultant analysis (ex. visualizations, smaller data files). In this talk, we will showcase a few of these workflows, highlighting how Globus Flows can be used for petabyte-scale climate analysis.
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.
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!
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
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.
Paketo Buildpacks : la meilleure façon de construire des images OCI? DevopsDa...Anthony Dahanne
Les Buildpacks existent depuis plus de 10 ans ! D’abord, ils étaient utilisés pour détecter et construire une application avant de la déployer sur certains PaaS. Ensuite, nous avons pu créer des images Docker (OCI) avec leur dernière génération, les Cloud Native Buildpacks (CNCF en incubation). Sont-ils une bonne alternative au Dockerfile ? Que sont les buildpacks Paketo ? Quelles communautés les soutiennent et comment ?
Venez le découvrir lors de cette session ignite
Into the Box Keynote Day 2: Unveiling amazing updates and announcements for modern CFML developers! Get ready for exciting releases and updates on Ortus tools and products. Stay tuned for cutting-edge innovations designed to boost your productivity.
Enterprise Resource Planning System includes various modules that reduce any business's workload. Additionally, it organizes the workflows, which drives towards enhancing productivity. Here are a detailed explanation of the ERP modules. Going through the points will help you understand how the software is changing the work dynamics.
To know more details here: https://blogs.nyggs.com/nyggs/enterprise-resource-planning-erp-system-modules/
Navigating the Metaverse: A Journey into Virtual Evolution"Donna Lenk
Join us for an exploration of the Metaverse's evolution, where innovation meets imagination. Discover new dimensions of virtual events, engage with thought-provoking discussions, and witness the transformative power of digital realms."
Navigating the Metaverse: A Journey into Virtual Evolution"
Data Pipelines Made Simple with Apache Kafka
1. 1
Data Pipelines Made Simple
With Apache Kafka
Ewen Cheslack-Postava
Engineer, Apache Kafka Committer
2. 2
Attend the whole series!
Simplify Governance for Streaming Data in Apache Kafka
Date: Thursday, April 6, 2017
Time: 9:30 am - 10:00 am PT | 12:30 pm - 1:00 pm ET
Speaker: Gwen Shapira, Product Manager, Confluent
Using Apache Kafka to Analyze Session Windows
Date: Thursday, March 30, 2017
Time: 9:30 am - 10:00 am PT | 12:30 pm - 1:00 pm ET
Speaker: Michael Noll, Product Manager, Confluent
Monitoring and Alerting Apache Kafka with Confluent Control
Center
Date: Thursday, March 16, 2017
Time: 9:30 am - 10:00 am PT | 12:30 pm - 1:00 pm ET
Speaker: Nick Dearden, Director, Engineering and Product
Data Pipelines Made Simple with Apache Kafka
Date: Thursday, March 23, 2017
Time: 9:30 am - 10:00 am PT | 12:30 pm - 1:00 pm ET
Speaker: Ewen Cheslack-Postava, Engineer, Confluent
https://www.confluent.io/online-talk/online-talk-series-five-steps-to-production-with-apache-kafka/
What’s New in Apache Kafka 0.10.2 and Confluent 3.2
Date: Thursday, March 9, 2017
Time: 9:30 am - 10:00 am PT | 12:30 pm - 1:00 pm ET
Speaker: Clarke Patterson, Senior Director, Product Marketing
7. 7
Single Message Transforms for Kafka Connect
Modify events before storing in
Kafka:
• Mask sensitive information
• Add identifiers
• Tag events
• Store lineage
• Remove unnecessary columns
Modify events going out of
Kafka:
• Route high priority events to
faster data stores
• Direct events to different
Elasticsearch indexes
• Cast data types to match
destination
• Remove unnecessary columns
9. 9
Built-in Transformations
• InsertField – Add a field using either static data or record metadata
• ReplaceField – Filter or rename fields
• MaskField – Replace field with valid null value for the type (0, empty string, etc)
• ValueToKey – Set the key to one of the value’s fields
• HoistField – Wrap the entire event as a single field inside a Struct or a Map
• ExtractField – Extract a specific field from Struct and Map and include only this field in results
• SetSchemaMetadata – modify the schema name or version
• TimestampRouter – Modify the topic of a record based on original topic and timestamp. Useful
when using a sink that needs to write to different tables or indexes based on timestamps
• RegexpRouter – modify the topic of a record based on original topic, replacement string and a
regular expression
11. 11
Why only single messages?
• Delivery guarantees!
• Always provide at least once semantics
• For supported connectors, provide exactly once semantics
• No additional complication: transformations happens inline with import/export
12. 12
When should I use each tool?
Kafka Connect & Single Message Transforms
• Simple, message at a time
• Transformation can be performed inline
• Transformation does not interact with
external systems
Kafka Streams
• Complex transformations including
• Aggregations
• Windowing
• Joins
• Transformed data stored back in Kafka,
enabling reuse
• Write, deploy, and monitor a Java
application
13. 13
Conclusion
Single Message Transforms in Kafka Connect
• Lightweight transformation of individual messages
• Configuration-only data pipelines
• Pluggable, with lots of built-in transformations
14. 14
Attend the whole series!
Simplify Governance for Streaming Data in Apache Kafka
Date: Thursday, April 6, 2017
Time: 9:30 am - 10:00 am PT | 12:30 pm - 1:00 pm ET
Speaker: Gwen Shapira, Product Manager, Confluent
Using Apache Kafka to Analyze Session Windows
Date: Thursday, March 30, 2017
Time: 9:30 am - 10:00 am PT | 12:30 pm - 1:00 pm ET
Speaker: Michael Noll, Product Manager, Confluent
Monitoring and Alerting Apache Kafka with Confluent Control
Center
Date: Thursday, March 16, 2017
Time: 9:30 am - 10:00 am PT | 12:30 pm - 1:00 pm ET
Speaker: Nick Dearden, Director, Engineering and Product
Data Pipelines Made Simple with Apache Kafka
Date: Thursday, March 23, 2017
Time: 9:30 am - 10:00 am PT | 12:30 pm - 1:00 pm ET
Speaker: Ewen Cheslack-Postava, Engineer, Confluent
https://www.confluent.io/online-talk/online-talk-series-five-steps-to-production-with-apache-kafka/
What’s New in Apache Kafka 0.10.2 and Confluent 3.2
Date: Thursday, March 9, 2017
Time: 9:30 am - 10:00 am PT | 12:30 pm - 1:00 pm ET
Speaker: Clarke Patterson, Senior Director, Product Marketing
15. 15
Get Started with Apache Kafka Today!
https://www.confluent.io/downloads/
THE place to start with Apache Kafka!
Thoroughly tested and quality
assured
More extensible developer
experience
Easy upgrade path to
Confluent Enterprise
16. 16
Discount code: kafcom17
Use the Apache Kafka community discount code to get $50 off
www.kafka-summit.org
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