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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.

Data integration with Apache Kafka

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

Streaming in Practice - Putting Apache Kafka in Production

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.

Demystifying Stream Processing with Apache Kafka

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.

Deep Dive into Apache Kafka

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.

What's new in Confluent 3.2 and Apache Kafka 0.10.2

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.

A Practical Guide to Selecting a Stream Processing Technology

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

Introduction To Streaming Data and Stream Processing with Apache Kafka

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.

Power of the Log: LSM & Append Only Data Structures

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/

Monitoring Apache Kafka with Confluent Control Center

“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

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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.

Data Pipelines Made Simple with Apache Kafka

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.

Strata+Hadoop 2017 San Jose: Lessons from a year of supporting Apache Kafka

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

Distributed stream processing with Apache Kafka

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.

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Introduction to Apache Kafka and Confluent... and why they matter!

Milano Apache Kafka Meetup by Confluent (First Italian Kafka Meetup) on Wednesday, November 29th 2017. Il talk introduce Apache Kafka (incluse le APIs Kafka Connect e Kafka Streams), Confluent (la società creata dai creatori di Kafka) e spiega perché Kafka è un'ottima e semplice soluzione per la gestione di stream di dati nel contesto di due delle principali forze trainanti e trend industriali: Internet of Things (IoT) e Microservices.

Introduction to Apache Kafka and Confluent... and why they matter

Apache Kafka and KSQL in Action: Let's Build a Streaming Data Pipeline!

Apache Kafka is taking the world by storm and is rapidly becoming the de-facto event bus for event-driven and streaming applications that respond to events and data in real time. OpenShift Streams for Apache Kafka is Red Hat's fully hosted and managed Apache Kafka service targeting development teams that want to incorporate streaming data and scalable messaging in their applications, without the burden of setting up and maintaining a Kafka cluster infrastructure. In this session you will discover how Apache Kafka can be used in an IoT scenario to ingest data from devices and make them available in real-time to other applications. More specifically you will learn how to: Simulate devices that send MQTT messages to a MQTT broker Use Apache Camel and Camel-K to bridge MQTT with Apache Kafka Use Kafka Streams in a Quarkus application to process the device messages Query the state of the devices using GraphQ

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Introduction to apache kafka, confluent and why they matter

After a quick overview and introduction of Apache Kafka, this session cover two components which extend the core of Apache Kafka: Kafka Connect and Kafka Streams/KSQL. Kafka Connects role is to access data from the out-side-world and make it available inside Kafka by publishing it into a Kafka topic. On the other hand, Kafka Connect is also responsible to transport information from inside Kafka to the outside world, which could be a database or a file system. There are many existing connectors for different source and target systems available out-of-the-box, either provided by the community or by Confluent or other vendors. You simply configure these connectors and off you go. Kafka Streams is a light-weight component which extends Kafka with stream processing functionality. By that, Kafka can now not only reliably and scalable transport events and messages through the Kafka broker but also analyse and process these event in real-time. Interestingly Kafka Streams does not provide its own cluster infrastructure and it is also not meant to run on a Kafka cluster. The idea is to run Kafka Streams where it makes sense, which can be inside a “normal” Java application, inside a Web container or on a more modern containerized (cloud) infrastructure, such as Mesos, Kubernetes or Docker. Kafka Streams has a lot of interesting features, such as reliable state handling, queryable state and much more. KSQL is a streaming engine for Apache Kafka, providing a simple and completely interactive SQL interface for processing data in Kafka.

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Kafka, Apache Kafka evolved from an enterprise messaging system to a fully distributed streaming data platform (Kafka Core + Kafka Connect + Kafka Streams) for building streaming data pipelines and streaming data applications. This talk, that I gave at the Chicago Java Users Group (CJUG) on June 8th 2017, is mainly focusing on Kafka Streams, a lightweight open source Java library for building stream processing applications on top of Kafka using Kafka topics as input/output. You will learn more about the following: 1. Apache Kafka: a Streaming Data Platform 2. Overview of Kafka Streams: Before Kafka Streams? What is Kafka Streams? Why Kafka Streams? What are Kafka Streams key concepts? Kafka Streams APIs and code examples? 3. Writing, deploying and running your first Kafka Streams application 4. Code and Demo of an end-to-end Kafka-based Streaming Data Application 5. Where to go from here?

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OSSNA Building Modern Data Streaming Apps https://ossna2023.sched.com/event/1Jt05/virtual-building-modern-data-streaming-apps-with-open-source-timothy-spann-streamnative Timothy Spann Cloudera Principal Developer Advocate Data in Motion In my session, I will show you some best practices I have discovered over the last seven years in building data streaming applications, including IoT, CDC, Logs, and more. In my modern approach, we utilize several open-source frameworks to maximize all the best features. We often start with Apache NiFi as the orchestrator of streams flowing into Apache Pulsar. From there, we build streaming ETL with Apache Spark and enhance events with Pulsar Functions for ML and enrichment. We make continuous queries against our topics with Flink SQL. We will stream data into various open-source data stores, including Apache Iceberg, Apache Pinot, and others. We use the best streaming tools for the current applications with the open source stack - FLiPN. https://www.flipn.app/ Updates: This will be in-person with live coding based on feedback from the crowd. This will also include new data stores, new sources, and data relevant to and from the Vancouver area. This will also include updates to the platforms and inclusion of Apache Iceberg, Apache Pinot and some other new tech. https://github.com/tspannhw/SpeakerProfile Tim Spann is a Principal Developer Advocate for Cloudera. He works with Apache Kafka, Apache Flink, Flink SQL, Apache NiFi, MiniFi, 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, messaging, streaming technologies, and Java programming. Previously, he was a Principal DataFlow Field Engineer at Cloudera, a Senior Solutions Engineer at Hortonworks, a Senior Solutions Architect at AirisData, a Senior Field Engineer at Pivotal and a Team Leader at HPE. He blogs for DZone, where he is the Big Data Zone leader, and runs a popular meetup in Princeton on Big Data, Cloud, IoT, deep learning, streaming, NiFi, the blockchain, and Spark. Tim is a frequent speaker at conferences such as ApacheCon, DeveloperWeek, Pulsar Summit and many more. He holds a BS and MS in computer science. Timothy J Spann Cloudera Principal Developer Advocate Hightstown, NJ Websitehttps://datainmotion.dev/

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Enterprise integration is more challenging than ever before. The IT evolution requires the integration of more and more technologies. Applications are deployed across the edge, hybrid, and multi-cloud architectures. Traditional middleware such as MQ, ETL, ESB does not scale well enough or only processes data in batch instead of real-time. This presentation explores why Apache Kafka is the new black for integration projects, how Kafka fits into the discussion around cloud-native iPaaS (Integration Platform as a Service) solutions, and why event streaming is a new software category. A concrete real-world example shows the difference between event streaming and traditional integration platforms respectively cloud-native iPaaS. Video Recording of this presentation: https://www.youtube.com/watch?v=I8yZwKg_IJc&t=2842s Blog post about this topic: https://www.kai-waehner.de/blog/2021/11/03/apache-kafka-cloud-native-ipaas-versus-mq-etl-esb-middleware/

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Leverage Kafka to build a stream processing platform

As a streaming platform, Apache Kafka provides low-latency, high-throughput, fault-tolerant publish and subscribe pipelines and excels at processing streams of real-time events. Kafka provides reliable, millisecond delivery for connecting downstream systems with real-time data. In this talk, we will show how easy it is to leverage Kafka and the Elasticsearch connector to keep your indices populated with the latest data from the rest of your enterprise, as it changes.

Integrating Apache Kafka and Elastic Using the Connect Framework

Presentation @ Oracle Code Berlin. Independent of the source of data, the integration of event streams into an Enterprise Architecture gets more and more important in the world of sensors, social media streams and Internet of Things. Events have to be accepted quickly and reliably, they have to be distributed and analysed, often with many consumers or systems interested in all or part of the events. How can we make sure that all these events are accepted and forwarded in an efficient and reliable way? This is where Apache Kafaka comes into play, a distirbuted, highly-scalable messaging broker, build for exchanging huge amounts of messages between a source and a target. This session will start with an introduction of Apache and presents the role of Apache Kafka in a modern data / information architecture and the advantages it brings to the table.

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Today, many companies are faced with a huge quantity of data and a wide variety of tools with which to process it. This potentially allows for great opportunities to satisfy customers’ needs and bring user experience to the next level. However, in order to achieve this and provide a competitive solution, sophisticated and complex data processing is needed. Such processing can rarely be done with one tool or framework — a number of tools are often involved, each having prowess in a particular field of the processing pipeline. In this session, we will see the latest endeavors of Apache Ignite to integrate with other big data platforms and provide its in-memory computing strengths for data processing pipelines. In particular we will have a closer look at how it can be integrated and used with Apache Kafka and/or Flume, and outline several use scenarios.

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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.

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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

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