Apache Kafka - A Distributed Streaming Platform

Speaker: Nick Dearden, Director of Engineering, Confluent We’ll discuss how to leverage some of the more advanced transformation capabilities available in both KSQL and Kafka Connect, including how to chain them together into powerful combinations for handling tasks such as data-masking, restructuring and aggregations. Using KSQL, you can deliver the streaming transformation capability easily and quickly. This is part 3 of 3 in Streaming ETL - The New Data Integration series. Watch the recording: https://videos.confluent.io/watch/en56Qt3KAdrpQ4JE5EZNHj?.

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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/

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Monitoring Apache Kafka with Confluent Control Center

Speaker: Gabriel Schenker, Lead Curriculum Developer, Confluent Streaming platforms have emerged as a popular, new trend, but what exactly is a streaming platform? Part messaging system, part Hadoop made fast, part fast ETL and scalable data integration. With Apache Kafka® at the core, event streaming platforms offer an entirely new perspective on managing the flow of data. This talk will explain what an event streaming platform such as Apache Kafka is and some of the use cases and design patterns around its use—including several examples of where it is solving real business problems. New developments in this area such as KSQL will also be discussed.

What is Apache Kafka and What is an Event Streaming Platform?

Presentation from South Bay.NET meetup on 3/30. Speaker: Matt Howlett, Software Engineer at Confluent Apache Kafka is a scalable streaming platform that forms a key part of the infrastructure at many companies including Uber, Netflix, Walmart, Airbnb, Goldman Sachs and LinkedIn. In this talk Matt will give a technical overview of Kafka, discuss some typical use cases (from surge pricing to fraud detection to web analytics) and show you how to use Kafka from within your C#/.NET applications.

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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.

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My talk at Strata Data Conference, London, May 2017. https://conferences.oreilly.com/strata/strata-eu/public/schedule/detail/57619 Abstract: Modern businesses have data at their core, but this data is changing continuously. How can you harness this torrent of information in real time? The answer: stream processing. The core platform for streaming data is Apache Kafka, and thousands of companies are using Kafka to transform and reshape their industries, including Netflix, Uber, PayPal, Airbnb, Goldman Sachs, Cisco, and Oracle. Unfortunately, today’s common architectures for real-time data processing at scale suffer from complexity: to succeed, many technologies 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 engineers would like to work and how we actually end up working in practice. Michael Noll explains how Apache Kafka helps you radically simplify your data processing architectures by building normal applications to serve your real-time processing needs rather than building clusters or similar special-purpose infrastructure—while still benefiting from properties typically associated exclusively with cluster technologies, like high scalability, distributed computing, and fault tolerance. Michael also covers Kafka’s Streams API, its abstractions for streams and tables, and its recently introduced interactive queries functionality. Along the way, Michael shares common use cases that demonstrate that stream processing in practice often requires database-like functionality and how Kafka allows you to bridge the worlds of streams and databases when implementing your own core business applications (for example, in the form of event-driven, containerized microservices). As you’ll see, Kafka makes such architectures equally viable for small-, medium-, and large-scale use cases.

Rethinking Stream Processing with Apache Kafka: Applications vs. Clusters, St...

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Can and should Apache Kafka replace a database? How long can and should I store data in Kafka? How can I query and process data in Kafka? These are common questions that come up more and more. This session explains the idea behind databases and different features like storage, queries, transactions, and processing to evaluate when Kafka is a good fit, and when it is not. The discussion includes different Kafka-native add-ons like Tiered Storage for long-term, cost-efficient storage, and ksqlDB as an event streaming database. The relation and trade-offs between Kafka and other databases are explored to complement each other instead of thinking about a replacement. This includes different options for pull and push-based bi-directional integration.

Can Apache Kafka Replace a Database? – The 2021 Update | Kai Waehner, Confluent

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Many enterprises have a large technical debt in legacy applications hosted in on-premises data centers. There is a strong desire to modernize and move to a cloud-based infrastructure, but the world won’t stop for you to transition. Existing applications need to be supported and enhanced; data from legacy platforms is required to make decisions that drive the business. On the other hand, data from cloud-based applications does not exist in a vacuum. Legacy applications need access to these cloud data sources and vice versa. Can an enterprise have it both ways? Can new applications be built in the cloud while existing applications are maintained in a private data center? Monsanto has adopted a cloud-first mentality—today most new development is focused on the cloud. However, this transition did not happen overnight. Chrix Finne and Bob Lehmann share their experience building and implementing a Kafka-based cross-data-center streaming platform to facilitate the move to the cloud—in the process, kick-starting Monsanto’s transition from batch to stream processing. Details include an overview of the challenges involved in transitioning to the cloud and a deep dive into the cross-data-center stream platform architecture, including best practices for running this architecture in production and a summary of the benefits seen after deploying this architecture.

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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.

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Agenda: Apache Kafka Ecosystem Kafka Streams as Foundation for KSQL Motivation for KSQL KSQL Concepts Live Demo #1 – Intro to KSQL KSQL Architecture Live Demo #2 - Clickstream Analysis Building a User Defined Function (Example: Machine Learning) Getting Started ### The rapidly expanding world of stream processing can be daunting, with new concepts such as various types of time semantics, windowed aggregates, changelogs, and programming frameworks to master. KSQL is an open-source, Apache 2.0 licensed streaming SQL engine on top of Apache Kafka which aims to simplify all this and make stream processing available to everyone. Even though it is simple to use, KSQL is built for mission-critical and scalable production deployments (using Kafka Streams under the hood). Benefits of using KSQL include No coding required; no additional analytics cluster needed; streams and tables as first-class constructs; access to the rich Kafka ecosystem. This session introduces the concepts and architecture of KSQL. Use cases such as Streaming ETL, Real-Time Stream Monitoring or Anomaly Detection are discussed. A live demo shows how to setup and use KSQL quickly and easily on top of your Kafka ecosystem.

KSQL Deep Dive - The Open Source Streaming Engine for Apache Kafka

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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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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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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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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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.

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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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This session discusses uses cases leveraging Apache Kafka open source ecosystem as streaming platform to process IoT data. See use cases, architectural alternatives and a live demo of how devices connect to Kafka via MQTT. Learn how to analyze the IoT data either natively on Kafka with Kafka Streams/KSQL, or on an external big data cluster like Spark, Flink or Elastic leveraging Kafka Connect, and how to leverage TensorFlow for Machine Learning. The focus is on connected cars / connected vehicles and V2X use cases respectively mobility services. A live demo shows how to build a cloud-native IoT infrastructure on Kubernetes to connect and process streaming data in real-time from 100.000 cars to do predictive maintenance at scale in real-time. Code for the live demo on Github: https://github.com/kaiwaehner/hivemq-mqtt-tensorflow-kafka-realtime-iot-machine-learning-training-inference

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Talk from JavaOne 2017: Apache Kafka + Kafka Streams for Scalable, Mission Critical Deep Learning. Intelligent real time applications are a game changer in any industry. Deep Learning is one of the hottest buzzwords in this area. New technologies like GPUs combined with elastic cloud infrastructure enable the sophisticated usage of artificial neural networks to add business value in real world scenarios. Tech giants use it e.g. for image recognition and speech translation. This session discusses some real-world scenarios from different industries to explain when and how traditional companies can leverage deep learning in real time applications. This session shows how to deploy Deep Learning models into real time applications to do predictions on new events. Apache Kafka will be used to execute analytic models in a highly scalable and performant way. The first part introduces the use cases and concepts behind Deep Learning. It discusses how to build Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN) and Autoencoders leveraging open source frameworks like TensorFlow, DeepLearning4J or H2O. The second part shows how to deploy the built analytic models to real time applications leveraging Apache Kafka as streaming platform and Apache Kafka’s Streams API to embed the intelligent business logic into any external application or microservice. Some further material around Apache Kafka and Machine Learning: - Blog Post: How to Build and Deploy Scalable Machine Learning in Production with Apache Kafka: https://www.confluent.io/blog/build-deploy-scalable-machine-learning-production-apache-kafka/ - Video: Build and Deploy Analytic Models with H2O.ai and Apache Kafka: https://www.youtube.com/watch?v=-q7CyIExBKM&feature=youtu.be - Code: Github Examples using Apache Kafka, TensorFlow, H2O, DeepLearning4J: https://github.com/kaiwaehner/kafka-streams-machine-learning-examples

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