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GSJUG: Mastering Data Streaming Pipelines 09May2023 https://www.meetup.com/futureofdata-princeton/events/293233881/ This is a repost from the Garden State Java Users Group Event. Join me at https://www.meetup.com/garden-state-java-user-group/events/293229660/ See: https://www.eventbrite.com/e/mastering-data-streaming-pipelines-tickets-627677218457?_ga=2.253257801.1787151623.1682868226-741104479.1678110925 Please note that registration via EventBrite is required to attend either in-person or online. We are happy to announce that Tim Spann will be our special guest for the May 9, 2023 meeting! Abstract: In this session, Tim will show you some best practices that he has discovered over the last seven years in building data streaming applications including IoT, CDC, Logs, and more. In his modern approach, we utilize several Apache frameworks to maximize the best features of all. We often start with Apache NiFi as the orchestrator of streams flowing into Apache Kafka. From there we build streaming ETL with Apache Flink, enhance events with NiFi enrichment. We build continuous queries against our topics with Flink SQL. We will show where Java fits in as sources, enrichments, NiFi processors and sinks. We hope to see you on May 9! Speaker Timothy Spann Tim Spann is a Principal Developer Advocate in Data In Motion for Cloudera. He works with Apache NiFi, Apache Pulsar, Apache Kafka, Apache Flink, Flink SQL, Apache Pinot, Trino, Apache Iceberg, DeltaLake, Apache Spark, Big Data, IoT, Cloud, AI/DL, machine learning, and deep learning. Tim has over ten years of experience with the IoT, big data, distributed computing, messaging, streaming technologies, and Java programming. Previously, he was a Developer Advocate at StreamNative, 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 & NYC 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. In this session, Tim will show you some best practices that he has discovered over the last seven years in building data streaming applications, including IoT, CDC, Logs, and more. In his modern approach, we utilize several Apache frameworks to maximize the best features of all. We often start with Apache NiFi as the orchestrator of streams flowing into Apache Kafka. From there, we build streaming ETL with Apache Flink, enhance events with NiFi enrichment. We build continuous queries against our topics with Flink SQL. We will show where Java fits in as sources, enrichments, NiFi processors, and sinks. https://www.eventbrite.com/e/mastering-data-streaming-pipelines-tickets-627677218457?_ga=2.253257801.178

GSJUG: Mastering Data Streaming Pipelines 09May2023

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Apache kafka

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Why is Kafka so fast? Why is Kafka so popular? Why Kafka? This slide deck is a tutorial for the Kafka streaming platform. This slide deck covers Kafka Architecture with some small examples from the command line. Then we expand on this with a multi-server example to demonstrate failover of brokers as well as consumers. Then it goes through some simple Java client examples for a Kafka Producer and a Kafka Consumer. We have also expanded on the Kafka design section and added references. The tutorial covers Avro and the Schema Registry as well as advance Kafka Producers.

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If you were to ask any developer, ""what's a schema and where is it used?"" Most likely, you'd get an answer involving a relational database. The truth is the domain objects used in applications represent a contract, an implied schema, whether developers choose to acknowledge them or not. But even if you recognize the need for a formal schema, what's the best way to manage them? This presentation will contain some theory and primarily practical application for schemas with Schema Registry. I'll briefly explain what a schema is and how it's very relevant to any application working with Kafka today. It will go into the practical, introducing Schema Registry, describing how it works and how developers can leverage it to provide schemas across an organization. The discussion will cover working with Schema Registry from the command line, how to leverage it with Kafka clients, and the supported serialization formats. Some established build tools that make life easier for the Kafka developer will also be covered. Attendees will walk away with knowledge of Schema Registry and a solid understanding of how it works, how to integrate them into Kafka clients. They'll also learn enough about the supported serialization frameworks to start implementing schemas right away in their Kafka development efforts.

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(Gaurav Goyal + Dharmesh Panchmatia, Cisco Systems) Kafka Summit SF 2018 Cisco e-commerce platform is a custom-built mission-critical platform which accounts for $40+ billion of Cisco’s revenue annually. It’s a suite of 35 different applications and 300+ services that powers product configuration, pricing, quoting and order booking across all Cisco product lines including hardware, software, services and subscriptions. It’s a B2B platform used by the Cisco sales team, partners and direct customers, serving 140,000 unique users across the globe. In order to improve customer experience and business agility, Cisco decided to transition the platform to cloud-native technologies, MongoDB, Elasticsearch and Kafka. In this session, we will share details around: -Kafka architecture -How we are experiencing significant resiliency advantages, zero-downtime deployment and improved performance -How we’ve implemented Kafka to pass data to 20+ downstream applications, removing point-to-point integrations, batch jobs and standardizing the handshake -How are we using Kafka for pushing data for machine learning and analytics use cases -Best practices and lessons learned

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(Gunnar Morling, RedHat) Kafka Summit SF 2018 Debezium (noun | de·be·zi·um | /dɪ:ˈbɪ:ziːəm/): secret sauce for change data capture (CDC) streaming changes from your datastore that enables you to solve multiple challenges: synchronizing data between microservices, gradually extracting microservices from existing monoliths, maintaining different read models in CQRS-style architectures, updating caches and full-text indexes and feeding operational data to your analytics tools Join this session to learn what CDC is about, how it can be implemented using Debezium, an open source CDC solution based on Apache Kafka and how it can be utilized for your microservices. Find out how Debezium captures all the changes from datastores such as MySQL, PostgreSQL and MongoDB, how to react to the change events in near real time and how Debezium is designed to not compromise on data correctness and completeness also if things go wrong. In a live demo we’ll show how to set up a change data stream out of your application’s database without any code changes needed. You’ll see how to sink the change events into other databases and how to push data changes to your clients using WebSockets.

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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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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.

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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

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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.

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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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“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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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.

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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

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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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BY Jun Rao From the Bay Area Apache Kafka September 2016 Meetup. Abstract: To manage the ever-increasing volume and velocity of data within your company you have successfully made the transition from single machines and one-off solutions to large, distributed stream infrastructures in your data center powered by Apache Kafka. But what needs to be done if one data center is not enough? In this session we describe building resilient data pipelines with Apache Kafka that span multiple data centers and points of presence. We provide an overview of best practices and common patterns while covering key areas such as architecture guidelines, data replication and mirroring as well as disaster scenarios and failure handling.

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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.

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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.

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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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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.

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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?

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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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