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| confluent kafka architecture diagram: Kafka: The Definitive Guide Neha Narkhede, Gwen Shapira, Todd Palino, 2017-08-31 Every enterprise application creates data, whether it’s log messages, metrics, user activity, outgoing messages, or something else. And how to move all of this data becomes nearly as important as the data itself. If you’re an application architect, developer, or production engineer new to Apache Kafka, this practical guide shows you how to use this open source streaming platform to handle real-time data feeds. Engineers from Confluent and LinkedIn who are responsible for developing Kafka explain how to deploy production Kafka clusters, write reliable event-driven microservices, and build scalable stream-processing applications with this platform. Through detailed examples, you’ll learn Kafka’s design principles, reliability guarantees, key APIs, and architecture details, including the replication protocol, the controller, and the storage layer. Understand publish-subscribe messaging and how it fits in the big data ecosystem. Explore Kafka producers and consumers for writing and reading messages Understand Kafka patterns and use-case requirements to ensure reliable data delivery Get best practices for building data pipelines and applications with Kafka Manage Kafka in production, and learn to perform monitoring, tuning, and maintenance tasks Learn the most critical metrics among Kafka’s operational measurements Explore how Kafka’s stream delivery capabilities make it a perfect source for stream processing systems |
| confluent kafka architecture diagram: Kafka Streams in Action Bill Bejeck, 2018-08-29 Summary Kafka Streams in Action teaches you everything you need to know to implement stream processing on data flowing into your Kafka platform, allowing you to focus on getting more from your data without sacrificing time or effort. Foreword by Neha Narkhede, Cocreator of Apache Kafka Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Not all stream-based applications require a dedicated processing cluster. The lightweight Kafka Streams library provides exactly the power and simplicity you need for message handling in microservices and real-time event processing. With the Kafka Streams API, you filter and transform data streams with just Kafka and your application. About the Book Kafka Streams in Action teaches you to implement stream processing within the Kafka platform. In this easy-to-follow book, you'll explore real-world examples to collect, transform, and aggregate data, work with multiple processors, and handle real-time events. You'll even dive into streaming SQL with KSQL! Practical to the very end, it finishes with testing and operational aspects, such as monitoring and debugging. What's inside Using the KStreams API Filtering, transforming, and splitting data Working with the Processor API Integrating with external systems About the Reader Assumes some experience with distributed systems. No knowledge of Kafka or streaming applications required. About the Author Bill Bejeck is a Kafka Streams contributor and Confluent engineer with over 15 years of software development experience. Table of Contents PART 1 - GETTING STARTED WITH KAFKA STREAMS Welcome to Kafka Streams Kafka quicklyPART 2 - KAFKA STREAMS DEVELOPMENT Developing Kafka Streams Streams and state The KTable API The Processor APIPART 3 - ADMINISTERING KAFKA STREAMS Monitoring and performance Testing a Kafka Streams applicationPART 4 - ADVANCED CONCEPTS WITH KAFKA STREAMS Advanced applications with Kafka StreamsAPPENDIXES Appendix A - Additional configuration information Appendix B - Exactly once semantics |
| confluent kafka architecture diagram: Mastering Kafka Streams and ksqlDB Mitch Seymour, 2021-02-04 Working with unbounded and fast-moving data streams has historically been difficult. But with Kafka Streams and ksqlDB, building stream processing applications is easy and fun. This practical guide shows data engineers how to use these tools to build highly scalable stream processing applications for moving, enriching, and transforming large amounts of data in real time. Mitch Seymour, data services engineer at Mailchimp, explains important stream processing concepts against a backdrop of several interesting business problems. You'll learn the strengths of both Kafka Streams and ksqlDB to help you choose the best tool for each unique stream processing project. Non-Java developers will find the ksqlDB path to be an especially gentle introduction to stream processing. Learn the basics of Kafka and the pub/sub communication pattern Build stateless and stateful stream processing applications using Kafka Streams and ksqlDB Perform advanced stateful operations, including windowed joins and aggregations Understand how stateful processing works under the hood Learn about ksqlDB's data integration features, powered by Kafka Connect Work with different types of collections in ksqlDB and perform push and pull queries Deploy your Kafka Streams and ksqlDB applications to production |
| confluent kafka architecture diagram: Building Data Streaming Applications with Apache Kafka Manish Kumar, Chanchal Singh, 2017-08-18 Design and administer fast, reliable enterprise messaging systems with Apache Kafka About This Book Build efficient real-time streaming applications in Apache Kafka to process data streams of data Master the core Kafka APIs to set up Apache Kafka clusters and start writing message producers and consumers A comprehensive guide to help you get a solid grasp of the Apache Kafka concepts in Apache Kafka with pracitcalpractical examples Who This Book Is For If you want to learn how to use Apache Kafka and the different tools in the Kafka ecosystem in the easiest possible manner, this book is for you. Some programming experience with Java is required to get the most out of this book What You Will Learn Learn the basics of Apache Kafka from scratch Use the basic building blocks of a streaming application Design effective streaming applications with Kafka using Spark, Storm &, and Heron Understand the importance of a low -latency , high- throughput, and fault-tolerant messaging system Make effective capacity planning while deploying your Kafka Application Understand and implement the best security practices In Detail Apache Kafka is a popular distributed streaming platform that acts as a messaging queue or an enterprise messaging system. It lets you publish and subscribe to a stream of records, and process them in a fault-tolerant way as they occur. This book is a comprehensive guide to designing and architecting enterprise-grade streaming applications using Apache Kafka and other big data tools. It includes best practices for building such applications, and tackles some common challenges such as how to use Kafka efficiently and handle high data volumes with ease. This book first takes you through understanding the type messaging system and then provides a thorough introduction to Apache Kafka and its internal details. The second part of the book takes you through designing streaming application using various frameworks and tools such as Apache Spark, Apache Storm, and more. Once you grasp the basics, we will take you through more advanced concepts in Apache Kafka such as capacity planning and security. By the end of this book, you will have all the information you need to be comfortable with using Apache Kafka, and to design efficient streaming data applications with it. Style and approach A step-by –step, comprehensive guide filled with practical and real- world examples |
| confluent kafka architecture diagram: Gradle Beyond the Basics Tim Berglund, 2013-07-16 If you’re familiar with Gradle’s basics elements—possibly through the author’s previous O’Reilly book, Building and Testing with Gradle—this more advanced guide provides the recipes, techniques, and syntax to help you master this build automation tool. With clear, concise explanations and lots of ready-to-use code examples, you’ll explore four discrete areas of Gradle functionality: file operations, custom Gradle plugins, build lifecycle hooks, and dependency management. Learn how to use Gradle’s rich set of APIs and Groovy-based Domain Specific Language to customize build software that actually conforms to your product. By using the techniques in this book, you’ll be able to write domain-specific builds that support every other line of code your team creates. Examine Gradle’s file API, including copy tasks, pattern matching, content filtering, and the FileCollection interface Understand the process for building and packaging a custom Gradle plug-in Manage build complexity with hook methods and Gradle’s rule feature Learn how Gradle handles dependency management natively and through customization Explore Gradle’s core plug-ins as well as key examples from the Gradle community |
| confluent kafka architecture diagram: Designing Data-Intensive Applications Martin Kleppmann, 2017-03-16 Data is at the center of many challenges in system design today. Difficult issues need to be figured out, such as scalability, consistency, reliability, efficiency, and maintainability. In addition, we have an overwhelming variety of tools, including relational databases, NoSQL datastores, stream or batch processors, and message brokers. What are the right choices for your application? How do you make sense of all these buzzwords? In this practical and comprehensive guide, author Martin Kleppmann helps you navigate this diverse landscape by examining the pros and cons of various technologies for processing and storing data. Software keeps changing, but the fundamental principles remain the same. With this book, software engineers and architects will learn how to apply those ideas in practice, and how to make full use of data in modern applications. Peer under the hood of the systems you already use, and learn how to use and operate them more effectively Make informed decisions by identifying the strengths and weaknesses of different tools Navigate the trade-offs around consistency, scalability, fault tolerance, and complexity Understand the distributed systems research upon which modern databases are built Peek behind the scenes of major online services, and learn from their architectures |
| confluent kafka architecture diagram: Enterprise Integration Patterns Gregor Hohpe, Bobby Woolf, 2012-03-09 Enterprise Integration Patterns provides an invaluable catalog of sixty-five patterns, with real-world solutions that demonstrate the formidable of messaging and help you to design effective messaging solutions for your enterprise. The authors also include examples covering a variety of different integration technologies, such as JMS, MSMQ, TIBCO ActiveEnterprise, Microsoft BizTalk, SOAP, and XSL. A case study describing a bond trading system illustrates the patterns in practice, and the book offers a look at emerging standards, as well as insights into what the future of enterprise integration might hold. This book provides a consistent vocabulary and visual notation framework to describe large-scale integration solutions across many technologies. It also explores in detail the advantages and limitations of asynchronous messaging architectures. The authors present practical advice on designing code that connects an application to a messaging system, and provide extensive information to help you determine when to send a message, how to route it to the proper destination, and how to monitor the health of a messaging system. If you want to know how to manage, monitor, and maintain a messaging system once it is in use, get this book. |
| confluent kafka architecture diagram: Kafka in Action Dylan Scott, Viktor Gamov, Dave Klein, 2022-03-22 Master the wicked-fast Apache Kafka streaming platform through hands-on examples and real-world projects. In Kafka in Action you will learn: Understanding Apache Kafka concepts Setting up and executing basic ETL tasks using Kafka Connect Using Kafka as part of a large data project team Performing administrative tasks Producing and consuming event streams Working with Kafka from Java applications Implementing Kafka as a message queue Kafka in Action is a fast-paced introduction to every aspect of working with Apache Kafka. Starting with an overview of Kafka's core concepts, you'll immediately learn how to set up and execute basic data movement tasks and how to produce and consume streams of events. Advancing quickly, you’ll soon be ready to use Kafka in your day-to-day workflow, and start digging into even more advanced Kafka topics. About the technology Think of Apache Kafka as a high performance software bus that facilitates event streaming, logging, analytics, and other data pipeline tasks. With Kafka, you can easily build features like operational data monitoring and large-scale event processing into both large and small-scale applications. About the book Kafka in Action introduces the core features of Kafka, along with relevant examples of how to use it in real applications. In it, you’ll explore the most common use cases such as logging and managing streaming data. When you’re done, you’ll be ready to handle both basic developer- and admin-based tasks in a Kafka-focused team. What's inside Kafka as an event streaming platform Kafka producers and consumers from Java applications Kafka as part of a large data project About the reader For intermediate Java developers or data engineers. No prior knowledge of Kafka required. About the author Dylan Scott is a software developer in the insurance industry. Viktor Gamov is a Kafka-focused developer advocate. At Confluent, Dave Klein helps developers, teams, and enterprises harness the power of event streaming with Apache Kafka. Table of Contents PART 1 GETTING STARTED 1 Introduction to Kafka 2 Getting to know Kafka PART 2 APPLYING KAFK 3 Designing a Kafka project 4 Producers: Sourcing data 5 Consumers: Unlocking data 6 Brokers 7 Topics and partitions 8 Kafka storage 9 Management: Tools and logging PART 3 GOING FURTHER 10 Protecting Kafka 11 Schema registry 12 Stream processing with Kafka Streams and ksqlDB |
| confluent kafka architecture diagram: Effective Kafka Emil Koutanov, 2020-03-17 The software architecture landscape has evolved dramatically over the past decade. Microservices have displaced monoliths. Data and applications are increasingly becoming distributed and decentralised. But composing disparate systems is a hard problem. More recently, software practitioners have been rapidly converging on event-driven architecture as a sustainable way of dealing with complexity - integrating systems without increasing their coupling.In Effective Kafka, Emil Koutanov explores the fundamentals of Event-Driven Architecture - using Apache Kafka - the world's most popular and supported open-source event streaming platform.You'll learn: - The fundamentals of event-driven architecture and event streaming platforms- The background and rationale behind Apache Kafka, its numerous potential uses and applications- The architecture and core concepts - the underlying software components, partitioning and parallelism, load-balancing, record ordering and consistency modes- Installation of Kafka and related tooling - using standalone deployments, clusters, and containerised deployments with Docker- Using CLI tools to interact with and administer Kafka classes, as well as publishing data and browsing topics- Using third-party web-based tools for monitoring a cluster and gaining insights into the event streams- Building stream processing applications in Java 11 using off-the-shelf client libraries- Patterns and best-practice for organising the application architecture, with emphasis on maintainability and testability of the resulting code- The numerous gotchas that lurk in Kafka's client and broker configuration, and how to counter them- Theoretical background on distributed and concurrent computing, exploring factors affecting their liveness and safety- Best-practices for running multi-tenanted clusters across diverse engineering teams, how teams collaborate to build complex systems at scale and equitably share the cluster with the aid of quotas- Operational aspects of running Kafka clusters at scale, performance tuning and methods for optimising network and storage utilisation- All aspects of Kafka security -including network segregation, encryption, certificates, authentication and authorization.The coverage is progressively delivered and carefully aimed at giving you a journey-like experience into becoming proficient with Apache Kafka and Event-Driven Architecture. The goal is to get you designing and building applications. And by the conclusion of this book, you will be a confident practitioner and a Kafka evangelist within your organisation - wielding the knowledge necessary to teach others. |
| confluent kafka architecture diagram: I Heart Logs Jay Kreps, 2014-09-23 Why a book about logs? That’s easy: the humble log is an abstraction that lies at the heart of many systems, from NoSQL databases to cryptocurrencies. Even though most engineers don’t think much about them, this short book shows you why logs are worthy of your attention. Based on his popular blog posts, LinkedIn principal engineer Jay Kreps shows you how logs work in distributed systems, and then delivers practical applications of these concepts in a variety of common uses—data integration, enterprise architecture, real-time stream processing, data system design, and abstract computing models. Go ahead and take the plunge with logs; you’re going love them. Learn how logs are used for programmatic access in databases and distributed systems Discover solutions to the huge data integration problem when more data of more varieties meet more systems Understand why logs are at the heart of real-time stream processing Learn the role of a log in the internals of online data systems Explore how Jay Kreps applies these ideas to his own work on data infrastructure systems at LinkedIn |
| confluent kafka architecture diagram: Data Lake for Enterprises Tomcy John, Pankaj Misra, 2017-05-31 A practical guide to implementing your enterprise data lake using Lambda Architecture as the base About This Book Build a full-fledged data lake for your organization with popular big data technologies using the Lambda architecture as the base Delve into the big data technologies required to meet modern day business strategies A highly practical guide to implementing enterprise data lakes with lots of examples and real-world use-cases Who This Book Is For Java developers and architects who would like to implement a data lake for their enterprise will find this book useful. If you want to get hands-on experience with the Lambda Architecture and big data technologies by implementing a practical solution using these technologies, this book will also help you. What You Will Learn Build an enterprise-level data lake using the relevant big data technologies Understand the core of the Lambda architecture and how to apply it in an enterprise Learn the technical details around Sqoop and its functionalities Integrate Kafka with Hadoop components to acquire enterprise data Use flume with streaming technologies for stream-based processing Understand stream- based processing with reference to Apache Spark Streaming Incorporate Hadoop components and know the advantages they provide for enterprise data lakes Build fast, streaming, and high-performance applications using ElasticSearch Make your data ingestion process consistent across various data formats with configurability Process your data to derive intelligence using machine learning algorithms In Detail The term Data Lake has recently emerged as a prominent term in the big data industry. Data scientists can make use of it in deriving meaningful insights that can be used by businesses to redefine or transform the way they operate. Lambda architecture is also emerging as one of the very eminent patterns in the big data landscape, as it not only helps to derive useful information from historical data but also correlates real-time data to enable business to take critical decisions. This book tries to bring these two important aspects — data lake and lambda architecture—together. This book is divided into three main sections. The first introduces you to the concept of data lakes, the importance of data lakes in enterprises, and getting you up-to-speed with the Lambda architecture. The second section delves into the principal components of building a data lake using the Lambda architecture. It introduces you to popular big data technologies such as Apache Hadoop, Spark, Sqoop, Flume, and ElasticSearch. The third section is a highly practical demonstration of putting it all together, and shows you how an enterprise data lake can be implemented, along with several real-world use-cases. It also shows you how other peripheral components can be added to the lake to make it more efficient. By the end of this book, you will be able to choose the right big data technologies using the lambda architectural patterns to build your enterprise data lake. Style and approach The book takes a pragmatic approach, showing ways to leverage big data technologies and lambda architecture to build an enterprise-level data lake. |
| confluent kafka architecture diagram: Building Event-Driven Microservices Adam Bellemare, 2020-07-02 Organizations today often struggle to balance business requirements with ever-increasing volumes of data. Additionally, the demand for leveraging large-scale, real-time data is growing rapidly among the most competitive digital industries. Conventional system architectures may not be up to the task. With this practical guide, you’ll learn how to leverage large-scale data usage across the business units in your organization using the principles of event-driven microservices. Author Adam Bellemare takes you through the process of building an event-driven microservice-powered organization. You’ll reconsider how data is produced, accessed, and propagated across your organization. Learn powerful yet simple patterns for unlocking the value of this data. Incorporate event-driven design and architectural principles into your own systems. And completely rethink how your organization delivers value by unlocking near-real-time access to data at scale. You’ll learn: How to leverage event-driven architectures to deliver exceptional business value The role of microservices in supporting event-driven designs Architectural patterns to ensure success both within and between teams in your organization Application patterns for developing powerful event-driven microservices Components and tooling required to get your microservice ecosystem off the ground |
| confluent kafka architecture diagram: Streaming Systems Tyler Akidau, Slava Chernyak, Reuven Lax, 2018-07-16 Streaming data is a big deal in big data these days. As more and more businesses seek to tame the massive unbounded data sets that pervade our world, streaming systems have finally reached a level of maturity sufficient for mainstream adoption. With this practical guide, data engineers, data scientists, and developers will learn how to work with streaming data in a conceptual and platform-agnostic way. Expanded from Tyler Akidau’s popular blog posts Streaming 101 and Streaming 102, this book takes you from an introductory level to a nuanced understanding of the what, where, when, and how of processing real-time data streams. You’ll also dive deep into watermarks and exactly-once processing with co-authors Slava Chernyak and Reuven Lax. You’ll explore: How streaming and batch data processing patterns compare The core principles and concepts behind robust out-of-order data processing How watermarks track progress and completeness in infinite datasets How exactly-once data processing techniques ensure correctness How the concepts of streams and tables form the foundations of both batch and streaming data processing The practical motivations behind a powerful persistent state mechanism, driven by a real-world example How time-varying relations provide a link between stream processing and the world of SQL and relational algebra |
| confluent kafka architecture diagram: Enterprise Application Architecture with .NET Core Ganesan Senthilvel, Ovais Mehboob Ahmed Khan, Habib Ahmed Qureshi, 2017-04-25 Architect and design highly scalable, robust, clean and highly performant applications in .NET Core About This Book Incorporate architectural soft-skills such as DevOps and Agile methodologies to enhance program-level objectives Gain knowledge of architectural approaches on the likes of SOA architecture and microservices to provide traceability and rationale for architectural decisions Explore a variety of practical use cases and code examples to implement the tools and techniques described in the book Who This Book Is For This book is for experienced .NET developers who are aspiring to become architects of enterprise-grade applications, as well as software architects who would like to leverage .NET to create effective blueprints of applications. What You Will Learn Grasp the important aspects and best practices of application lifecycle management Leverage the popular ALM tools, application insights, and their usage to monitor performance, testability, and optimization tools in an enterprise Explore various authentication models such as social media-based authentication, 2FA and OpenID Connect, learn authorization techniques Explore Azure with various solution approaches for Microservices and Serverless architecture along with Docker containers Gain knowledge about the recent market trends and practices and how they can be achieved with .NET Core and Microsoft tools and technologies In Detail If you want to design and develop enterprise applications using .NET Core as the development framework and learn about industry-wide best practices and guidelines, then this book is for you. The book starts with a brief introduction to enterprise architecture, which will help you to understand what enterprise architecture is and what the key components are. It will then teach you about the types of patterns and the principles of software development, and explain the various aspects of distributed computing to keep your applications effective and scalable. These chapters act as a catalyst to start the practical implementation, and design and develop applications using different architectural approaches, such as layered architecture, service oriented architecture, microservices and cloud-specific solutions. Gradually, you will learn about the different approaches and models of the Security framework and explore various authentication models and authorization techniques, such as social media-based authentication and safe storage using app secrets. By the end of the book, you will get to know the concepts and usage of the emerging fields, such as DevOps, BigData, architectural practices, and Artificial Intelligence. Style and approach Filled with examples and use cases, this guide takes a no-nonsense approach to show you the best tools and techniques required to become a successful software architect. |
| confluent kafka architecture diagram: The Digital Journey of Banking and Insurance, Volume III Volker Liermann, Claus Stegmann, 2021-10-27 This book, the third one of three volumes, focuses on data and the actions around data, like storage and processing. The angle shifts over the volumes from a business-driven approach in “Disruption and DNA” to a strong technical focus in “Data Storage, Processing and Analysis”, leaving “Digitalization and Machine Learning Applications” with the business and technical aspects in-between. In the last volume of the series, “Data Storage, Processing and Analysis”, the shifts in the way we deal with data are addressed. |
| confluent kafka architecture diagram: Apache Camel Developer's Cookbook Scott Cranton, Jakub Korab, 2013-12-26 This book is written in a Cookbook style with short recipes showing developers how to effectively implement EIP without breaking everything in the process. It is concise and to the point, and it helps developers get their data flowing between different components without the need to read through page upon page of theory, while also enabling the reader to learn how to create exciting new projects. Camel Enterprise Integration Cookbook is intended for developers who have some familiarity with Apache Camel and who want a quick lookup reference to practical, proven tips on how to perform common tasks. Every recipe also includes a summary and reference pointers for more details that make it easy for you to get a deeper understanding of the Apache Camel capabilities that you will use day to day. |
| confluent kafka architecture diagram: Data Mesh Zhamak Dehghani, 2022-03-08 Many enterprises are investing in a next-generation data lake, hoping to democratize data at scale to provide business insights and ultimately make automated intelligent decisions. In this practical book, author Zhamak Dehghani reveals that, despite the time, money, and effort poured into them, data warehouses and data lakes fail when applied at the scale and speed of today's organizations. A distributed data mesh is a better choice. Dehghani guides architects, technical leaders, and decision makers on their journey from monolithic big data architecture to a sociotechnical paradigm that draws from modern distributed architecture. A data mesh considers domains as a first-class concern, applies platform thinking to create self-serve data infrastructure, treats data as a product, and introduces a federated and computational model of data governance. This book shows you why and how. Examine the current data landscape from the perspective of business and organizational needs, environmental challenges, and existing architectures Analyze the landscape's underlying characteristics and failure modes Get a complete introduction to data mesh principles and its constituents Learn how to design a data mesh architecture Move beyond a monolithic data lake to a distributed data mesh. |
| confluent kafka architecture diagram: Foundations for Architecting Data Solutions Ted Malaska, Jonathan Seidman, 2018-08-29 While many companies ponder implementation details such as distributed processing engines and algorithms for data analysis, this practical book takes a much wider view of big data development, starting with initial planning and moving diligently toward execution. Authors Ted Malaska and Jonathan Seidman guide you through the major components necessary to start, architect, and develop successful big data projects. Everyone from CIOs and COOs to lead architects and developers will explore a variety of big data architectures and applications, from massive data pipelines to web-scale applications. Each chapter addresses a piece of the software development life cycle and identifies patterns to maximize long-term success throughout the life of your project. Start the planning process by considering the key data project types Use guidelines to evaluate and select data management solutions Reduce risk related to technology, your team, and vague requirements Explore system interface design using APIs, REST, and pub/sub systems Choose the right distributed storage system for your big data system Plan and implement metadata collections for your data architecture Use data pipelines to ensure data integrity from source to final storage Evaluate the attributes of various engines for processing the data you collect |
| confluent kafka architecture diagram: The Allure of Machinic Life John Johnston, 2008 An account of the creation of new forms of life and intelligence in cybernetics, artificial life, and artificial intelligence that analyzes both the similarities and the differences among these sciences in actualizing life.The Allure of Machinic Life |
| confluent kafka architecture diagram: Data Management at Scale Piethein Strengholt, 2020-07-29 As data management and integration continue to evolve rapidly, storing all your data in one place, such as a data warehouse, is no longer scalable. In the very near future, data will need to be distributed and available for several technological solutions. With this practical book, you’ll learnhow to migrate your enterprise from a complex and tightly coupled data landscape to a more flexible architecture ready for the modern world of data consumption. Executives, data architects, analytics teams, and compliance and governance staff will learn how to build a modern scalable data landscape using the Scaled Architecture, which you can introduce incrementally without a large upfront investment. Author Piethein Strengholt provides blueprints, principles, observations, best practices, and patterns to get you up to speed. Examine data management trends, including technological developments, regulatory requirements, and privacy concerns Go deep into the Scaled Architecture and learn how the pieces fit together Explore data governance and data security, master data management, self-service data marketplaces, and the importance of metadata |
| confluent kafka architecture diagram: Enterprise Integration Patterns Gregor Hohpe, 2003 |
| confluent kafka architecture diagram: Cassandra: The Definitive Guide Jeff Carpenter, Eben Hewitt, 2016-06-29 Imagine what you could do if scalability wasn't a problem. With this hands-on guide, you’ll learn how the Cassandra database management system handles hundreds of terabytes of data while remaining highly available across multiple data centers. This expanded second edition—updated for Cassandra 3.0—provides the technical details and practical examples you need to put this database to work in a production environment. Authors Jeff Carpenter and Eben Hewitt demonstrate the advantages of Cassandra’s non-relational design, with special attention to data modeling. If you’re a developer, DBA, or application architect looking to solve a database scaling issue or future-proof your application, this guide helps you harness Cassandra’s speed and flexibility. Understand Cassandra’s distributed and decentralized structure Use the Cassandra Query Language (CQL) and cqlsh—the CQL shell Create a working data model and compare it with an equivalent relational model Develop sample applications using client drivers for languages including Java, Python, and Node.js Explore cluster topology and learn how nodes exchange data Maintain a high level of performance in your cluster Deploy Cassandra on site, in the Cloud, or with Docker Integrate Cassandra with Spark, Hadoop, Elasticsearch, Solr, and Lucene |
| confluent kafka architecture diagram: Flow Architectures James Urquhart, 2021-01-06 Software development today is embracing events and streaming data, which optimizes not only how technology interacts but also how businesses integrate with one another to meet customer needs. This phenomenon, called flow, consists of patterns and standards that determine which activity and related data is communicated between parties over the internet. This book explores critical implications of that evolution: What happens when events and data streams help you discover new activity sources to enhance existing businesses or drive new markets? What technologies and architectural patterns can position your company for opportunities enabled by flow? James Urquhart, global field CTO at VMware, guides enterprise architects, software developers, and product managers through the process. Learn the benefits of flow dynamics when businesses, governments, and other institutions integrate via events and data streams Understand the value chain for flow integration through Wardley mapping visualization and promise theory modeling Walk through basic concepts behind today's event-driven systems marketplace Learn how today's integration patterns will influence the real-time events flow in the future Explore why companies should architect and build software today to take advantage of flow in coming years |
| confluent kafka architecture diagram: Designing Cloud Data Platforms Danil Zburivsky, Lynda Partner, 2021-04-20 Centralized data warehouses, the long-time defacto standard for housing data for analytics, are rapidly giving way to multi-faceted cloud data platforms. Companies that embrace modern cloud data platforms benefit from an integrated view of their business using all of their data and can take advantage of advanced analytic practices to drive predictions and as yet unimagined data services. Designing Cloud Data Platforms is an hands-on guide to envisioning and designing a modern scalable data platform that takes full advantage of the flexibility of the cloud. As you read, you''ll learn the core components of a cloud data platform design, along with the role of key technologies like Spark and Kafka Streams. You''ll also explore setting up processes to manage cloud-based data, keep it secure, and using advanced analytic and BI tools to analyse it. about the technology Access to affordable, dependable, serverless cloud services has revolutionized the way organizations can approach data management, and companies both big and small are raring to migrate to the cloud. But without a properly designed data platform, data in the cloud can remain just as siloed and inaccessible as it is today for most organizations. Designing Cloud Data Platforms lays out the principles of a well-designed platform that uses the scalable resources of the public cloud to manage all of an organization''s data, and present it as useful business insights. about the book In Designing Cloud Data Platforms, you''ll learn how to integrate data from multiple sources into a single, cloud-based, modern data platform. Drawing on their real-world experiences designing cloud data platforms for dozens of organizations, cloud data experts Danil Zburivsky and Lynda Partner take you through a six-layer approach to creating cloud data platforms that maximizes flexibility and manageability and reduces costs. Starting with foundational principles, you''ll learn how to get data into your platform from different databases, files, and APIs, the essential practices for organizing and processing that raw data, and how to best take advantage of the services offered by major cloud vendors. As you progress past the basics you''ll take a deep dive into advanced topics to get the most out of your data platform, including real-time data management, machine learning analytics, schema management, and more. what''s inside The tools of different public cloud for implementing data platforms Best practices for managing structured and unstructured data sets Machine learning tools that can be used on top of the cloud Cost optimization techniques about the reader For data professionals familiar with the basics of cloud computing and distributed data processing systems like Hadoop and Spark. about the authors Danil Zburivsky has over 10 years experience designing and supporting large-scale data infrastructure for enterprises across the globe. Lynda Partner is the VP of Analytics-as-a-Service at Pythian, and has been on the business side of data for over 20 years. |
| confluent kafka architecture diagram: SAP HANA 2.0 Denys Van Kempen, 2019 Enter the fast-paced world of SAP HANA 2.0 with this introductory guide. Begin with an exploration of the technological backbone of SAP HANA as a database and platform. Then, step into key SAP HANA user roles and discover core capabilities for administration, application development, advanced analytics, security, data integration, and more. No matter how SAP HANA 2.0 fits into your business, this book is your starting point. In this book, you'll learn about: a. Technology Discover what makes an in-memory database platform. Learn about SAP HANA's journey from version 1.0 to 2.0, take a tour of your technology options, and walk through deployment scenarios and implementation requirements. b. Tools Unpack your SAP HANA toolkit. See essential tools in action, from SAP HANA cockpit and SAP HANA studio, to the SAP HANA Predictive Analytics Library and SAP HANA smart data integration. c. Key Roles Understand how to use SAP HANA as a developer, administrator, data scientist, data center architect, and more. Explore key tasks like backend programming with SQLScript, security setup with roles and authorizations, data integration with the SAP HANA Data Management Suite, and more. Highlights include: 1) Architecture 2) Administration 3) Application development 4) Analytics 5) Security 6) Data integration 7) Data architecture 8) Data center |
| confluent kafka architecture diagram: Hadoop Application Architectures Mark Grover, Ted Malaska, Jonathan Seidman, Gwen Shapira, 2015-06-30 Get expert guidance on architecting end-to-end data management solutions with Apache Hadoop. While many sources explain how to use various components in the Hadoop ecosystem, this practical book takes you through architectural considerations necessary to tie those components together into a complete tailored application, based on your particular use case. To reinforce those lessons, the book’s second section provides detailed examples of architectures used in some of the most commonly found Hadoop applications. Whether you’re designing a new Hadoop application, or planning to integrate Hadoop into your existing data infrastructure, Hadoop Application Architectures will skillfully guide you through the process. This book covers: Factors to consider when using Hadoop to store and model data Best practices for moving data in and out of the system Data processing frameworks, including MapReduce, Spark, and Hive Common Hadoop processing patterns, such as removing duplicate records and using windowing analytics Giraph, GraphX, and other tools for large graph processing on Hadoop Using workflow orchestration and scheduling tools such as Apache Oozie Near-real-time stream processing with Apache Storm, Apache Spark Streaming, and Apache Flume Architecture examples for clickstream analysis, fraud detection, and data warehousing |
| confluent kafka architecture diagram: Secure Operations Technology Andrew Ginter, 2019-01-03 IT-SEC protects the information. SEC-OT protects physical, industrial operations from information, more specifically from attacks embedded in information. When the consequences of compromise are unacceptable - unscheduled downtime, impaired product quality and damaged equipment - software-based IT-SEC defences are not enough. Secure Operations Technology (SEC-OT) is a perspective, a methodology, and a set of best practices used at secure industrial sites. SEC-OT demands cyber-physical protections - because all software can be compromised. SEC-OT strictly controls the flow of information - because all information can encode attacks. SEC-OT uses a wide range of attack capabilities to determine the strength of security postures - because nothing is secure. This book documents the Secure Operations Technology approach, including physical offline and online protections against cyber attacks and a set of twenty standard cyber-attack patterns to use in risk assessments. |
| confluent kafka architecture diagram: ActiveMQ in Action Dejan Bosanac, Bruce Snyder, Rob Davies, 2011-03-30 Applications in enterprises need to communicate, most commonly done by messaging. Apache ActiveMQ is an open-source implementation of the Java Message Service (JMS), which provides messaging in Java applications. ActiveMQ in Action is a thorough, practical guide to implementing message-oriented systems using ActiveMQ and Java. Co-authored by one of the leading ActiveMQ developers, Bruce Snyder, the book starts with the anatomy of a core Java message, then moves quickly through fundamentals including data persistence, authentication and authorization. Later chapters cover advanced features such as configuration and performance tuning, illustrating each concept with a running real-world stock portfolio application. Readers will learn to integrate ActiveMQ with Apache Geronimo and JBoss, and tie into both Java and non-Java technologies including AJAX, .NET, C++, Ruby, and the Spring framework. Purchase of the print book comes with an offer of a free PDF, ePub, and Kindle eBook from Manning. Also available is all code from the book. |
| confluent kafka architecture diagram: Kubernetes Patterns Bilgin Ibryam, Roland Huß, 2019-04-09 The way developers design, build, and run software has changed significantly with the evolution of microservices and containers. These modern architectures use new primitives that require a different set of practices than most developers, tech leads, and architects are accustomed to. With this focused guide, Bilgin Ibryam and Roland Huß from Red Hat provide common reusable elements, patterns, principles, and practices for designing and implementing cloud-native applications on Kubernetes. Each pattern includes a description of the problem and a proposed solution with Kubernetes specifics. Many patterns are also backed by concrete code examples. This book is ideal for developers already familiar with basic Kubernetes concepts who want to learn common cloud native patterns. You’ll learn about the following pattern categories: Foundational patterns cover the core principles and practices for building container-based cloud-native applications. Behavioral patterns explore finer-grained concepts for managing various types of container and platform interactions. Structural patterns help you organize containers within a pod, the atom of the Kubernetes platform. Configuration patterns provide insight into how application configurations can be handled in Kubernetes. Advanced patterns covers more advanced topics such as extending the platform with operators. |
| confluent kafka architecture diagram: Building Microservices Sam Newman, 2015-02-02 Annotation Over the past 10 years, distributed systems have become more fine-grained. From the large multi-million line long monolithic applications, we are now seeing the benefits of smaller self-contained services. Rather than heavy-weight, hard to change Service Oriented Architectures, we are now seeing systems consisting of collaborating microservices. Easier to change, deploy, and if required retire, organizations which are in the right position to take advantage of them are yielding significant benefits. This book takes an holistic view of the things you need to be cognizant of in order to pull this off. It covers just enough understanding of technology, architecture, operations and organization to show you how to move towards finer-grained systems. |
| confluent kafka architecture diagram: Monolith to Microservices Sam Newman, 2019-11-14 How do you detangle a monolithic system and migrate it to a microservice architecture? How do you do it while maintaining business-as-usual? As a companion to Sam Newman’s extremely popular Building Microservices, this new book details a proven method for transitioning an existing monolithic system to a microservice architecture. With many illustrative examples, insightful migration patterns, and a bevy of practical advice to transition your monolith enterprise into a microservice operation, this practical guide covers multiple scenarios and strategies for a successful migration, from initial planning all the way through application and database decomposition. You’ll learn several tried and tested patterns and techniques that you can use as you migrate your existing architecture. Ideal for organizations looking to transition to microservices, rather than rebuild Helps companies determine whether to migrate, when to migrate, and where to begin Addresses communication, integration, and the migration of legacy systems Discusses multiple migration patterns and where they apply Provides database migration examples, along with synchronization strategies Explores application decomposition, including several architectural refactoring patterns Delves into details of database decomposition, including the impact of breaking referential and transactional integrity, new failure modes, and more |
| confluent kafka architecture diagram: Spark in Action Jean-Georges Perrin, 2020-05-12 Summary The Spark distributed data processing platform provides an easy-to-implement tool for ingesting, streaming, and processing data from any source. In Spark in Action, Second Edition, you’ll learn to take advantage of Spark’s core features and incredible processing speed, with applications including real-time computation, delayed evaluation, and machine learning. Spark skills are a hot commodity in enterprises worldwide, and with Spark’s powerful and flexible Java APIs, you can reap all the benefits without first learning Scala or Hadoop. Foreword by Rob Thomas. About the technology Analyzing enterprise data starts by reading, filtering, and merging files and streams from many sources. The Spark data processing engine handles this varied volume like a champ, delivering speeds 100 times faster than Hadoop systems. Thanks to SQL support, an intuitive interface, and a straightforward multilanguage API, you can use Spark without learning a complex new ecosystem. About the book Spark in Action, Second Edition, teaches you to create end-to-end analytics applications. In this entirely new book, you’ll learn from interesting Java-based examples, including a complete data pipeline for processing NASA satellite data. And you’ll discover Java, Python, and Scala code samples hosted on GitHub that you can explore and adapt, plus appendixes that give you a cheat sheet for installing tools and understanding Spark-specific terms. What's inside Writing Spark applications in Java Spark application architecture Ingestion through files, databases, streaming, and Elasticsearch Querying distributed datasets with Spark SQL About the reader This book does not assume previous experience with Spark, Scala, or Hadoop. About the author Jean-Georges Perrin is an experienced data and software architect. He is France’s first IBM Champion and has been honored for 12 consecutive years. Table of Contents PART 1 - THE THEORY CRIPPLED BY AWESOME EXAMPLES 1 So, what is Spark, anyway? 2 Architecture and flow 3 The majestic role of the dataframe 4 Fundamentally lazy 5 Building a simple app for deployment 6 Deploying your simple app PART 2 - INGESTION 7 Ingestion from files 8 Ingestion from databases 9 Advanced ingestion: finding data sources and building your own 10 Ingestion through structured streaming PART 3 - TRANSFORMING YOUR DATA 11 Working with SQL 12 Transforming your data 13 Transforming entire documents 14 Extending transformations with user-defined functions 15 Aggregating your data PART 4 - GOING FURTHER 16 Cache and checkpoint: Enhancing Spark’s performances 17 Exporting data and building full data pipelines 18 Exploring deployment |
| confluent kafka architecture diagram: Cloud Native Architecture and Design Shivakumar R Goniwada, 2021-10-01 Build enterprise-grade cloud-native systems and learn all about cloud-native architecture and design. This book provides extensive in-depth details of patterns, tools, techniques, and processes with plenty of examples. Cloud Native Architecture and Design begins by explaining the fundamentals of cloud-native architecture and services, what cloud principles and patterns to use, and details of designing a cloud-native element. The book progresses to cover the details of how IT systems can modernize to embrace cloud-native architecture, and also provides details of various enterprise assessment techniques to decide what systems can move and cannot move into the cloud. Architecting and designing a cloud-native system isn’t possible without modernized software engineering principles, the culture of automation, and the culture of innovation. As such, this book covers the details of cloud-native software engineering methodologies, and process, and how to adopt an automated governance approach across enterprises with the adoption of artificial intelligence. Finally, you need your cloud-native applications to run efficiently; this section covers the details of containerization, orchestration, and virtualization in the public, private, and hybrid clouds. After reading this book, you will have familiarity with the many concepts related to cloud-native and understand how to design and develop a successful cloud-native application. Technologies and practices may change over time, but the book lays a strong foundation on which you can build successful cloud-native systems. What You Will Learn Discover cloud-native principles and patterns, and how you can leverage them to solve your business problems Gain the techniques and concepts you need to adapt to design a cloud-native application Use assessment techniques and tools for IT modernization Apply cloud-native engineering principles to the culture of automation and culture of innovation Harness the techniques and tools to run your cloud-native applications and automate infrastructure Operate your cloud-native applications by using AI techniques and zero operation techniques Who This Book Is For Software architects, leaders, developers, engineers, project managers, and students. |
| confluent kafka architecture diagram: Proceedings of International Conference on Machine Intelligence and Data Science Applications Manish Prateek, 2021 This book is a compilation of peer-reviewed papers presented at the International Conference on Machine Intelligence and Data Science Applications, organized by the School of Computer Science, University of Petroleum & Energy Studies, Dehradun, on September 4 and 5, 2020. The book starts by addressing the algorithmic aspect of machine intelligence which includes the framework and optimization of various states of algorithms. Variety of papers related to wide applications in various fields like image processing, natural language processing, computer vision, sentiment analysis, and speech and gesture analysis have been included with upfront details. The book concludes with interdisciplinary applications like legal, health care, smart society, cyber physical system and smart agriculture. The book is a good reference for computer science engineers, lecturers/researchers in machine intelligence discipline and engineering graduates. |
| confluent kafka architecture diagram: Stream Processing with Apache Flink Fabian Hueske, Vasiliki Kalavri, 2019-04-11 Get started with Apache Flink, the open source framework that powers some of the world’s largest stream processing applications. With this practical book, you’ll explore the fundamental concepts of parallel stream processing and discover how this technology differs from traditional batch data processing. Longtime Apache Flink committers Fabian Hueske and Vasia Kalavri show you how to implement scalable streaming applications with Flink’s DataStream API and continuously run and maintain these applications in operational environments. Stream processing is ideal for many use cases, including low-latency ETL, streaming analytics, and real-time dashboards as well as fraud detection, anomaly detection, and alerting. You can process continuous data of any kind, including user interactions, financial transactions, and IoT data, as soon as you generate them. Learn concepts and challenges of distributed stateful stream processing Explore Flink’s system architecture, including its event-time processing mode and fault-tolerance model Understand the fundamentals and building blocks of the DataStream API, including its time-based and statefuloperators Read data from and write data to external systems with exactly-once consistency Deploy and configure Flink clusters Operate continuously running streaming applications |
| confluent kafka architecture diagram: Performance Dashboards Wayne W. Eckerson, 2005-10-27 Tips, techniques, and trends on how to use dashboard technology to optimize business performance Business performance management is a hot new management discipline that delivers tremendous value when supported by information technology. Through case studies and industry research, this book shows how leading companies are using performance dashboards to execute strategy, optimize business processes, and improve performance. Wayne W. Eckerson (Hingham, MA) is the Director of Research for The Data Warehousing Institute (TDWI), the leading association of business intelligence and data warehousing professionals worldwide that provide high-quality, in-depth education, training, and research. He is a columnist for SearchCIO.com, DM Review, Application Development Trends, the Business Intelligence Journal, and TDWI Case Studies & Solution. |
| confluent kafka architecture diagram: Kubernetes in Action Marko Luksa, 2017-12-14 Summary Kubernetes in Action is a comprehensive guide to effectively developing and running applications in a Kubernetes environment. Before diving into Kubernetes, the book gives an overview of container technologies like Docker, including how to build containers, so that even readers who haven't used these technologies before can get up and running. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Kubernetes is Greek for helmsman, your guide through unknown waters. The Kubernetes container orchestration system safely manages the structure and flow of a distributed application, organizing containers and services for maximum efficiency. Kubernetes serves as an operating system for your clusters, eliminating the need to factor the underlying network and server infrastructure into your designs. About the Book Kubernetes in Action teaches you to use Kubernetes to deploy container-based distributed applications. You'll start with an overview of Docker and Kubernetes before building your first Kubernetes cluster. You'll gradually expand your initial application, adding features and deepening your knowledge of Kubernetes architecture and operation. As you navigate this comprehensive guide, you'll explore high-value topics like monitoring, tuning, and scaling. What's Inside Kubernetes' internals Deploying containers across a cluster Securing clusters Updating applications with zero downtime About the Reader Written for intermediate software developers with little or no familiarity with Docker or container orchestration systems. About the Author Marko Luksa is an engineer at Red Hat working on Kubernetes and OpenShift. Table of Contents PART 1 - OVERVIEW Introducing Kubernetes First steps with Docker and Kubernetes PART 2 - CORE CONCEPTS Pods: running containers in Kubernetes Replication and other controllers: deploying managed pods Services: enabling clients to discover and talk to pods Volumes: attaching disk storage to containers ConfigMaps and Secrets: configuring applications Accessing pod metadata and other resources from applications Deployments: updating applications declaratively StatefulSets: deploying replicated stateful applications PART 3 - BEYOND THE BASICS Understanding Kubernetes internals Securing the Kubernetes API server Securing cluster nodes and the network Managing pods' computational resources Automatic scaling of pods and cluster nodes Advanced scheduling Best practices for developing apps Extending Kubernetes |
| confluent kafka architecture diagram: Intelligent Human Computer Interaction Madhusudan Singh, Dae-Ki Kang, Jong-Ha Lee, Uma Shanker Tiwary, Dhananjay Singh, Wan-Young Chung, 2021-02-05 The two-volume set LNCS 12615 + 12616 constitutes the refereed proceedings of the 12th International Conference on Intelligent Human Computer Interaction, IHCI 2020, which took place in Daegu, South Korea, during November 24-26, 2020. The 75 full and 18 short papers included in these proceedings were carefully reviewed and selected from a total of 185 submissions. The papers were organized in topical sections named: cognitive modeling and system; biomedical signal processing and complex problem solving; natural language, speech, voice and study; algorithm and related applications; crowd sourcing and information analysis; intelligent usability and test system; assistive living; image processing and deep learning; and human-centered AI applications. |
| confluent kafka architecture diagram: Advanced Platform Development with Kubernetes Craig Johnston, 2020-09-18 Leverage Kubernetes for the rapid adoption of emerging technologies. Kubernetes is the future of enterprise platform development and has become the most popular, and often considered the most robust, container orchestration system available today. This book focuses on platforming technologies that power the Internet of Things, Blockchain, Machine Learning, and the many layers of data and application management supporting them. Advanced Platform Development with Kubernetes takes you through the process of building platforms with these in-demand capabilities. You'll progress through the development of Serverless, CICD integration, data processing pipelines, event queues, distributed query engines, modern data warehouses, data lakes, distributed object storage, indexing and analytics, data routing and transformation, query engines, and data science/machine learning environments. You’ll also see how to implement and tie together numerous essential and trending technologies including: Kafka, NiFi, Airflow, Hive, Keycloak, Cassandra, MySQL, Zookeeper, Mosquitto, Elasticsearch, Logstash, Kibana, Presto, Mino, OpenFaaS, and Ethereum. The book uses Golang and Python to demonstrate the development integration of custom container and Serverless functions, including interaction with the Kubernetes API. The exercises throughout teach Kubernetes through the lens of platform development, expressing the power and flexibility of Kubernetes with clear and pragmatic examples. Discover why Kubernetes is an excellent choice for any individual or organization looking to embark on developing a successful data and application platform. What You'll Learn Configure and install Kubernetes and k3s on vendor-neutral platforms, including generic virtual machines and bare metal Implement an integrated development toolchain for continuous integration and deployment Use data pipelines with MQTT, NiFi, Logstash, Kafka and Elasticsearch Install a serverless platform with OpenFaaS Explore blockchain network capabilities with Ethereum Support a multi-tenant data science platform and web IDE with JupyterHub, MLflow and Seldon Core Build a hybrid cluster, securely bridging on-premise and cloud-based Kubernetes nodes Who This Book Is For System and software architects, full-stack developers, programmers, and DevOps engineers with some experience building and using containers. This book also targets readers who have started with Kubernetes and need to progress from a basic understanding of the technology and Hello World example to more productive, career-building projects. |
| confluent kafka architecture diagram: Microservices for the Enterprise Kasun Indrasiri, Prabath Siriwardena, 2018-11-14 Understand the key challenges and solutions around building microservices in the enterprise application environment. This book provides a comprehensive understanding of microservices architectural principles and how to use microservices in real-world scenarios. Architectural challenges using microservices with service integration and API management are presented and you learn how to eliminate the use of centralized integration products such as the enterprise service bus (ESB) through the use of composite/integration microservices. Concepts in the book are supported with use cases, and emphasis is put on the reality that most of you are implementing in a “brownfield” environment in which you must implement microservices alongside legacy applications with minimal disruption to your business. Microservices for the Enterprise covers state-of-the-art techniques around microservices messaging, service development and description, service discovery, governance, and data management technologies and guides you through the microservices design process. Also included is the importance of organizing services as core versus atomic, composite versus integration, and API versus edge, and how such organization helps to eliminate the use of a central ESB and expose services through an API gateway. What You'll LearnDesign and develop microservices architectures with confidence Put into practice the most modern techniques around messaging technologies Apply the Service Mesh pattern to overcome inter-service communication challenges Apply battle-tested microservices security patterns to address real-world scenarios Handle API management, decentralized data management, and observability Who This Book Is For Developers and DevOps engineers responsible for implementing applications around a microservices architecture, and architects and analysts who are designing such systems |
Confluent Investor Presentation Q1'25
end of this presentation for a reconciliation of our non-GAAP financial measures to the most directly comparable GAAP financial measures. …
Confluent Earnings Presentation Q4 2023
22 Confluent Cloud Revenue $348.8M +65% YoY Total Revenue $777.0M +33% YoY Non-GAAP Operating Margin (7.4%) +23 pts YoY Fiscal Year 2023 Results …
Form 10-K for Confluent INC filed 02/18/2025
t ab l e of c on te n ts. u n i t e d s t a t e s. s e c u r i t i e s a n d e x c h a n g e c o m m i s s i o n. wa s h i n g t o n , d c …
Confluent Earnings Report
What Confluent Delivers With a complete data streaming platform (DSP) from Confluent, organizations can build a shared source of real-time truth for …
Form 10-Q for Confluent INC filed 04/30/2025
Symbol (s ) Name of e ac h e xc hange on whi c h r e gi s te r e d. C l a s s A C om m on St oc k, pa r va l ue $0.00001 …
Confluent Investor Presentation Q1'25
end of this presentation for a reconciliation of our non-GAAP financial measures to the most directly comparable GAAP financial measures. Confluent Cloud revenue includes revenue …
Confluent Earnings Presentation Q4 2023
22 Confluent Cloud Revenue $348.8M +65% YoY Total Revenue $777.0M +33% YoY Non-GAAP Operating Margin (7.4%) +23 pts YoY Fiscal Year 2023 Results Note: Financials are for …
Form 10-K for Confluent INC filed 02/18/2025
t ab l e of c on te n ts. u n i t e d s t a t e s. s e c u r i t i e s a n d e x c h a n g e c o m m i s s i o n. wa s h i n g t o n , d c 20549
Confluent Earnings Report
What Confluent Delivers With a complete data streaming platform (DSP) from Confluent, organizations can build a shared source of real-time truth for sophisticated model building and …
Form 10-Q for Confluent INC filed 04/30/2025
Symbol (s ) Name of e ac h e xc hange on whi c h r e gi s te r e d. C l a s s A C om m on St oc k, pa r va l ue $0.00001 pe r s ha re; C FL T. T he
Confluent, Inc.
Confluent, Inc. (E xac t n ame of re gi s tr an t as s p e c i fi e d i n i ts c h ar te r ) D e l aw are 47-1824387 (State or othe r jur i s di c ti on of i nc or por ati on or or gani z ati on) (I .R.S. E mpl …
Annual Report 2023 - Confluent, Inc.
CLO@confluent.io. On or about April 24, 2024, we expect to mail to our stockholders a Notice of Internet Availability of Proxy Materials (Notice) containing instructions on how to access the …
436166 017 BMK WEB - Confluent, Inc.
(Confluent). The Annual Meeting will be held virtually, via live webcast at www.virtualshareholdermeeting.com/ CFLT2023 on Thursday, June 1, 2023 at 9:00 a.m., …
FORM 4 SEC Form 4 - Confluent, Inc.
Mar 6, 2024 · Confluent, Inc. [ CFLT] 5. Relationship of Reporting Person(s) to Issuer (Check all applicable) Director 10% Owner. X. Officer (give title. below) Other (specify below) …
Q4 and Fiscal Year 2022 Supplemental Financial Information
Confluent, Inc. Condensed Consolidated Statements of Cash Flows - Fiscal Years (in thousands) (unaudited) Year Ended December 31, 2020 2021 2022 CASH FLOWS FROM OPERATING …
