Riviera Jug - 20/03/2018 - Kafka streams
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When it comes to the fast-paced nature of capital markets and IoT, the ability to analyze data in real time is critical to gaining an edge. It’s not just about the quantity of data you can analyze at once, it’s about the speed, scale, and quality of the data you have at your fingertips.
Modern streaming data technologies like Apache Kafka and the broader Confluent platform can help detect opportunities and threats in real time. They can improve profitability, yield, and performance. Combining Kafka with Panopticon visual analytics provides a powerful foundation for optimizing your operations.
Use cases in capital markets include transaction cost analysis (TCA), risk monitoring, surveillance of trading and trader activity, compliance, and optimizing profitability of electronic trading operations. Use cases in IoT include monitoring manufacturing processes, logistics, and connected vehicle telemetry and geospatial data.
This online talk will include in depth practical demonstrations of how Confluent and Panopticon together support several key applications. You will learn:
-Why Apache Kafka is widely used to improve performance of complex operational systems
-How Confluent and Panopticon open new opportunities to analyze operational data in real time
-How to quickly identify and react immediately to fast-emerging trends, clusters, and anomalies
-How to scale data ingestion and data processing
-Build new analytics dashboards in minutes
RefCard API Architecture Strategy
As soon as we start working on an API, architecture issues arise. Many mistaken common beliefs turn out to be fiction in this area. A poorly designed API architecture will lead to misuse or – even worse – not be used at all by its intended clients: application developers.
To facilitate and accelerate design and development of your APIs, we share our vision and beliefs with you in this Reference Card. They come from our direct experience on API projects.
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Every application is created with a background to run, from a simple registration app development though a larger communication app development. If a user enters some data such as login id etc, it must be stored somewhere and once the user comes back and enter the login id app must retrieve the user data to make this process every application either may be a mobile app development or web development
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More from Florent Ramiere
Back to database fundamentals aka the origin of the streaming platform.
This document discusses the history and evolution of database concepts like commit logs, replication, and partitioning into the modern streaming data architecture. It traces how technologies like Kafka evolved from basic concepts like commit logs for high availability and scalability to power stream processing, data integration, and real-time analytics. The core idea of storing a commit log of all changes has remained fundamental to powering new use cases with streaming data.
Perfug 20-11-2019 - Kafka Performances
1. Confluent is a software company founded in 2014 that has raised $205M with over 950 employees and offices in 20 countries, serving hundreds of enterprise customers.
2. The document discusses various Kafka performance considerations including producer, consumer, and broker configurations that impact throughput, latency, durability, and ordering guarantees.
3. It emphasizes that the appropriate metrics to monitor depend on specific performance needs like throughput versus latency, and highlights some key broker and client-side metrics to monitor like request latency, records lag, disk usage, and more.
Back to database fundamentals
Please find a better version here: https://www.slideshare.net/FlorentRamiere/back-to-database-fundamentals-aka-the-origin-of-the-streaming-platform
... aka the origin of the streaming platform.
The streaming platforms rely on a component that has been part of our most precious systems. Let's rediscover that, and see why the streaming platform is an evolution of our current systems.
JHipster conf 2019 - Kafka Ecosystem
JHipster and Kafka Provide a great set of combinations of properties for the Modern Digital Enterprise!
Paris Kafka Meetup - patterns anti-patterns
How to fail brilliantly with Apache Kafka. Don't be bitten by silly things, learn to avoid the most common pitfalls.
Apache Kafka - Patterns anti-patterns
1) Apache Kafka is a distributed streaming platform that can be used for publish-subscribe messaging and storing and processing streams of data. However, there are many potential anti-patterns to be aware of when using Kafka.
2) Some common anti-patterns include not properly configuring data durability, ignoring error handling and exceptions, failing to use Kafka's built-in retries and idempotence features, and not embracing Kafka's at least once processing semantics.
3) It is also important to properly configure Kafka for production use by tuning OS settings, reading documentation on best practices, implementing monitoring, and addressing topics and partitioning design.
Devoxx university - Kafka de haut en bas
Kafka can be used to build real-time streaming applications and process large amounts of data. It provides a simple publish-subscribe messaging model with streams of records. Kafka Connect allows connecting Kafka with other data systems and formats through reusable connectors. Kafka Streams provides a streaming library to allow building streaming applications and processing data in Kafka streams through operators like map, filter and windowing.
Beyond the brokers - Un tour de l'écosystème Kafka
Apache Kafka ne se résume pas aux brokers, il y a tout un écosystème open-source qui gravite autour. Je vous propose ainsi de découvrir les principaux composants comme Kafka Streams, KSQL, Kafka Connect, Rest proxy, Schema Registry, MirrorMaker, etc.
Jug - ecosystem
This document provides an overview and agenda for a presentation on Confluent, streaming, and KSQL. The presentation includes: an introduction to Confluent and Apache Kafka; an explanation of why streaming platforms are useful; an overview of the Confluent Platform and its components; key concepts in streaming and Kafka; a demonstration of Kafka Streams, Kafka Connect, and KSQL; and resources for further information. The presentation aims to explain streaming concepts, demonstrate Confluent tools, and allow for a question and answer session.
Paris jug ksql - 2018-06-28
This document provides an overview of KSQL, an open-source streaming SQL engine for Apache Kafka. It discusses how KSQL allows users to do stream processing by writing SQL-like statements and queries against Kafka topics. It also covers how KSQL provides a familiar SQL-like interface but with the flexibility, scalability, and performance of stream processing with Kafka Streams.
Chti jug - 2018-06-26
This document contains an agenda and overview of Confluent and streaming with Kafka. The agenda includes introductions to Confluent, streaming, KSQL, and a demo. Confluent is presented as the company founded by the creators of Apache Kafka to develop streaming platforms based on Kafka. Key concepts of streaming, the Confluent platform, and Kafka Streams, Kafka Connect, and KSQL are summarized. The document concludes with resources and time for questions.
Riviera Jug - 20/03/2018 - KSQL
http://www.rivierajug.org/xwiki/bin/view/Blog/Mardi+20+mars+2018+%40+Sophia-Antipolis++soir%C3%A9e+Kafka
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Back to database fundamentals aka the origin of the streaming platform.
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Beyond the brokers - Un tour de l'écosystème Kafka
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2开始安排制作毕业证成绩单电子图；
3毕业证成绩单电子版做好以后发送给您确认；
4毕业证成绩单电子版您确认信息无误之后安排制作成品；
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— — -公司理念 【诚信为主】— — —
我們以質量求生存.以服务求发展有雄厚的实力专业的团队咨询顾问为您细心解答可详谈是真是假眼见为实让您真正放心平凡人生,尽我所能助您一臂之力让我們携手圆您梦想!
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WWDC 2024 Keynote Review: For CocoaCoders Austin
Overview of WWDC 2024 Keynote Address.
Covers: Apple Intelligence, iOS18, macOS Sequoia, iPadOS, watchOS, visionOS, and Apple TV+.
Understandable dialogue on Apple TV+
On-device app controlling AI.
Access to ChatGPT with a guest appearance by Chief Data Thief Sam Altman!
App Locking! iPhone Mirroring! And a Calculator!!
Measures in SQL (SIGMOD 2024, Santiago, Chile)
SQL has attained widespread adoption, but Business Intelligence tools still use their own higher level languages based upon a multidimensional paradigm. Composable calculations are what is missing from SQL, and we propose a new kind of column, called a measure, that attaches a calculation to a table. Like regular tables, tables with measures are composable and closed when used in queries.
SQL-with-measures has the power, conciseness and reusability of multidimensional languages but retains SQL semantics. Measure invocations can be expanded in place to simple, clear SQL.
To define the evaluation semantics for measures, we introduce context-sensitive expressions (a way to evaluate multidimensional expressions that is consistent with existing SQL semantics), a concept called evaluation context, and several operations for setting and modifying the evaluation context.
A talk at SIGMOD, June 9–15, 2024, Santiago, Chile
Authors: Julian Hyde (Google) and John Fremlin (Google)
https://doi.org/10.1145/3626246.3653374
一比一原版(sdsu毕业证书)圣地亚哥州立大学毕业证如何办理
原版一模一样【微信：741003700 】【(sdsu毕业证书)圣地亚哥州立大学毕业证成绩单】【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
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◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
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6:个人职称评审加20分
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办理(sdsu毕业证书)圣地亚哥州立大学毕业证【微信：741003700 】外观非常简单，由纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。
办理(sdsu毕业证书)圣地亚哥州立大学毕业证【微信：741003700 】格式相对统一，各专业都有相应的模板。通常包括以下部分：
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校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
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综上所述，办理(sdsu毕业证书)圣地亚哥州立大学毕业证【微信：741003700 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。
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This talk explores the challenges of bringing modelling rigour to the business and strategy levels, and talking to your non-technical counterparts in the process.
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A money management app can transform your financial life by tracking expenses, creating budgets, and setting financial goals. These apps offer features like real-time expense tracking, bill reminders, and personalized insights to help you save and manage money effectively. With a user-friendly interface, they simplify financial planning, making it easier to stay on top of your finances and achieve long-term financial stability.
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Benefits of Artificial Intelligence in Healthcare!
Benefits of AI in Healthcare
Faster Diagnoses
AI speeds up the diagnostic process, helping doctors identify conditions quickly and accurately.
Personalized Treatments
AI creates customized treatment plans based on individual patient data, improving outcomes.
Predictive Healthcare
AI anticipates health issues before they arise, allowing for preventative measures.
Improved Accuracy
AI reduces human error in diagnostics and treatment, leading to better patient care.
Enhanced Imaging
AI improves the clarity and precision of medical imaging, aiding in early detection of diseases.
Efficient Drug Development
AI accelerates the drug discovery process, bringing new treatments to market faster.
Streamlined Operations
AI automates administrative tasks, reducing the burden on healthcare professionals and improving efficiency.
24/7 Patient Support
AI-powered virtual assistants and chatbots provide round-the-clock support and information to patients.
Cost Reduction
AI optimizes resource use and reduces operational costs, making healthcare more affordable.
Continuous Monitoring
AI continuously monitors patient health, enabling timely interventions and better management of chronic conditions.
Experience the Future of Healthcare with AI!
#AIinHealthcare #MedicalInnovation #HealthTech #BetterCare
一比一原版(USF毕业证)旧金山大学毕业证如何办理
USF硕士毕业证成绩单【微信95270640】一比一伪造旧金山大学文凭@假冒USF毕业证成绩单+Q微信95270640办理USF学位证书@仿造USF毕业文凭证书@购买旧金山大学毕业证成绩单USF真实使馆认证/真实留信认证回国人员证明
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国外毕业证学位证成绩单办理方法：
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2开始安排制作毕业证成绩单电子图；
3毕业证成绩单电子版做好以后发送给您确认；
4毕业证成绩单电子版您确认信息无误之后安排制作成品；
5成品做好拍照或者视频给您确认；
6快递给客户（国内顺丰国外DHLUPS等快读邮寄）。
教育部文凭学历认证认证的用途：
如果您计划在国内发展那么办理国内教育部认证是必不可少的。事业性用人单位如银行国企公务员在您应聘时都会需要您提供这个认证。其他私营 #外企企业无需提供！办理教育部认证所需资料众多且烦琐所有材料您都必须提供原件我们凭借丰富的经验帮您快速整合材料让您少走弯路。
实体公司专业为您服务如有需要请联系我: 微信95270640奈一次次令他失望山娃今年岁上五年级识得很多字从走出小屋开始山娃就知道父亲的家和工地共有一个很动听的名字——天河工地的底层空空荡荡很宽阔很凉爽在地上铺上报纸和水泥袋父亲和工人们中午全睡在地上地面坑坑洼洼山娃曾多次绊倒过也曾有长铁钉穿透凉鞋刺在脚板上但山娃不怕工地上也常有五六个从乡下来的小学生他们的父母亲也是高楼上的建筑工人小伙伴来自不同省份都操着带有浓重口音的普通话可不知为啥山娃不仅很快与他们熟识了
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Riviera Jug - 20/03/2018 - Kafka streams
- 1. R ET HINKING Stream Processing With Apache Kafka
- 2. 2 1.0 Enterprise Ready 0.10 Data Processing (Streams API) 0.11 Exactly-once Semantics Kafka the Streaming Data Platform 2013 2014 2015 2016 2017 2018 0.8 Intra-cluster replication 0.9 Data Integration (Connect API)
- 3. 3 Your System, a Hotel APP
- 4. 4 Your System, a Hotel Microservices APP APP APPSecurity Guard ReceptionistRoom Service
- 5. 5 Your System, a Hotel Distributed Apps APP Security Guard Security Team
- 6. 6 As developers, we want to build APPS not INFRASTRUCTURE
- 7. 7 We want our apps to be: Scalable Elastic Fault-tolerant Stateful Distributed
- 8. 8 Where do I put my compute?
- 9. 9 Where do I put my state?
- 10. 10 The actual question is Where is my code?
- 11. 11 the KAFKA STREAMS API is a JAVA API to BUILD REAL-TIME APPLICATIONS to POWER THE BUSINESS
- 16. 16 this means you can DEPLOY your app ANYWHERE using WHATEVER TECHNOLOGY YOU WANT
- 17. 17 Things Kafka Streams Does Runs everywhere Clustering done for you Exactly-once processing Event-time processing Integrated database Joins, windowing, aggregation S/M/L/XL/XXL/XXXL sizes
- 22. 22 // Example: reading data from Kafka KStream<byte[], String> textLines = builder.stream("textlines-topic", Consumed.with( Serdes.ByteArray(), Serdes.String())); // Example: transforming data KStream<byte[], String> upperCasedLines= rawRatings.mapValues(String::toUpperCase)); KStream
- 23. 23 // Example: aggregating data KTable<String, Long> wordCounts = textLines .flatMapValues(textLine -> Arrays.asList(textLine.toLowerCase().split("W+"))) .groupBy((key, word) -> word) .count(); KTable
- 24. 24 DEMO
- 25. 25 Remember, we want to build APPS not INFRASTRUCTURE
- 26. 26 the KAFKA STREAMS API is a JAVA API to BUILD REAL-TIME APPLICATIONS to POWER THE BUSINESS
- 27. 27 Things Kafka Streams Does Runs everywhere Clustering done for you Exactly-once processing Event-time processing Integrated database Joins, windowing, aggregation S/M/L/XL/XXL/XXXL sizes
- 28. THANK YOU!