Getting started with streaming analytics: Setting up a pipeline

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In this session I will show you how to create a simple streaming analytics pipeline, first using open source tools and developing locally, then moving to a VM, then moving to fully managed AWS services. The session will serve as an introduction to some details of Apache Kafka, Apache Flink, ElasticSearch, Amazon Managed Streaming for Kafka, Kinesis Data Analytics, and Amazon ElasticSearch. It will be an almost slideless presentation, as I will spent most of the time at the command line and the IDE.

Data & Analytics

© 2020, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Confidential and Trademark
© 2020, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Confidential
Part 2 of 3: Setting up a pipeline
Getting started with streaming analytics
Javier Ramirez
AWS Developer Advocate
@supercoco9

© 2020, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Confidential
Agenda
Building and running a basic Apache Kafka + Apache
Flink pipeline locally
Deploying to Amazon MSK + Kinesis Data Analytics
Adding aggregations and using Elasticsearch and
Kibana for the dashboards
Replacing our Kafka input by Kinesis Data Streams

© 2020, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Confidential
Ingestion/in-stream storage: Apache Kafka
A distributed streaming platform
Concepts:
Producers
Topics
Brokers
Consumers

© 2020, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Confidential
Stream Processing: Apache Flink
Stateful computation over Data Streams
Concepts:
Job Manager/Workers
Source
DataStream
Transforms/Operators
TableAPI/SQL
Sinks

© 2020, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Confidential
Dashboard: Elasticsearch with Kibana
Elasticsearch is a distributed JSON-based search and
analytics engine. Kibana gives shape to your data
https://www.elastic.co/kibana
Wikimedia has a live
interactive dashboard
powered by Kibana at
https://wikimedia.biterg.io/
Concepts:
Master Node
Data Nodes
Shard
Index

© 2020, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Confidential
Amazon Kinesis Data Streams
• Easy administration and low cost
• Real-time, elastic performance
• Secure, durable storage
• Available to multiple real-time analytics applications

© 2020, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Confidential
Amazon Kinesis Data Streams
Time-based
seek
• Data streams are made of Shards
• Each Shard ingests data up to 1MB/sec,
and up to 1000 TPS
• Each Shard emits up to 2 MB/sec
• All data is stored for 24 hours – 7 days
• Scale Kinesis data streams by splitting or
merging Shards
• Replay data inside of 24Hr -7days
Window

© 2020, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Confidential
Getting Started
https://ci.apache.org/projects/flink/flink-docs-stable/
Apache Flink official docs
https://www.elastic.co/guide/en/elasticsearch/reference/current/index.html
Elasticsearch official docs
https://docs.aws.amazon.com/msk/latest/developerguide/what-is-msk.html
Getting started with Apache Kafka/Amazon MSK
https://aws.amazon.com/kinesis/
Amazon Kinesis Services for streaming data
https://aws.amazon.com/elasticsearch-service/
Amazon ElasticSearch Service
https://kafka.apache.org/documentation/
Apache Kafka official docs

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Build a highly scalable system with serverless container environment - Fargate 利用 Fargate - 無伺服器的容器環境建置高可用的系統 Level: 200, 中文演講講師: Jayson Hsieh, Solution Architect, AWS & Alan Hsieh, R&D Manager, Growth Machine 使用無伺服器的 Fargate 來部屬 docker 容器環境，是應用服務面對高擴充性、高可用性需求時很好的選擇，免除了底層虛擬機器的維運工作。在這個 session 之中，我們會聊聊以 Fargate 為主體的無伺服器容器環境如何融入 DevOps，並且聽聽 Growth Machine 如何運營上百個 Fargate 容器來滿足高彈性擴張的需求。聯繫銷售: https://aws.amazon.com/tw/contact-us/ 與銷售線上聊天: https://pages.awscloud.com/tw-hkt-sales-chat.html

Real-Time Web Analytics with Amazon Kinesis Data Analytics (ADT401) - AWS re:...

Amazon Web Services

Knowing what users are doing on your websites in real time provides insights you can act on without waiting for delayed batch processing of clickstream data. Watching the immediate impact on user behavior after new releases, detecting and responding to anomalies, situational awareness, and evaluating trends are all benefits of real-time website analytics. In this workshop, we build a cost-optimized platform to capture web beacon traffic, analyze it for interesting metrics, and display it on a customized dashboard. We start by deploying the Web Analytics Solution Accelerator, then once the core is complete, we extend their solution to capture new and interesting metrics, process those with Amazon Kinesis Analytics, and display new graphs on their custom dashboard. Participants come away with a fully functional system for capturing, analyzing, and displaying valuable website metrics in real time.

Cyber Data Lake: How CIS Analyzes Billions of Network Traffic Records per Day

Amazon Web Services

As network traffic exponentially increased, the Center for Internet Security (CIS) needed a way to cost-effectively scale Albert, its IP traffic-monitoring tool. With over 10 terabytes of data and more than 10 billion logs for daily analysis, its existing on-premises architecture could no longer meet the performance requirements for providing low-latency analytics. Learn how CIS worked jointly with AWS Professional Services to develop an architecture that uses native AWS services, such as Amazon Athena, Amazon S3, Amazon EC2, and AWS Lambda to build a scalable, cost-effective system. This new architecture provides ingestion, enrichment, and access to all of CIS's data in near real time, enabling answers that previously took days, in just a few minutes.

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Hubo un tiempo en el que casi cualquier componente de software requería pagar una licencia. Afortunadamente, hoy en día gracias al software libre y de código abierto, se puede desarrollar prácticamente cualquier aplicación usando componentes gratuitos. Pero, si el software es gratis, ¿Quién lo desarrolla? ¿Trabaja la comunidad de software libre de forma altruista? ¿Se puede desarrollar software libre de forma profesional? De hecho, hay quien dice que el código abierto tal y como lo conocimos ya no existe, y que lo que hay hoy en día es otra cosa. En esta charla hablaré de cómo se puede monetizar el código libre, y de algunos posibles conflictos que puedes encontrarte en el camino. Además, te contaré cómo hacemos desde QuestDB para desarrollar una base de datos de código abierto y mantener un equipo estable viviendo de ello. Comentaré también algunas situaciones problemáticas a las que proyectos muy destacados se han enfrentado, o que se enfrentan a día de hoy.

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(talk delivered at OSA CON 23) Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds. It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will learn how it deals with data ingestion, and which SQL extensions it implements for working with time-series efficiently. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or data deduplication.

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QuestDB es una base de datos open source de alto rendimiento. Mucha gente nos comentaba que les gustaría usarla como servicio, sin tener que gestionar las máquinas. Así que nos pusimos manos a la obra para desarrollar una solución que nos permitiese lanzar instancias de QuestDB con provisionado, monitorización, seguridad o actualizaciones totalmente gestionadas. Unos cuantos clusters de Kubernetes más tarde, conseguimos lanzar nuestra oferta de QuestDB Cloud. Esta charla es la historia de cómo llegamos ahí. Hablaré de herramientas como Calico, Karpenter, CoreDNS, Telegraf, Prometheus, Loki o Grafana, pero también de retos como autenticación, facturación, multi-nube, o de a qué tienes que decir que no para poder sobrevivir en la nube.

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How would you build a database to support sustained ingestion of several hundreds of thousands rows per second while running near real-time queries on top? In this session I will go over some of the technical decisions and trade-offs we applied when building QuestDB, an open source time-series database developed mainly in JAVA, and how we can achieve over four million row writes per second on a single instance without blocking or slowing down the reads. There will be code and demos, of course. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.

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Time series data pipelines tend to prioritise speed and freshness over completeness and integrity. In such scenarios, it is very common to ingest duplicate data, which may be fine for many analytical use cases, but is very inconvenient for others. There are many open source databases built specifically for the speed and query semantics of time series, and most of them lack automatic deduplication of events in near real-time. One such database is QuestDB, which requires a manual batch process to deduplicate ingested data. In this talk, we will see how we can successfully use Apache Beam to deduplicate streaming time series, which can then be analysed by a time series database.

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Relational databases were created a long time ago for a simpler world. Even if they are still awesome tools for generic workloads, there are some things they cannot do well. In this session I will speak about purpose-built databases that you can use for specific business scenarios. We will see the type of queries you can run on a Graph database, a Document Database, and a Time-Series database. We will then see how a relational database could also be used for the same use cases, just in a much more complex way.

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CBU毕业证【微信95270640】《如何办理不列颠海角大学毕业证认证》【办证Q微信95270640】《不列颠海角大学文凭毕业证制作》《CBU学历学位证书哪里买》办理不列颠海角大学学位证书扫描件、办理不列颠海角大学雅思证书！国际留学归国服务中心《如何办不列颠海角大学毕业证认证》《CBU学位证书扫描件哪里买》实体公司，注册经营，行业标杆，精益求精！ 1:1完美还原海外各大学毕业材料上的工艺：水印阴影底纹钢印LOGO烫金烫银LOGO烫金烫银复合重叠。文字图案浮雕激光镭射紫外荧光温感复印防伪。可办理以下真实不列颠海角大学存档留学生信息存档认证： 1不列颠海角大学真实留信网认证（网上可查永久存档无风险百分百成功入库）； 2真实教育部认证（留服）等一切高仿或者真实可查认证服务（暂时不可办理）； 3购买英美真实学籍（不用正常就读直接出学历）； 4英美一年硕士保毕业证项目（保录取学校挂名不用正常就读保毕业）留学本科/硕士毕业证书成绩单制作流程： 1客户提供办理信息：姓名生日专业学位毕业时间等（如信息不确定可以咨询顾问：我们有专业老师帮你查询不列颠海角大学不列颠海角大学本科学位证成绩单）； 2开始安排制作不列颠海角大学毕业证成绩单电子图； 3不列颠海角大学毕业证成绩单电子版做好以后发送给您确认； 4不列颠海角大学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不列颠海角大学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） — — — — — — — — — — — 《文凭顾问Q/微：95270640》这么大这么美的地方赚大钱高楼大厦鳞次栉比大街小巷人潮涌动山娃一路张望一路惊叹他发现城里的桥居然层层叠叠扭来扭去桥下没水却有着水一般的车水马龙山娃惊诧于城里的公交车那么大那么美不用买票乖乖地掷下二枚硬币空调享受还能坐着看电视呢屡经辗转山娃终于跟着父亲到家了山娃没想到父亲城里的家会如此寒碜更没料到父亲的城里竟有如此简陋的鬼地方父亲的家在高楼最底屋最下面很矮很黑是很不显眼的地下室父亲的家安在别人脚底下孰

一比一原版(TWU毕业证)西三一大学毕业证成绩单

ocavb

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Predicting Product Ad Campaign Performance: A Data Analysis Project Presentation

Boston Institute of Analytics

Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/

Investigate & Recover / StarCompliance.io / Crypto_Crimes

StarCompliance.io

StarCompliance is a leading firm specializing in the recovery of stolen cryptocurrency. Our comprehensive services are designed to assist individuals and organizations in navigating the complex process of fraud reporting, investigation, and fund recovery. We combine cutting-edge technology with expert legal support to provide a robust solution for victims of crypto theft. Our Services Include: Reporting to Tracking Authorities: We immediately notify all relevant centralized exchanges (CEX), decentralized exchanges (DEX), and wallet providers about the stolen cryptocurrency. This ensures that the stolen assets are flagged as scam transactions, making it impossible for the thief to use them. Assistance with Filing Police Reports: We guide you through the process of filing a valid police report. Our support team provides detailed instructions on which police department to contact and helps you complete the necessary paperwork within the critical 72-hour window. Launching the Refund Process: Our team of experienced lawyers can initiate lawsuits on your behalf and represent you in various jurisdictions around the world. They work diligently to recover your stolen funds and ensure that justice is served. At StarCompliance, we understand the urgency and stress involved in dealing with cryptocurrency theft. Our dedicated team works quickly and efficiently to provide you with the support and expertise needed to recover your assets. Trust us to be your partner in navigating the complexities of the crypto world and safeguarding your investments.

Jpolillo Amazon PPC - Bid Optimization Sample

James Polillo

社内勉強会資料_LLM Agents　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　.

NABLAS株式会社

一比一原版(IIT毕业证)伊利诺伊理工大学毕业证成绩单

ewymefz

IIT毕业证【微信95270640】购买（伊利诺伊理工大学毕业证成绩单硕士学历）Q微信95270640代办IIT学历认证留信网伪造伊利诺伊理工大学学位证书精仿伊利诺伊理工大学本科/硕士文凭证书补办伊利诺伊理工大学 diplomaoffer,Transcript购买伊利诺伊理工大学毕业证成绩单购买IIT假毕业证学位证书购买伪造伊利诺伊理工大学文凭证书学位证书,专业办理雅思、托福成绩单，学生ID卡，在读证明，海外各大学offer录取通知书，毕业证书，成绩单，文凭等材料:1:1完美还原毕业证、offer录取通知书、学生卡等各种在读或毕业材料的防伪工艺（包括烫金、烫银、钢印、底纹、凹凸版、水印、防伪光标、热敏防伪、文字图案浮雕，激光镭射，紫外荧光，温感光标）学校原版上有的工艺我们一样不会少，不论是老版本还是最新版本，都能保证最高程度还原，力争完美以求让所有同学都能享受到完美的品质服务。 #一整套伊利诺伊理工大学文凭证件办理#—包含伊利诺伊理工大学伊利诺伊理工大学毕业证假文凭学历认证|使馆认证|归国人员证明|教育部认证|留信网认证永远存档教育部学历学位认证查询办理国外文凭国外学历学位认证#我们提供全套办理服务。一整套留学文凭证件服务：一：伊利诺伊理工大学伊利诺伊理工大学毕业证假文凭毕业证 #成绩单等全套材料从防伪到印刷水印底纹到钢印烫金二：真实使馆认证（留学人员回国证明）使馆存档三：真实教育部认证教育部存档教育部留服网站永久可查四：留信认证留学生信息网站永久可查国外毕业证学位证成绩单办理方法： 1客户提供办理伊利诺伊理工大学伊利诺伊理工大学毕业证假文凭信息：姓名生日专业学位毕业时间等（如信息不确定可以咨询顾问：我们有专业老师帮你查询）； 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声和哐咣的关门声待山娃醒来时父亲早已上班去了床头总搁着山娃最爱吃的馒头和肉包还有白花花的豆浆父亲中午留在工地吃饭和午休山娃的中饭是对面快餐店送来的不用山娃付钱父亲早跟老板谈妥了钱到时一起结父亲给山娃配了台手机二手货诺基亚的父亲说有什么事只管给他挂电话能拥有自己的手机山娃很高兴除了玩游戏发短信除了挂电话给爷爷奶奶和母亲山娃还给班主任邱老师连挂了二个电话并给同学阿强和阿昌家挂山娃兴奋地向他们诉说城市一

Sample_Global Non-invasive Prenatal Testing (NIPT) Market, 2019-2030.pdf

Linda486226

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Oppotus

一比一原版(ArtEZ毕业证)ArtEZ艺术学院毕业证成绩单

vcaxypu

ArtEZ毕业证【微信95270640】☀《ArtEZ艺术学院毕业证购买》Q微信95270640《ArtEZ毕业证模板办理》文凭、本科、硕士、研究生学历都可以做,《文凭ArtEZ毕业证书原版制作ArtEZ成绩单》《仿制ArtEZ毕业证成绩单ArtEZ艺术学院学位证书pdf电子图》毕业证 [留学文凭学历认证(留信认证使馆认证)ArtEZ艺术学院毕业证成绩单毕业证证书大学Offer请假条成绩单语言证书国际回国人员证明高仿教育部认证申请学校等一切高仿或者真实可查认证服务。多年留学服务公司,拥有海外样板无数能完美1:1还原海外各国大学degreeDiplomaTranscripts等毕业材料。海外大学毕业材料都有哪些工艺呢？工艺难度主要由：烫金.钢印.底纹.水印.防伪光标.热敏防伪等等组成。而且我们每天都在更新海外文凭的样板以求所有同学都能享受到完美的品质服务。国外毕业证学位证成绩单办理方法： 1客户提供办理ArtEZ艺术学院ArtEZ艺术学院毕业证假文凭信息：姓名生日专业学位毕业时间等（如信息不确定可以咨询顾问：我们有专业老师帮你查询）； 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） — — — — 我们是挂科和未毕业同学们的福音我们是实体公司精益求精的工艺！ — — — - 一真实留信认证的作用(私企外企荣誉的见证): 1：该专业认证可证明留学生真实留学身份同时对留学生所学专业等级给予评定。 2：国家专业人才认证中心颁发入库证书这个入网证书并且可以归档到地方。 3：凡是获得留信网入网的信息将会逐步更新到个人身份内将在公安部网内查询个人身份证信息后同步读取人才网入库信息。 4：个人职称评审加20分个人信誉贷款加10分。 5：在国家人才网主办的全国网络招聘大会中纳入资料供国家500强等高端企业选择人才。却怎么也笑不出来山娃很迷惑父亲的家除了一扇小铁门连窗户也没有墓穴一般阴森森有些骇人父亲的城也便成了山娃的城父亲的家也便成了山娃的家父亲让山娃呆在屋里做作业看电视最多只能在门口透透气不能跟陌生人搭腔更不能乱跑一怕迷路二怕拐子拐人山娃很惊惧去年村里的田鸡就因为跟父亲进城一不小心被人拐跑了至今不见踪影害得田鸡娘天天哭得死去活来疯了一般那情那景无不令人摧肝裂肺山娃很听话天天呆在小屋里除了看书写作业就是睡带

Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...

John Andrews

SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation" Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults Description: Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project. Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas

Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...

Subhajit Sahu

Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.

The affect of service quality and online reviews on customer loyalty in the E...

jerlynmaetalle

【社内勉強会資料_Octo: An Open-Source Generalist Robot Policy】

NABLAS株式会社

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Getting started with streaming analytics: Setting up a pipeline

1. © 2020, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Confidential and Trademark © 2020, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Confidential Part 2 of 3: Setting up a pipeline Getting started with streaming analytics Javier Ramirez AWS Developer Advocate @supercoco9

2. © 2020, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Confidential Agenda Building and running a basic Apache Kafka + Apache Flink pipeline locally Deploying to Amazon MSK + Kinesis Data Analytics Adding aggregations and using Elasticsearch and Kibana for the dashboards Replacing our Kafka input by Kinesis Data Streams

3. © 2020, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Confidential Ingestion/in-stream storage: Apache Kafka A distributed streaming platform Concepts: Producers Topics Brokers Consumers

4. © 2020, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Confidential Stream Processing: Apache Flink Stateful computation over Data Streams Concepts: Job Manager/Workers Source DataStream Transforms/Operators TableAPI/SQL Sinks

6. © 2020, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Confidential Dashboard: Elasticsearch with Kibana Elasticsearch is a distributed JSON-based search and analytics engine. Kibana gives shape to your data https://www.elastic.co/kibana Wikimedia has a live interactive dashboard powered by Kibana at https://wikimedia.biterg.io/ Concepts: Master Node Data Nodes Shard Index

7. © 2020, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Confidential Amazon Kinesis Data Streams • Easy administration and low cost • Real-time, elastic performance • Secure, durable storage • Available to multiple real-time analytics applications

8. © 2020, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Confidential Amazon Kinesis Data Streams Time-based seek • Data streams are made of Shards • Each Shard ingests data up to 1MB/sec, and up to 1000 TPS • Each Shard emits up to 2 MB/sec • All data is stored for 24 hours – 7 days • Scale Kinesis data streams by splitting or merging Shards • Replay data inside of 24Hr -7days Window

9. © 2020, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Confidential Getting Started https://ci.apache.org/projects/flink/flink-docs-stable/ Apache Flink official docs https://www.elastic.co/guide/en/elasticsearch/reference/current/index.html Elasticsearch official docs https://docs.aws.amazon.com/msk/latest/developerguide/what-is-msk.html Getting started with Apache Kafka/Amazon MSK https://aws.amazon.com/kinesis/ Amazon Kinesis Services for streaming data https://aws.amazon.com/elasticsearch-service/ Amazon ElasticSearch Service https://kafka.apache.org/documentation/ Apache Kafka official docs

10. © 2020, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Confidential and Trademark © 2020, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Confidential ThanksJavier Ramirez AWS Developer Advocate @supercoco9

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