AWS Retail &amp; CPG Day - Yan So

ABD330_Combining Batch and Stream Processing to Get the Best of Both Worlds

Today, many architects and developers are looking to build solutions that integrate batch and real-time data processing, and deliver the best of both approaches. Lambda architecture (not to be confused with the AWS Lambda service) is a design pattern that leverages both batch and real-time processing within a single solution to meet the latency, accuracy, and throughput requirements of big data use cases. Come join us for a discussion on how to implement Lambda architecture (batch, speed, and serving layers) and best practices for data processing, loading, and performance tuning.

ABD318_Architecting a data lake with Amazon S3, Amazon Kinesis, AWS Glue and ...

"Learn how to architect a data lake where different teams within your organization can publish and consume data in a self-service manner. As organizations aim to become more data-driven, data engineering teams have to build architectures that can cater to the needs of diverse users - from developers, to business analysts, to data scientists. Each of these user groups employs different tools, have different data needs and access data in different ways. In this talk, we will dive deep into assembling a data lake using Amazon S3, Amazon Kinesis, Amazon Athena, Amazon EMR, and AWS Glue. The session will feature Mohit Rao, Architect and Integration lead at Atlassian, the maker of products such as JIRA, Confluence, and Stride. First, we will look at a couple of common architectures for building a data lake. Then we will show how Atlassian built a self-service data lake, where any team within the company can publish a dataset to be consumed by a broad set of users."

Streaming ETL for Data Lakes using Amazon Kinesis Firehose - May 2017 AWS Onl...

Learning Objectives: - Understand key requirements for collecting, preparing, and loading streaming data into data lakes - Get an overview of transmitting data using Amazon Kinesis Firehose - Learn how to perform data transformations with Amazon Kinesis Firehose Data lakes enable your employees across the organization to access and analyze massive amounts of unstructured and structured data from disparate data sources, many of which generate data continuously and rapidly. Making this data available in a timely fashion for analysis requires a streaming solution that can durably and cost-effectively ingest this data into your data lake. Amazon Kinesis Firehose is a fully managed service that makes it easy to prepare and load streaming data into AWS. In this tech talk, we will provide an overview of Amazon Kinesis Firehose and dive deep into how you can use the service to collect, transform, batch, compress, and load real-time streaming data into your Amazon S3 data lakes.

Introduction to Real-time, Streaming Data and Amazon Kinesis. Streaming Data ...

by Adrian Hornsby, Technical Evangelist, AWS Amazon Kinesis is a platform for streaming data on AWS, offering powerful services to make it easy to load and analyze streaming data. In this session, you’ll learn about how AWS customers are transitioning from batch to real-time processing using Amazon Kinesis, and how to get started. We will provide an overview of streaming data applications and introduce the Amazon Kinesis platform and its services. We will walk through a production use case to demonstrate how to ingest streaming data, prepare it, and analyze it to gain actionable insights in real time using Amazon Kinesis. We will also provide pointers to tutorials and other resources so you can quickly get started with your streaming data application.

Preparing Data for the Lake

How to build a data lake with aws glue data catalog (ABD213-R) re:Invent 2017

As data volumes grow and customers store more data on AWS, they often have valuable data that is not easily discoverable and available for analytics. The AWS Glue Data Catalog provides a central view of your data lake, making data readily available for analytics. We introduce key features of the AWS Glue Data Catalog and its use cases. Learn how crawlers can automatically discover your data, extract relevant metadata, and add it as table definitions to the AWS Glue Data Catalog. We will also explore the integration between AWS Glue Data Catalog and Amazon Athena, Amazon EMR, and Amazon Redshift Spectrum.

Evolving your analytics from batch processing to real-time processing can have a major business impact, but ingesting streaming data into your data warehouse requires building complex streaming data pipelines. Amazon Kinesis Firehose solves this problem by making it easy to ingest streaming data into Amazon Redshift so that you can use existing analytics and business intelligence tools to extract information in near real-time and respond promptly. In this webinar, we will dive deep using Amazon Kinesis Firehose to load streaming data into Amazon Redshift reliably, scalably, and cost-effectively. Join us to: - Understand the basics of ingesting streaming data from sources such as mobile devices, servers, and websites with Amazon Kinesis Firehose - Get a closer look at how to automate delivery of streaming data to Amazon Redshift reliably using Amazon Kinesis Firehose - Learn techniques to detect, troubleshoot, and avoid data loading problems Who should attend: Developers, data analysts, data engineers, architects

Build Data Lakes & Analytics on AWS: Patterns & Best Practices - BDA305 - Ana...

In this session, we show you how to understand what data you have, how to drive insights, and how to make predictions using purpose-built AWS services. Learn about the common pitfalls of building data lakes, and discover how to successfully drive analytics and insights from your data. Also learn how services such as Amazon S3, AWS Glue, Amazon Redshift, Amazon Athena, Amazon EMR, Amazon Kinesis, and Amazon ML services work together to build a successful data lake for various roles, including data scientists and business users.

AWS September Webinar Series - Building Your First Big Data Application on AWS

The Big Data ecosystem is moving so fast that is nearly impossible to keep pace. Meanwhile, the strong demand for high analytical and data management skills will continue to grow. So, how can you get up to speed? Join us for this webinar where we will help you get ramped up on how to use Amazon’s Big Data web services. In just 50 minutes, we will build a Big Data application using Amazon Elastic MapReduce and other AWS Big Data Services. In addition, we will review best practices and architecture design patterns for Big Data. Attending re:Invent? One more reason not to miss this webinar, as it will help you get ready for some of our Big Data deep dives! Learning Objectives: Learn about key AWS Big Data services including Amazon S3, Amazon EMR, Amazon Kinesis, and Amazon Redshift Learn about Big Data architectural patterns How to ingest data to Amazon S3 How to start an Amazon EMR cluster Help those attending re:Invent to get up to speed with Big Data services Who Should Attend: Architects and developers, interested in starting a Big Data initiative

Analytics on AWS with Amazon Redshift, Amazon QuickSight, and Amazon Machine ...

AWS has a large and growing portfolio of big data management and analytics services, designed to be integrated into solution architectures that meet the needs of your business. In this session, we look at analytics through the eyes of a business intelligence analyst, a data scientist, and an application developer, and we explore how to quickly leverage Amazon Redshift, Amazon QuickSight, RStudio, and Amazon Machine Learning to create powerful, yet straightforward, business solutions. Speaker: Paul Armstrong, Solutions Architect, Amazon Web Services

NEW LAUNCH! Introducing AWS Batch: Easy and efficient batch computing

AWS Batch is a fully-managed service that enables developers, scientists, and engineers to easily and efficiently run batch computing workloads of any scale on AWS. AWS Batch automatically provisions compute resources and optimizes the workload distribution based on the quantity and scale of the workloads. With AWS Batch, there is no need to install or manage batch computing software, allowing you to focus on analyzing results and solving problems. AWS Batch plans, schedules, and executes your batch computing workloads across the full range of AWS compute services and features, such as Amazon EC2, Spot Instances, and AWS Lambda. AWS Batch reduces operational complexities, saving time and reducing costs. In this session, Principal Product Managers Jamie Kinney and Dougal Ballantyne describe the core concepts behind AWS Batch and details of how the service functions. The presentation concludes with relevant use cases and sample code.

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AWS re:Invent 2016: Visualizing Big Data Insights with Amazon QuickSight (BDM...

Amazon QuickSight is a fast BI service that makes it easy for you to build visualizations, perform ad-hoc analysis, and quickly get business insights from your data. QuickSight is built to harness the power and scalability of the cloud, so you can easily run analysis on large datasets, and support hundreds of thousands of users. In this session, we’ll demonstrate how you can easily get started with Amazon QuickSight, uploading files, connecting to S3 and Redshift and creating analyses from visualizations that are optimized based on the underlying data. Once we’ve built our analysis and dashboard, we’ll show you easy it is to share it with colleagues and stakeholders in just a few seconds. And with SPICE – QuckSight’s in-memory calculation engine – you can go from data to insights, faster than ever.

Modern Data Architectures for Real Time Analytics & Engagement

Introduction to Amazon Kinesis Firehose - AWS August Webinar Series

Streaming data applications can deliver compelling, near real-time user experiences, but building the back-end infrastructure to collect and process streaming data is difficult. Amazon Kinesis Firehose makes it easy for you to load streaming data into AWS without having to build custom stream processing applications. In this webinar, we will introduce Amazon Kinesis Firehose and discuss how to ingest streaming data into Amazon S3, Amazon Redshift, and Amazon Elasticsearch Service using Amazon Kinesis Firehose. We will also highlight key use cases based on real-world examples from IoT, AdTech, E-Commerce, and Gaming. Join us to: - Get an introduction to streaming data and an overview of Amazon Kinesis Firehose - Learn about common streaming data use cases from IoT, Ad Tech, E-Commerce, and Gaming - Understand how to use Amazon Kinesis Firehose to load streaming data into Amazon S3, Amazon Redshift, and Amazon Elasticsearch Service Who should attend: Developers, data analysts, data engineers, architects

Building Serverless Analytics Pipelines with AWS Glue - AWS Summit Sydney 2019

Organisations are increasingly gaining insight and knowledge from a number of IoT, API, clickstream, unstructured, and log data sources. Learn how AWS Glue makes it easy to build and manage enterprise-grade data pipelines to ingest, clean, transform, and automatically catalogue data, which enables a variety of use cases such as ad-hoc analytics, data warehousing, big data analysis, and machine learning. Also, find out how to intergrate an end-to-end CI/CD pipeline to automate the release management process for your serverless data pipelines.

Preparing Data for the Lake: Data Analytics Week SF

Data Analytics Week at the San Francisco Loft Preparing Data for the Lake While a Data Lake can support completely unstructured data, getting performant analytics at scale requires some data preparation. We'll look at how to use Amazon Kinesis, AWS Glue, and Amazon EMR to make raw data ready to high-performance analytics. Speakers: John Mallory - Principal Business Development Manager Storage (Object), AWS Hemant Borole - Sr. Big Data Consultant, AWS

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BDA304 Data-Driven Post Mortems

"The only real mistake is the one from which we learn nothing.” So how do we learn from system failures? This session will move beyond “blameless” postmortems and show how to use data to avoid and mitigate future failures. We will share the best practices for gathering systems-related data and people-related data. You will then learn how to apply the data to formulate actionable response plans and avoid repeating failures. This session is brought to you by AWS Summit New York City sponsor, Datadog."

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Real-Time Log Analytics using Amazon Kinesis and Amazon Elasticsearch Service...

Learning Objectives: - Understand how to easily build an end to end, real time log analytics solution - Get an overview of collecting and processing data in real-time using Amazon Kinesis - Learn how to Interactively query and visualize your log data using Amazon Elasticsearch Service Log analytics is a common big data use case that allows you to analyze log data from websites, mobile devices, servers, sensors, and more for a wide variety of applications such as digital marketing, application monitoring, fraud detection, ad tech, gaming, and IoT. Moving your log analytics to real time can speed up your time to information allowing you to get insights in seconds or minutes instead of hours or days. In this session, you will learn how to ingest and deliver logs with no infrastructure using Amazon Kinesis Firehose. We will show how Amazon Kinesis Analytics can be used to process log data in real time to build responsive analytics. Finally, we will show how to use Amazon Elasticsearch Service to interactively query and visualize your log data.

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You can now build event-driven applications using Amazon S3 Event Notifications that trigger when objects are transitioned or expired (deleted) with S3 Lifecycle, or moved within the S3 Intelligent-Tiering storage class to its Archive Access or Deep Archive Access tiers. You can also trigger S3 Event Notifications for any changes to object tags or access control lists (ACLs). You can generate these new notifications for your entire bucket, or for a subset of your objects using prefixes or suffixes, and choose to deliver them to Amazon EventBridge, Amazon SNS, Amazon SQS, or an AWS Lambda function.

Query in Place with AWS (STG315-R1) - AWS re:Invent 2018

AWS supports a robust suite of tools and services that makes analyzing and processing large amounts of data in the cloud faster and more efficient. In this builders session, AWS storage and data experts guide you through Amazon S3, Amazon Glacier, and our query-in-place services, such as Amazon S3 Select, Amazon Glacier Select, Amazon Athena, and Amazon Redshift Spectrum. We also provide best practices around using them with other analytics services, like Amazon EMR and AWS Glue to build data lakes and deploy other analytics solutions. Understanding of a data lake construct, AWS storage services, and AWS analytics tools is recommended.

Best Practices for Building a Data Lake in Amazon S3 and Amazon Glacier, with...

Learn how to build a data lake for analytics in Amazon S3 and Amazon Glacier. In this session, we discuss best practices for data curation, normalization, and analysis on Amazon object storage services. We examine ways to reduce or eliminate costly extract, transform, and load (ETL) processes using query-in-place technology, such as Amazon Athena and Amazon Redshift Spectrum. We also review custom analytics integration using Apache Spark, Apache Hive, Presto, and other technologies in Amazon EMR. You'll also get a chance to hear from Airbnb & Viber about their solutions for Big Data analytics using S3 as a data lake.

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増え続けるAWSのデータベース・アナリティクス系サービス…。本セッションでは、2020年6月現在のAWS環境では、どんなデータベース、データ分析基盤、ETL基盤ができるのかを、サービス別・目的別にざっくりとご紹介していきます。本スライドは、Developers.IO 2020 Connectのビデオセッションで使用されたスライドです。動画本編を視聴したい場合は以下のリンクから飛べます。 https://www.youtube.com/watch?v=iEhSEj-_GKY

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Data lakes are helping enterprises of all sizes and industries make the most of their data. However, building a data lake requires consideration of your goals and an understanding of data lakes, including data ingestion, data consumption, and usability layers. In this chalk talk, AWS experts and representatives from Sysco, a Fortune 50 company and leader in food distribution and marketing, discuss parts of a data lake, design considerations, and the pros and cons of different architectural designs. They share guidance around data tracking, costs, user access, synchronization, and data integrity so that your data lake complies with governance requirements and works towards your data goals. Sysco representatives share their data lake experiences, best practices, and lessons learned. We highlight Amazon S3 and S3 Select, Amazon Athena, Amazon EMR, Amazon EC2, and Amazon Redshift Spectrum.

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Building big data applications often requires integrating a broad set of technologies to store, process, and analyze the increasing variety, velocity, and volume of data being collected by many organizations. In this session, we show how you can build entire big data applications using a core set of managed services including Amazon S3, Amazon Kinesis, Amazon EMR, Amazon Elasticsearch Service, Amazon Redshift, and Amazon QuickSight. We walk you through the steps of building and securing a big data application using the AWS Big Data Platform. We also share best practices and common use cases for AWS big data services, including tips to help you choose the best services for your specific application.

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B3 - Business intelligence apps on aws

Business intelligence is often described as a set of methodologies and technologies that transform raw data into meaningful and useful information for business purposes. But this simple description hides many technical challenges IT teams struggle with. This session will show how to build business intelligence applications leveraging AWS, from the raw data import, consumption and storage down to the information production. We will also cover best practices for services such as Amazon Redshift or Amazon RDS, and how to use applications such as SAP Hana, Jaspersoft and others.

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Data comes in many different forms that don’t easily fit into a traditional database structure. This is where data lakes help, enabling you to store vast amounts of data in its raw form. In this session, AWS experts dive into the benefits of Amazon S3 for building and managing data lakes in the AWS Cloud. Learn about the Amazon S3 integrations with the AWS analytics suite and Amazon FSx for Lustre. Also learn how to seamlessly run big data analytics, high performance computing applications, machine learning training models, media data processing workloads, and more, across your Amazon S3 data lakes.

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Amazon Web Services gives you fast access to flexible and low cost IT resources, so you can rapidly scale and build virtually any big data application including data warehousing, clickstream analytics, fraud detection, recommendation engines, event-driven ETL, serverless computing, and internet-of-things processing regardless of volume, velocity, and variety of data. https://aws.amazon.com/webinars/anz-webinar-series/

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一比一原版(QU毕业证)皇后大学毕业证成绩单

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一比一原版(ArtEZ毕业证)ArtEZ艺术学院毕业证成绩单

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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

一比一原版(RUG毕业证)格罗宁根大学毕业证成绩单

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RUG毕业证【微信95270640】办文凭{格罗宁根大学毕业证}Q微Q微信95270640RUG毕业证书成绩单/学历认证RUG Diploma未毕业、挂科怎么办？+QQ微信：Q微信95270640-大学Offer（申请大学）、成绩单（申请考研）、语言证书、在读证明、使馆公证、办真实留信网认证、真实大使馆认证、学历认证办理国外格罗宁根大学毕业证书 #成绩单改成绩 #教育部学历学位认证 #毕业证认证 #留服认证 #使馆认证（留学回国人员证明） #（证）等真实教育部认证教育部存档中国教育部留学服务中心认证（即教育部留服认证）网站100%可查. 真实使馆认证（即留学人员回国证明）使馆存档可通过大使馆查询确认. 留信网认证国家专业人才认证中心颁发入库证书留信网永久存档可查. 格罗宁根大学格罗宁根大学毕业证学历书毕业证 #成绩单等全套材料从防伪到印刷从水印到钢印烫金跟学校原版100%相同. 国际留学归国服务中心：实体公司注册经营行业标杆精益求精！国外毕业证学位证成绩单办理流程： 1客户提供办理格罗宁根大学格罗宁根大学毕业证学历书信息：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开始安排制作格罗宁根大学毕业证成绩单电子图； 3格罗宁根大学毕业证成绩单电子版做好以后发送给您确认； 4格罗宁根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格罗宁根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等快递邮寄格罗宁根大学格罗宁根大学毕业证学历书）。也得开灯开风扇山娃不想浪费电总将小方桌搁在门口看书写作业有一次山娃坐在门口写作业写着写着竟伏在桌上睡着了迷迷糊糊中山娃似乎听到了父亲的脚步声当他晃晃悠悠站起来时才诧然发现一位衣衫破旧的妇女挎着一只硕大的蛇皮袋手里拎着长铁钩正站在门口朝黑色的屋内张望不好坏人小偷山娃一怔却也灵机一动立马仰起头双手拢在嘴边朝楼上大喊：“爸爸爸——有人找——那人一听朝山娃尴尬地笑笑悻悻地走了山娃立马“嘭的一声将铁门锁死受

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一比一原版(UMich毕业证)密歇根大学|安娜堡分校毕业证成绩单

UMich毕业证【微信95270640】办理UMich毕业证【Q微信95270640】密歇根大学|安娜堡分校毕业证书原版↑制作密歇根大学|安娜堡分校学历认证文凭办理密歇根大学|安娜堡分校留信网认证,留学回国办理毕业证成绩单文凭学历认证【Q微信95270640】专业为海外学子办理毕业证成绩单、文凭制作，学历仿制，回国人员证明、做文凭，研究生、本科、硕士学历认证、留信认证、结业证、学位证书样本、美国教育部认证百分百真实存档可查】【本科硕士】密歇根大学|安娜堡分校密歇根大学|安娜堡分校硕士毕业证成绩单（GPA修改）；学历认证（教育部认证）；大学Offer录取通知书留信认证使馆认证；雅思语言证书等高仿类证书。办理流程： 1客户提供办理密歇根大学|安娜堡分校密歇根大学|安娜堡分校硕士毕业证成绩单信息：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）真实网上可查的证明材料 1教育部学历学位认证留服官网真实存档可查永久存档。 2留学回国人员证明（使馆认证）使馆网站真实存档可查。我们对海外大学及学院的毕业证成绩单所使用的材料尺寸大小防伪结构（包括：密歇根大学|安娜堡分校密歇根大学|安娜堡分校硕士毕业证成绩单隐形水印阴影底纹钢印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）都有原版本文凭对照。质量得到了广大海外客户群体的认可同时和海外学校留学中介做到与时俱进及时掌握各大院校的（毕业证成绩单资格证结业证录取通知书在读证明等相关材料）的版本更新信息能够在第一时间掌握最新的海外学历文凭的样版尺寸大小纸张材质防伪技术等等并在第一时间收集到原版实物以求达到客户的需求。本公司还可以按照客户原版印刷制作且能够达到客户理想的要求。有需要办理证件的客户请联系我们在线客服中心微信：95270640 或咨询在线在我脑海中回旋很幸运我有这样的机会能将埋在心底深深的感激之情得以表达从婴儿的“哇哇坠地到哺育我长大您们花去了多少的心血与汗水编织了多少个日日夜夜从上小学到初中乃至大学燃烧着自己默默奉献着光和热全天下的父母都是仁慈的无私的伟大的所以无论何时何地都不要忘记您们是这样的无私将一份沉甸甸的爱和希望传递到我们的眼里和心中而我应做的就是感恩父母回报社会共创和谐父母对我们的恩情深厚而无私孝敬父母是我们做人元

一比一原版(UPenn毕业证)宾夕法尼亚大学毕业证成绩单

UPenn毕业证【微信95270640】办理宾夕法尼亚大学毕业证原版一模一样、UPenn毕业证制作【Q微信95270640】《宾夕法尼亚大学毕业证购买流程》《UPenn成绩单制作》宾夕法尼亚大学毕业证书UPenn毕业证文凭宾夕法尼亚大学本科毕业证书,学历学位认证如何办理【留学国外学位学历认证、毕业证、成绩单、大学Offer、雅思托福代考、语言证书、学生卡、高仿教育部认证等一切高仿或者真实可查认证服务】代办国外（海外）英国、加拿大、美国、新西兰、澳大利亚、新西兰等国外各大学毕业证、文凭学历证书、成绩单、学历学位认证真实可查。办国外宾夕法尼亚大学宾夕法尼亚大学硕士学位证成绩单教育部学历学位认证留信认证大使馆认证留学回国人员证明修改成绩单信封申请学校offer录取通知书在读证明offer letter。快速办理高仿国外毕业证成绩单： 1宾夕法尼亚大学毕业证+成绩单+留学回国人员证明+教育部学历认证（全套留学回国必备证明材料给父母及亲朋好友一份完美交代）; 2雅思成绩单托福成绩单OFFER在读证明等留学相关材料（申请学校转学甚至是申请工签都可以用到）。 3.毕业证 #成绩单等全套材料从防伪到印刷从水印到钢印烫金高精仿度跟学校原版100%相同。专业服务请勿犹豫联系我！联系人微信号：95270640诚招代理：本公司诚聘当地代理人员如果你有业余时间有兴趣就请联系我们。国外宾夕法尼亚大学宾夕法尼亚大学硕士学位证成绩单办理过程： 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）； 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）。我们在哪里父母对我们的爱和思念为我们的生命增加了光彩给予我们自由追求的力量生活的力量我们也不忘感恩正因为这股感恩的线牵着我们使我们在一年的结束时刻义无反顾的踏上了回家的旅途人们常说父母恩最难回报愿我能以当年爸爸妈妈对待小时候的我们那样耐心温柔地对待我将渐渐老去的父母体谅他们以反哺之心奉敬父母以感恩之心孝顺父母哪怕只为父母换洗衣服为父母喂饭送汤按摩酸痛的腰背握着父母的手扶着他们一步一步地慢慢散步.娃

一比一原版(UofM毕业证)明尼苏达大学毕业证成绩单

Boston Institute of Analytics

UofM毕业证【微信95270640】办文凭{明尼苏达大学毕业证}Q微Q微信95270640UofM毕业证书成绩单/学历认证UofM Diploma未毕业、挂科怎么办？+QQ微信：Q微信95270640-大学Offer（申请大学）、成绩单（申请考研）、语言证书、在读证明、使馆公证、办真实留信网认证、真实大使馆认证、学历认证办国外明尼苏达大学明尼苏达大学毕业证假文凭教育部学历学位认证留信认证大使馆认证留学回国人员证明修改成绩单信封申请学校offer录取通知书在读证明offer letter。快速办理高仿国外毕业证成绩单： 1明尼苏达大学毕业证+成绩单+留学回国人员证明+教育部学历认证（全套留学回国必备证明材料给父母及亲朋好友一份完美交代）; 2雅思成绩单托福成绩单OFFER在读证明等留学相关材料（申请学校转学甚至是申请工签都可以用到）。 3.毕业证 #成绩单等全套材料从防伪到印刷从水印到钢印烫金高精仿度跟学校原版100%相同。专业服务请勿犹豫联系我！联系人微信号：95270640诚招代理：本公司诚聘当地代理人员如果你有业余时间有兴趣就请联系我们。国外明尼苏达大学明尼苏达大学毕业证假文凭办理过程： 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）； 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）。有一次山娃坐在门口写作业写着写着竟伏在桌上睡着了迷迷糊糊中山娃似乎听到了父亲的脚步声当他晃晃悠悠站起来时才诧然发现一位衣衫破旧的妇女挎着一只硕大的蛇皮袋手里拎着长铁钩正站在门口朝黑色的屋内张望不好坏人小偷山娃一怔却也灵机一动立马仰起头双手拢在嘴边朝楼上大喊：“爸爸爸——有人找——那人一听朝山娃尴尬地笑笑悻悻地走了山娃立马“嘭的一声将铁门锁死心却咚咚地乱跳当山娃跟父亲说起这事时父亲很吃惊抚摸着山娃母

一比一原版(TWU毕业证)西三一大学毕业证成绩单

ocavb

TWU毕业证【微信95270640】西三一大学没毕业>办理西三一大学毕业证成绩单【微信TWU】TWU毕业证成绩单TWU学历证书TWU文凭《TWU毕业套号文凭网认证西三一大学毕业证成绩单》《哪里买西三一大学毕业证文凭TWU成绩学校快递邮寄信封》《开版西三一大学文凭》TWU留信认证本科硕士学历认证 [留学文凭学历认证(留信认证使馆认证)西三一大学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客户提供办理西三一大学西三一大学本科学位证成绩单信息：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。广州火车东站的那一刻山娃感受到了一种从未有过的激动和震撼太美了可爱的广州父亲的城山娃惊喜得几乎叫出声来山娃觉得父亲太伟大了居然能单匹马地跑到这么远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拉

Innovative Methods in Media and Communication Research by Sebastian Kubitschk...

correoyaya

社内勉強会資料_LLM Agents　　　　　　　　　　　　　　　　　　　　　　　　　　　　　　.

NABLAS株式会社

Empowering Data Analytics Ecosystem.pptx

benishzehra469

Show drafts volume_up Empowering the Data Analytics Ecosystem: A Laser Focus on Value The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem: 1. Democratize Access, Not Data: Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse. Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources. 2. Foster Collaboration with Clear Roles: Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities. Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together. 3. Leverage Advanced Analytics Strategically: AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis. Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems. 4. Prioritize Data Quality with Automation: Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues. Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors. 5. Cultivate a Data-Driven Mindset: Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making. Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action. Benefits of a Precise Ecosystem: Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency. Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights. Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement. Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation. By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.

Predicting Product Ad Campaign Performance: A Data Analysis Project Presentation

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/

Ch03-Managing the Object-Oriented Information Systems Project a.pdf

haila53

一比一原版(NYU毕业证)纽约大学毕业证成绩单