Acme data engineering case study
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Case study showing problem statement and target architecture for very high volume external event data pipeline and on premise Teradata EDW integration pipeline with Cloud OLAP Google BigQuery, Amazon Athena to support batch and real time analytics
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Content Guide - How To Improve Content Creation
Table of Contents
Table of Contents .............................................................. 3
Introduction........................................................................ 5
What Is Quality Content? .................................................. 6
Easy Ways to Write Content ............................................. 8
Quotes .............................................................................. 8
Roundups ......................................................................... 8
Interviews ......................................................................... 9
Re-Blogging ..................................................................... 10
Guest Posts ..................................................................... 10
Other Forms of Media ...................................................... 11
Slideshows ....................................................................... 11
Video Roundups ............................................................... 12
Videos With Commentary ................................................. 12
Music Roundups ............................................................... 13
Infographics ...................................................................... 13
Outsourcing Content ......................................................... 14
Conclusion......................................................................... 15
Resources ......................................................................... 16
You may ask this:
# People searched for...
1 how buzzfeed mastered content creation
2 how does content creation differ from advertising
3 how much does content creation cost
4 how to automate content creation
5 how to automate content creation via xml
6 how to do content creation
7 how to improve content creation
8 how to outsource content creation
9 how to start a content creation company
10 what do you mean by content creation
11 what does a content creation specialist do
12 what is brave frontier content creation program
13 what is content creation
14 what is content creation in marketing
15 what is content creation in websites
16 what is content creation strategy
17 what is digital content creation
18 what is multimedia content creation
19 what is video content creation
20 why is content creation important
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Content Strategy
This document provides a summary of content strategy concepts and best practices. It discusses how content strategy guides plans for creating, delivering, and governing content to achieve business goals. It also covers defining the substance and structure of content, establishing workflows and ownership, and using tools like content audits, style guides, and governance policies. The document recommends resources like books, websites, and people in the field to learn more about developing an effective content strategy.
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Follow us on Twitter: @faberNovel
Information system of amazon
Amazon was founded in 1995 as an online bookstore and has since expanded into various products and services. It uses a management information system (MIS) to support its business operations such as inventory control, logistics, point-of-sale data collection, and internal communications. The MIS includes features like shopping carts, wish lists, one-click purchasing, analytical reports, and a decision support system. Over time, Amazon has innovated its business model and MIS through strategies like expanding product offerings, implementing affiliate marketing and cloud computing services, and developing expert systems to better predict product success and customer preferences.
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70 percent of marketers report that they plan
to produce more content this year than last
year. At the same time, only 30% rate their
efforts as “effective” or “very effective.”
That means a lot of us still struggle with the
marketing aspect of content marketing. Use
the 6 steps in the following slides as a
blueprint of how to build a content strategy
that produces measurable results.
Recommended
Content Guide - How To Improve Content Creation
Table of Contents
Table of Contents .............................................................. 3
Introduction........................................................................ 5
What Is Quality Content? .................................................. 6
Easy Ways to Write Content ............................................. 8
Quotes .............................................................................. 8
Roundups ......................................................................... 8
Interviews ......................................................................... 9
Re-Blogging ..................................................................... 10
Guest Posts ..................................................................... 10
Other Forms of Media ...................................................... 11
Slideshows ....................................................................... 11
Video Roundups ............................................................... 12
Videos With Commentary ................................................. 12
Music Roundups ............................................................... 13
Infographics ...................................................................... 13
Outsourcing Content ......................................................... 14
Conclusion......................................................................... 15
Resources ......................................................................... 16
You may ask this:
# People searched for...
1 how buzzfeed mastered content creation
2 how does content creation differ from advertising
3 how much does content creation cost
4 how to automate content creation
5 how to automate content creation via xml
6 how to do content creation
7 how to improve content creation
8 how to outsource content creation
9 how to start a content creation company
10 what do you mean by content creation
11 what does a content creation specialist do
12 what is brave frontier content creation program
13 what is content creation
14 what is content creation in marketing
15 what is content creation in websites
16 what is content creation strategy
17 what is digital content creation
18 what is multimedia content creation
19 what is video content creation
20 why is content creation important
MVP: What it is and why we all (should) care
The document discusses the concept of Minimum Viable Product (MVP) and its importance for product development. It defines MVP as the fastest and cheapest thing that can be built and put in customers' hands to observe how they use it and incorporate their feedback. The document outlines three approaches to MVP - using low-fidelity sketches, selling a "fake" product before building it, and building with few features. It emphasizes getting feedback from customers as soon as possible through MVPs to rapidly iterate the product based on learning.
A humorous look at marketing
This document humorously presents different types of marketing approaches through examples of pick-up lines used at a party to attract a gorgeous girl. It describes direct marketing, advertising, telemarketing, public relations, brand recognition, customer feedback, content marketing, anticipation marketing, word-of-mouth marketing, thought leadership marketing, pick-up marketing, social marketing, in-your-face marketing, stealth marketing, shadow marketing, permission marketing, cloud marketing, multi-level marketing, guerrilla marketing, green marketing, and disruptive marketing. The document is meant to be shared and enjoyed for its humorous take on various marketing flavors.
Content Strategy
This document provides a summary of content strategy concepts and best practices. It discusses how content strategy guides plans for creating, delivering, and governing content to achieve business goals. It also covers defining the substance and structure of content, establishing workflows and ownership, and using tools like content audits, style guides, and governance policies. The document recommends resources like books, websites, and people in the field to learn more about developing an effective content strategy.
Amazon.com: the Hidden Empire - Update 2013
Our "most favorited" 2011 study revealing Amazon.com's strategies for dominating online retail has been updated to include analyses on all of the company's latest moves, and insights into where they may be going next.
Follow us on Twitter: @faberNovel
Information system of amazon
Amazon was founded in 1995 as an online bookstore and has since expanded into various products and services. It uses a management information system (MIS) to support its business operations such as inventory control, logistics, point-of-sale data collection, and internal communications. The MIS includes features like shopping carts, wish lists, one-click purchasing, analytical reports, and a decision support system. Over time, Amazon has innovated its business model and MIS through strategies like expanding product offerings, implementing affiliate marketing and cloud computing services, and developing expert systems to better predict product success and customer preferences.
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Startups need a clear understanding of their market segment and show know about SAM, TAM abd SOM. As a Mentor in StartupWeekend and NEXT program, I have noticed that many entrepreneurs are not familiar with these terms. Here’s a quick explanation of what they mean, as an addition to this useful slides (Copyright notes inside) :TAM is Your Total Available or Addressable Market (everyone you wish to reach with your product) , SAM is Your Segmented Addressable Market or Served Available Market (the portion of TAM you will target), SOM is Your Share of the Market (the subset of your SAM that you will realistically reach – particularly in the first few years of your business), SOM is known as Target Market in Steve Blanc Startup Manual.
Identifying your TAM, SAM and SOM requires some market research.
6-Step Guide to B2B Content Marketing that Works
70 percent of marketers report that they plan
to produce more content this year than last
year. At the same time, only 30% rate their
efforts as “effective” or “very effective.”
That means a lot of us still struggle with the
marketing aspect of content marketing. Use
the 6 steps in the following slides as a
blueprint of how to build a content strategy
that produces measurable results.
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For more check my presentation: http://bit.ly/FMCGSlideshare
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1) Businesses are getting smarter about collecting only the most essential customer data needed to meet business goals and optimize operational efficiency.
2) Point solution tools focused on specific needs, like product analytics and search, saw the fastest growth on the Segment platform despite economic uncertainty.
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This document provides an overview of Microsoft's StreamInsight Complex Event Processing (CEP) platform. It discusses CEP concepts and benefits, the StreamInsight architecture and development environment, and deployment scenarios. The presentation aims to introduce IT professionals to CEP and Microsoft's StreamInsight solution for building event-driven applications that process streaming data with low latency.
Hadoop World 2011: Building Web Analytics Processing on Hadoop at CBS Interac...
Michael Sun presented on CBS Interactive's use of Hadoop for web analytics processing. Some key points:
- CBS Interactive processes over 1 billion web logs daily from hundreds of websites on a Hadoop cluster with over 1PB of storage.
- They developed an ETL framework called Lumberjack in Python for extracting, transforming, and loading data from web logs into Hadoop and databases.
- Lumberjack uses streaming, filters, and schemas to parse, clean, lookup dimensions, and sessionize web logs before loading into a data warehouse for reporting and analytics.
- Migrating to Hadoop provided significant benefits including reduced processing time, fault tolerance, scalability, and cost effectiveness compared to their
AnalysisServices
The document summarizes Microsoft's SQL Server 2005 Analysis Services (SSAS). It provides an overview of SSAS capabilities such as data mining algorithms, unified dimensional modeling, scalability features, and integrated manageability with SQL Server. It also describes demos of the OLAP and data mining capabilities and how SSAS can be deployed and managed for scalability, availability, and serviceability.
Flash session -streaming--ses1243-lon
Oracle OpenWorld London - session for Stream Analysis, time series analytics, streaming ETL, streaming pipelines, big data, kafka, apache spark, complex event processing
The Future of Data Science and Machine Learning at Scale: A Look at MLflow, D...
Many had dubbed 2020 as the decade of data. This is indeed an era of data zeitgeist.
From code-centric software development 1.0, we are entering software development 2.0, a data-centric and data-driven approach, where data plays a central theme in our everyday lives.
As the volume and variety of data garnered from myriad data sources continue to grow at an astronomical scale and as cloud computing offers cheap computing and data storage resources at scale, the data platforms have to match in their abilities to process, analyze, and visualize at scale and speed and with ease — this involves data paradigm shifts in processing and storing and in providing programming frameworks to developers to access and work with these data platforms.
In this talk, we will survey some emerging technologies that address the challenges of data at scale, how these tools help data scientists and machine learning developers with their data tasks, why they scale, and how they facilitate the future data scientists to start quickly.
In particular, we will examine in detail two open-source tools MLflow (for machine learning life cycle development) and Delta Lake (for reliable storage for structured and unstructured data).
Other emerging tools such as Koalas help data scientists to do exploratory data analysis at scale in a language and framework they are familiar with as well as emerging data + AI trends in 2021.
You will understand the challenges of machine learning model development at scale, why you need reliable and scalable storage, and what other open source tools are at your disposal to do data science and machine learning at scale.
Creating a Modern Data Architecture for Digital Transformation
By managing Data in Motion, Data at Rest, and Data in Use differently, modern Information Management Solutions are enabling a whole range of architecture and design patterns that allow enterprises to fully harness the value in data flowing through their systems. In this session we explored some of the patterns (e.g. operational data lakes, CQRS, microservices and containerisation) that enable CIOs, CDOs and senior architects to tame the data challenge, and start to use data as a cross-enterprise asset.
PayPal datalake journey | teradata - edge of next | san diego | 2017 october ...
PayPal Data Lake Journey | 2017-Oct | San Diego | Teradata Edge of Next
Gimel [http://www.gimel.io] is a Big Data Processing Library, open sourced by PayPal.
https://www.youtube.com/watch?v=52PdNno_9cU&t=3s
Gimel empowers analysts, scientists, data engineers alike to access a variety of Big Data / Traditional Data Stores - with just SQL or a single line of code (Unified Data API).
This is possible via the Catalog of Technical properties abstracted from users, along with a rich collection of Data Store Connectors available in Gimel Library.
A Catalog provider can be Hive or User Supplied (runtime) or UDC.
In addition, PayPal recently open sourced UDC [Unified Data Catalog], which can host and serve the Technical Metatada of the Data Stores & Objects. Visit http://www.unifieddatacatalog.io to experience first hand.
Pivotal Digital Transformation Forum: Journey to Become a Data-Driven Enterprise
The document discusses Pivotal's Big Data Suite for helping enterprises become data-driven. It outlines challenges in analyzing large amounts of data and the value that can be gained. The suite includes tools for ingesting, processing, storing and analyzing streaming and batch data at scale. It also provides examples of how the suite can be used for applications like financial compliance monitoring and connected cars.
Self-serve analytics journey at Celtra: Snowflake, Spark, and Databricks
Celtra provides a platform for streamlined ad creation and campaign management used by customers including Porsche, Taco Bell, and Fox to create, track, and analyze their digital display advertising. Celtra’s platform processes billions of ad events daily to give analysts fast and easy access to reports and ad hoc analytics. Celtra’s Grega Kešpret leads a technical dive into Celtra’s data-pipeline challenges and explains how it solved them by combining Snowflake’s cloud data warehouse with Spark to get the best of both.
Topics include:
- Why Celtra changed its pipeline, materializing session representations to eliminate the need to rerun its pipeline
- How and why it decided to use Snowflake rather than an alternative data warehouse or a home-grown custom solution
- How Snowflake complemented the existing Spark environment with the ability to store and analyze deeply nested data with full consistency
- How Snowflake + Spark enables production and ad hoc analytics on a single repository of data
ADV Slides: When and How Data Lakes Fit into a Modern Data Architecture
Whether to take data ingestion cycles off the ETL tool and the data warehouse or to facilitate competitive Data Science and building algorithms in the organization, the data lake – a place for unmodeled and vast data – will be provisioned widely in 2020.
Though it doesn’t have to be complicated, the data lake has a few key design points that are critical, and it does need to follow some principles for success. Avoid building the data swamp, but not the data lake! The tool ecosystem is building up around the data lake and soon many will have a robust lake and data warehouse. We will discuss policy to keep them straight, send data to its best platform, and keep users’ confidence up in their data platforms.
Data lakes will be built in cloud object storage. We’ll discuss the options there as well.
Get this data point for your data lake journey.
Unlocking Operational Intelligence from the Data Lake
The document discusses unlocking operational intelligence from data lakes using MongoDB. It begins by describing how digital transformation is driving changes in data volume, velocity, and variety. It then discusses how MongoDB can help operationalize data lakes by providing real-time access and analytics on data stored in data lakes, while also integrating batch processing capabilities. The document provides an example reference architecture of how MongoDB can be used with a data lake (Hadoop) and stream processing framework (Kafka) to power operational applications and machine learning models with both real-time and batch data and analytics.
Otimizações de Projetos de Big Data, Dw e AI no Microsoft Azure
Apresentação para a Agência Nacional de Aviação Civil sobre Otimzações de Projetos de Big Data, Dw e AI
Big Data Simplified - Is all about Ab'strakSHeN
This document discusses designing a new big data platform to replace an existing complex and outdated one. It analyzes challenges with the current platform, including inability to keep up with business needs. The proposed new platform called Dredge would use abstraction layers to integrate big data tools in a loosely coupled and scalable way. This would simplify development and maintenance while supporting business goals. Key aspects of Dredge include declarative configuration, logical workflows, and plug-and-play integration of tools like HDFS, Hive, HBase, Kafka and Spark in a reusable and event-driven manner. The new platform aims to improve scalability, reduce costs and better support analytics needs over time.
Designing a Feedback Loop for Event-Driven Data Sharing With Teresa Wang | Cu...
This document summarizes a presentation about designing an automated data transport system using Kafka Connect and KSQL. The system handles complex data dependencies across tables while respecting business rules. It establishes a feedback loop to notify the source system about successful deliveries and errors. The solution uses Kafka components like streams and tables, source and sink connectors, and microservices. Data is extracted from multiple source tables and written to topics. Sink connectors load the data to the destination. KSQL is used for aggregations to declare when an event is complete or needs alerting. The design leverages existing tools with minimal custom coding.
The Hidden Value of Hadoop Migration
Many organizations focus on the licensing cost of Hadoop when considering migrating to a cloud platform. But other costs should be considered, as well as the biggest impact, which is the benefit of having a modern analytics platform that can handle all of your use cases. This session will cover lessons learned in assisting hundreds of companies to migrate from Hadoop to Databricks.
Similar to Acme data engineering case study (20)
Gimel and PayPal Notebooks @ TDWI Leadership Summit Orlando
Gimel and PayPal Notebooks @ TDWI Leadership Summit Orlando
Data Platform Architecture Principles and Evaluation Criteria
Data Platform Architecture Principles and Evaluation Criteria
Building Analytic Apps for SaaS: “Analytics as a Service”
Building Analytic Apps for SaaS: “Analytics as a Service”
Webinar future dataintegration-datamesh-and-goldengatekafka
Webinar future dataintegration-datamesh-and-goldengatekafka
Building a Pluggable Analytics Stack with Cassandra (Jim Peregord, Element Co...
Building a Pluggable Analytics Stack with Cassandra (Jim Peregord, Element Co...
Microsoft SQL Server - StreamInsight Overview Presentation
Microsoft SQL Server - StreamInsight Overview Presentation
Hadoop World 2011: Building Web Analytics Processing on Hadoop at CBS Interac...
Hadoop World 2011: Building Web Analytics Processing on Hadoop at CBS Interac...
The Future of Data Science and Machine Learning at Scale: A Look at MLflow, D...
The Future of Data Science and Machine Learning at Scale: A Look at MLflow, D...
Creating a Modern Data Architecture for Digital Transformation
Creating a Modern Data Architecture for Digital Transformation
PayPal datalake journey | teradata - edge of next | san diego | 2017 october ...
PayPal datalake journey | teradata - edge of next | san diego | 2017 october ...
Pivotal Digital Transformation Forum: Journey to Become a Data-Driven Enterprise
Pivotal Digital Transformation Forum: Journey to Become a Data-Driven Enterprise
Self-serve analytics journey at Celtra: Snowflake, Spark, and Databricks
Self-serve analytics journey at Celtra: Snowflake, Spark, and Databricks
ADV Slides: When and How Data Lakes Fit into a Modern Data Architecture
ADV Slides: When and How Data Lakes Fit into a Modern Data Architecture
Unlocking Operational Intelligence from the Data Lake
Unlocking Operational Intelligence from the Data Lake
Otimizações de Projetos de Big Data, Dw e AI no Microsoft Azure
Otimizações de Projetos de Big Data, Dw e AI no Microsoft Azure
Designing a Feedback Loop for Event-Driven Data Sharing With Teresa Wang | Cu...
Designing a Feedback Loop for Event-Driven Data Sharing With Teresa Wang | Cu...
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在线办理(英国UCA毕业证书)创意艺术大学毕业证在读证明一模一样
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如果您处于以下几种情况：
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留信网认证的作用:
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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
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
一比一原版(GWU,GW文凭证书)乔治·华盛顿大学毕业证如何办理
毕业原版【微信:176555708】【(GWU,GW毕业证书)乔治·华盛顿大学毕业证】【微信:176555708】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：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）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，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
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二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信176555708】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，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，快捷的绿色通道帮您快速整合材料，让您少走弯路。
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
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
一比一原版(UO毕业证)渥太华大学毕业证如何办理
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文凭办理流程：
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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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6快递给客户（国内顺丰国外DHLUPS等快读邮寄）。
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高精端提供以下服务：
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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可查
四：留信认证留学生信息网站可查
五：与学校颁发的相关证件1:1纸质尺寸制定（定期向各大院校毕业生购买最新版本毕,业证成绩单保证您拿到的是鲁昂大学内部最新版本毕业证成绩单微信95270640）
A.为什么留学生需要操作留信认证?
留信认证全称全国留学生信息服务网认证,隶属于北京中科院。①留信认证门槛条件更低,费用更美丽,并且包过,完单周期短,效率高②留信认证虽然不能去国企,但是一般的公司都没有问题,因为国内很多公司连基本的留学生学历认证都不了解。这对于留学生来说,这就比自己光拿一个证书更有说服力,因为留学学历可以在留信网站上进行查询!
B.为什么我们提供的毕业证成绩单具有使用价值？
查询留服认证是国内鉴别留学生海外学历的唯一途径但认证只是个体行为不是所有留学生都操作所以没有办理认证的留学生的学历在国内也是查询不到的他们也仅仅只有一张文凭。所以这时候我们提供的和学校颁发的一模一样的毕业证成绩单就有了使用价值。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的头说还好醒得及时要不家早被人掏空了到时连电视也没得看啰不过父亲还是夸山娃能临危不乱随机应变有胆有谋山娃笑笑说那都是书上学的看童话和小说时多
Analysis insight about a Flyball dog competition team's performance
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
Learn SQL from basic queries to Advance queries
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
The Building Blocks of QuestDB, a Time Series Database
Talk Delivered at Valencia Codes Meetup 2024-06.
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 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.
一比一原版(牛布毕业证书)牛津布鲁克斯大学毕业证如何办理
毕业原版【微信:41543339】【(牛布毕业证书)牛津布鲁克斯大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：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）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，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，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，快捷的绿色通道帮您快速整合材料，让您少走弯路。
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
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
DSSML24_tspann_CodelessGenerativeAIPipelines
Codeless Generative AI Pipelines
(GenAI with Milvus)
https://ml.dssconf.pl/user.html#!/lecture/DSSML24-041a/rate
Discover the potential of real-time streaming in the context of GenAI as we delve into the intricacies of Apache NiFi and its capabilities. Learn how this tool can significantly simplify the data engineering workflow for GenAI applications, allowing you to focus on the creative aspects rather than the technical complexities. I will guide you through practical examples and use cases, showing the impact of automation on prompt building. From data ingestion to transformation and delivery, witness how Apache NiFi streamlines the entire pipeline, ensuring a smooth and hassle-free experience.
Timothy Spann
https://www.youtube.com/@FLaNK-Stack
https://medium.com/@tspann
https://www.datainmotion.dev/
milvus, unstructured data, vector database, zilliz, cloud, vectors, python, deep learning, generative ai, genai, nifi, kafka, flink, streaming, iot, edge
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Dat...
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
办(uts毕业证书)悉尼科技大学毕业证学历证书原版一模一样
原版一模一样【微信：741003700 】【(uts毕业证书)悉尼科技大学毕业证学历证书】【微信：741003700 】学位证，留信认证（真实可查，永久存档）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。
本公司拥有海外各大学样板无数，能完美还原海外各大学 Bachelor Diploma degree, Master Degree Diploma
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、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
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
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
一比一原版(Chester毕业证书)切斯特大学毕业证如何办理
毕业原版【微信:41543339】【(Chester毕业证书)切斯特大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：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）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，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，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，快捷的绿色通道帮您快速整合材料，让您少走弯路。
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
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
Global Situational Awareness of A.I. and where its headed
You can see the future first in San Francisco.
Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.
The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.
Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.
Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
Let me tell you what we see.
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
Experts live - Improving user adoption with AI
Bekijk de slides van onze sessie Enhancing Modern Workplace Efficiency op Experts Live 2024.
一比一原版(Harvard毕业证书)哈佛大学毕业证如何办理
毕业原版【微信:41543339】【(Harvard毕业证书)哈佛大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：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）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，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，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，快捷的绿色通道帮您快速整合材料，让您少走弯路。
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
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...
This webinar will explore cutting-edge, less familiar but powerful experimentation methodologies which address well-known limitations of standard A/B Testing. Designed for data and product leaders, this session aims to inspire the embrace of innovative approaches and provide insights into the frontiers of experimentation!
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STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...
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Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...
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Acme data engineering case study
- 1. Mukul Sood
- 2. Agenda Problem Statement Specs Design Decisions/Criteria Target State Architecture Diagram Conceptual Data Model Technical Rationalization
- 3. Problem Statement Challenges in getting insights in expected SLA across the business due to lack of a unified data fabric across the multiple data sources. This includes : On premise enterprise data warehouse, databases Media publishers on web, other channels Other 3rd party sources
- 4. Specs Volume of data – 40k events/s, 100GB+ daily records High variability in data volume - spikes, bursts Real time, interactive analysis of very large Media activity data sets unified with Publisher data On premise Teradata DW Publisher data, frequent adds/inserts, infrequent updates BI Reporting Layer data sources - Big Data OLAP, Teradata Data Marts 24/7 availability, ensure data quality
- 5. Design Decisions/Criteria Lambda Architecture, ETL+ELT, Hybrid Cloud Separation of concerns, layers : Extract Load, Processing Reporting Solution evolves as technology changes Key guiding principles : Agility Performance Scalability Reliability Security Maintainability Cost
- 6. Target State Architecture Media Website1 Media Website2 Publishe r3 Teradata EDW – Data Marts On Premise Extract • Python scripts • Alteryx, Fivetran, Stitch Load • AWS S3 • Google • Bucket • Azure Blob? Transform • AWS Athena • Google • BigQuery • Azure HDInsights? Data Sources Cloud Reporting/ Visualization • Tableau • Qlik • Power BI? Full Load one time – script sql client read data, write parquet Serverless DW, Very fast OLAP Rest Api endpoints Azure HDInsights is not Serverless Facts, Dimensions Star schema Power BI No native connector for Athena, BQ Incremental – script sql client determine change, write arrow Incremental Transform Pipelines Full Load Load Pipelines Orchestration Layer – Airflow – Pipeline interaction with Load, Transform Kafka Event Layer Spark Streaming Spark Batch Load Job to OLAP OLAP – Facts, Measures processing Jenkins, Docker CI/CD for Pipelines ETL – Full (One time), Incremental (CDC) Enrich, Cook, Aggregate EDW Data with OLAP Publish Data Extracts, Cubes to Tableau, Qlik
- 7. Conceptual Data Model Dim_Date Dim_Author Dim_Article Dim_Publisher Dim_Contract_Terms Conditions Fact_Activity_Aggregate_Daily Dim_Contract Pre computed aggregate per author, article, publisher. Could serve multiple queries Very high volume table, Date partition Could be per Publisher and per Author Activity description Fact_Activity Number of articles read Number of downloads Publisher name Number of shares Duration DollarsSCD Type 2 Fact_Revenue Fact_Cost Fast Changing Dimension Confirmed Dimensions Revenue could be derived from contract revenue and activity
- 8. Technical Rationalization, Factors Data Volume, Variety, Veracity, Frequency Measures, Grain, Slice, Processing volume Query types, SLAs, Dashboards, Metrics Cost – Compute, Storage, Network, hosted infra Flexible, Scalable, Maintainable, Reliable, Secure Hybrid approach - Cloud + OnPrem Cloud for high volume external, on premise for Enterprise DW Appropriate partitions, tuning Very fast MPP columnar OLAP Athena, BigQuery Serverless DW minimize infra work Use CI/CD practices, Jenkins, Docker for Deploy ETL, ELT Layer – Orchestration (Airflow) +Pipeline Tool(Stitch) + Storage(S3, Google Bucket) + Transform (Spark) + OLAP (BigQuery, Athena) + EDW External data sources for media activity - Very high volume, bursty, spike Internal data source for Enterprise entities, source of truth Analyze query requirements. Data Model Use of partitions, keys, Fact, Dim tables Various enrichments, cooked data, aggregations in OLAP, EDW Full Load one time, Incremental Load on going Storage layer as Data Lake Data movement within cloud infra (Storage to Transform) Faster, easier, maintainable pipeline development Establish Data Quality Assurance, Use Test framework such as DBT BI Reporting Layer - Tableau, Qlik Data source – OLAP, EDW, Data Extracts Star, Snowflake schema to support Ad Hoc, Canned Cloud based Native connectors for Athena, BigQuery SQL queries. EDW, DataExtracts data
- 9. Questions??