Big Data Maturity and its Evolution
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Introduction to Big Data and its Maturity. How the evolution takes place and also we will see about the Time-Value curve
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This document discusses big data, defining it as data that is too large and complex for traditional data processing systems due to its volume, variety and velocity. It outlines the 3Vs of big data - volume, referring to the large amount of data being generated daily; variety, referring to different data formats; and velocity, referring to the speed at which data is generated and needs to be processed. The document also discusses characteristics of big data like structured, semi-structured and unstructured data, benefits of big data, challenges of capturing, storing, analyzing and presenting big data, and technologies like Hadoop and MapReduce used for big data solutions.
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Big Data is een hype. Je hoort er iedereen mee zwaaien als de Big Thing van vandaag en tot morgen. Ondanks deze Buzz is het voor ons technische mensen meer en meer een realiteit. Het zal weldra zijn vaste plaats hebben in onze gereedschapskist.
In deze sessie bekijken we wat Big Data echt is en wat je moet weten om de Big Data vragen van je klant technisch te beantwoorden.
Naast de betekenis, de verscheidene disciplines, een overzicht en architectuur gaan we ook een aantal technologieen kort van dichtbij bekijken.
- Hadoop, de computing engine, de omgeving en al zijn sattelieten.
- Neo4j, de graph database.
- ElasticSearch, de search database.
Data is the new oil
The document discusses how data has become as valuable as oil, as it can be refined into useful information and applied across many products and services. It notes that while people data is becoming less unique, comprehensive behavioral data offers great insights. Businesses are recognizing the value of data and encouraging more data creation, but must balance this with user privacy and data security concerns. Proper systems to distribute the right data to the right audiences will be key to extracting value from data, just as refining crude oil into specific forms creates significant value.
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After the two previous episodes you know the basics about Big Data. Yet, it might get a bit more complicated than that. Usually when you have to deal with data which is generated in real-time. In this case, you are dealing with Big Stream.
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#CHEDTEB
www.chedteb.eu
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Big data hype or reality
Big Data is een hype. Je hoort er iedereen mee zwaaien als de Big Thing van vandaag en tot morgen. Ondanks deze Buzz is het voor ons technische mensen meer en meer een realiteit. Het zal weldra zijn vaste plaats hebben in onze gereedschapskist.
In deze sessie bekijken we wat Big Data echt is en wat je moet weten om de Big Data vragen van je klant technisch te beantwoorden.
Naast de betekenis, de verscheidene disciplines, een overzicht en architectuur gaan we ook een aantal technologieen kort van dichtbij bekijken.
- Hadoop, de computing engine, de omgeving en al zijn sattelieten.
- Neo4j, de graph database.
- ElasticSearch, de search database.
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The document discusses how data has become as valuable as oil, as it can be refined into useful information and applied across many products and services. It notes that while people data is becoming less unique, comprehensive behavioral data offers great insights. Businesses are recognizing the value of data and encouraging more data creation, but must balance this with user privacy and data security concerns. Proper systems to distribute the right data to the right audiences will be key to extracting value from data, just as refining crude oil into specific forms creates significant value.
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Big Data, a recent phenomenon. Everyone talks about it, but do you really know what Big Data is? Join our four-part series about Big Data and you will get answers to your questions!
We will cover Introduction to Big Data and available platforms which we can use to deal with Big Data. And in the end, we are going to give you an insight into the possible future of dealing with Big Data.
After the two previous episodes you know the basics about Big Data. Yet, it might get a bit more complicated than that. Usually when you have to deal with data which is generated in real-time. In this case, you are dealing with Big Stream.
This episode of our series will be focussed on processing systems capable of dealing with Big Streams. But analysing data lacking graphical representation will not be very convenient for us. And this is where we have to use a platform capable of visualising Big Graphs. All these topics will be covered in today’s presentation.
#CHEDTEB
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2. Big data is not a "silver bullet" but a new problem-solving philosophy that seeks evidence-based decisions to increase the tempo and speed of decisions in both the private sector and government.
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- The way your people use data to achieve your organizational strategy
This will help in ways never imagined. Data are your sole non-depletable, non-degradable, durable strategic assets, and they are pervasively shared across every organizational area. Addressing existing challenges programmatically includes overcoming necessary but insufficient prerequisites and developing a disciplined, repeatable means of improving business objectives. This process (based on the theory of constraints) is where the strategic data work really occurs as organizations identify prioritized areas where better assets, literacy, and support (data strategy components) can help an organization better achieve specific strategic objectives. Then the process becomes lather, rinse, and repeat. Several complementary concepts are also covered, including:
- A cohesive argument for why data strategy is necessary for effective data governance
- An overview of prerequisites for effective strategic use of data strategy, as well as common pitfalls
- A repeatable process for identifying and removing data constraints
- The importance of balancing business operation and innovation
Fundamentals of Big Data
Big data refers to the massive amounts of data that are being created every day from sources like mobile devices, the internet, and sensors. As data volumes and variety have increased exponentially, traditional data processing tools are no longer adequate. This has led to the development of new techniques for data storage, processing, and analysis that can handle "big data". Some key aspects of big data include volume, velocity, and variety of data. Common big data uses cases include customer analytics, fraud detection, and scientific research. Terms related to big data include data pipelines, distributed processing, machine learning, and data visualization.
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2. Big data is not a "silver bullet" but a new problem-solving philosophy that seeks evidence-based decisions to increase the tempo and speed of decisions in both the private sector and government.
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First of all we define Big Data in the light of the 3 Vs: volume, velocity and variety; next we move on to redefine Big Data, and we touch the topic of a data lake. We envision that Big Data will become mainstream for small organisations as well, what we can do with Big Data, how to tackle Big Data projects, what challenges lie ahead, but what opportunities are there to reap. And of course how important data science is to find the meaning in all the data.
A Short History of Big Data
This document summarizes the history of big data from 1944 to 2013. It outlines key milestones such as the first use of the term "big data" in 1997, the growth of internet traffic in the late 1990s, Doug Laney coining the three V's of big data in 2001, and the focus of big data professionals shifting from IT to business functions that utilize data in 2013. The document serves to illustrate how data storage and analysis have evolved over time due to technological advances and changing needs.
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It is a brief overview of Big Data. It contains History, Applications and Characteristics on BIg Data.
It also includes some concepts on Hadoop.
It also gives the statistics of big data and impact of it all over the world.
Steps towards a Data Value Chain
This document discusses steps towards a data value chain, including big data, public open data, and linked (open) data. It provides definitions and examples for each topic. For big data, it discusses the large volumes of data being created and challenges in working with such data. For public open data, it outlines principles like completeness and ease of access. It also shows examples of apps using open government data. For linked open data, it discusses moving from a web of documents to a web of interconnected data through using URIs and typed links. It also shows the growth of the linked open data cloud over time.
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The document discusses how the COVID-19 pandemic has accelerated digital transformation efforts and the use of technology in businesses. It cites statistics on potential cost savings from improved construction practices and healthcare adherence enabled by new technologies. The document also shares quotes from business leaders about increased productivity, faster application approval processes, and overcoming obstacles to change since adopting remote work during the pandemic. It analyzes survey results finding that companies in the top 10% for data capabilities increased investment in technologies 3x more than peers after COVID-19 and were 4.5x more likely to have a chief data officer and a modern data stack. The conclusion encourages businesses to commit to embracing modern data stacks.
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Big data refers to a process that is used when traditional data mining and handling techniques cannot uncover the insights and meaning of the underlying data. Data that is unstructured or time sensitive or simply very large cannot be processed by relational database engines. This type of data requires a different processing approach called big data, which uses massive parallelism on readily-available hardware.
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This document provides an overview of big data, including its definition, characteristics, storage and processing. It discusses big data in terms of volume, variety, velocity and variability. Examples of big data sources like the New York Stock Exchange and social media are provided. Popular tools for working with big data like Hadoop, Spark, Storm and MongoDB are listed. The applications of big data analytics in various industries are outlined. Finally, the future growth of the big data industry and market size are projected to continue rising significantly in the coming years.
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Too often I hear the question “Can you help me with our data strategy?” Unfortunately, for most, this is the wrong request because it focuses on the least valuable component: the data strategy itself. A more useful request is: “Can you help me apply data strategically?” Yes, at early maturity phases the process of developing strategic thinking about data is more important than the actual product! Trying to write a good (must less perfect) data strategy on the first attempt is generally not productive –particularly given the widespread acceptance of Mike Tyson’s truism: “Everybody has a plan until they get punched in the face.” This program refocuses efforts on learning how to iteratively improve the way data is strategically applied. This will permit data-based strategy components to keep up with agile, evolving organizational strategies. It also contributes to three primary organizational data goals. Learn how to improve the following:
- Your organization’s data
- The way your people use data
- The way your people use data to achieve your organizational strategy
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This document provides an overview of big data and how it can be applied in the oil and gas industry. It discusses key aspects of big data including definitions, how big data analytics works and differs from conventional analytics, preparing for a big data project, potential business cases and benefits in oil and gas domains. Examples are given around using big data to better manage electrical submersible pumps, streamline drilling operations, and conduct well integrity risk analysis. The document emphasizes that big data projects require significant preparation, including defining success criteria and evaluating technology options before full implementation, in order to maximize chances of success.
06. 9534 14985-1-ed b edit dhyan
Big data refers to huge set of data which is very common these days due to the increase of internet utilities. Data generated from social media is a very common example for the same. This paper depicts the summary on big data and ways in which it has been utilized in all aspects. Data mining is radically a mode of deriving the indispensable knowledge from extensively vast fractions of data which is quite challenging to be interpreted by conventional methods. The paper mainly focuses on the issues related to the clustering techniques in big data. For the classification purpose of the big data, the existing classification algorithms are concisely acknowledged and after that, k-nearest neighbour algorithm is discreetly chosen among them and described along with an example.
Big Data: Beyond the Hype - Why Big Data Matters to You
This document discusses big data and its importance. It notes that big data is more prevalent than many realize, with most companies and industries now dealing with large volumes of various types of data. It also explains that effectively managing big data provides competitive advantages, with data-savvy companies experiencing much stronger growth rates. Additionally, the document introduces DataStax Enterprise as a solution for easily and effectively managing big data at scale through its support for Apache Cassandra, analytics capabilities, visualization tools, and enterprise services.
pwc-data-mesh.pdf
This document discusses the evolution of enterprise data platforms and introduces the concept of a data mesh as a potential next-generation architecture. It makes the following key points:
- Traditional centralized data platforms like data warehouses and data lakes have limitations around scalability and organizational bottlenecks as data use cases increase.
- A data mesh proposes a decentralized architecture with "domain ownership of data" to address these challenges. It advocates for data to be treated as a product and shared across organizational boundaries.
- A data mesh aims to enable rapid development of data use cases at scale, improve data quality/trustworthiness, and efficiently govern data - seen as the three pillars for increasing value from data.
- Many companies are
How Data Virtualization Puts Enterprise Machine Learning Programs into Produc...
Watch full webinar here: https://bit.ly/3offv7G
Presented at AI Live APAC
Advanced data science techniques, like machine learning, have proven an extremely useful tool to derive valuable insights from existing data. Platforms like Spark, and complex libraries for R, Python and Scala put advanced techniques at the fingertips of the data scientists. However, these data scientists spend most of their time looking for the right data and massaging it into a usable format. Data virtualization offers a new alternative to address these issues in a more efficient and agile way.
Watch this on-demand session to learn how companies can use data virtualization to:
- Create a logical architecture to make all enterprise data available for advanced analytics exercise
- Accelerate data acquisition and massaging, providing the data scientist with a powerful tool to complement their practice
- Integrate popular tools from the data science ecosystem: Spark, Python, Zeppelin, Jupyter, etc.
Applying Big Data
This document discusses applying big data. It begins by defining common big data buzzwords like the 3V's of volume, velocity and variety. It then discusses agile development approaches and data modeling. Several use cases for big data are presented, including customer analytics, security, and operations analysis. Metrics for measuring ROI are discussed, though they are difficult to predict. The document emphasizes that formulating the right questions is important when moving forward with big data initiatives.
¿En qué se parece el Gobierno del Dato a un parque de atracciones?
Watch full webinar here: https://bit.ly/3Ab9gYq
Imagina llegar a un parque de atracciones con tu familia y comenzar tu día sin el típico plano que te permitirá planificarte para saber qué espectáculos ver, a qué atracciones ir, donde pueden o no pueden montar los niños… Posiblemente, no podrás sacar el máximo partido a tu día y te habrás perdido muchas cosas. Hay personas que les gusta ir a la aventura e ir descubriendo poco a poco, pero cuando hablamos de negocios, ir a la aventura puede ser fatídico...
En la era de la explosión de la información repartida en distintas fuentes, el gobierno de datos es clave para garantizar la disponibilidad, usabilidad, integridad y seguridad de esa información. Asimismo, el conjunto de procesos, roles y políticas que define permite que las organizaciones alcancen sus objetivos asegurando el uso eficiente de sus datos.
La virtualización de datos, herramienta estratégica para implementar y optimizar el gobierno del dato, permite a las empresas crear una visión 360º de sus datos y establecer controles de seguridad y políticas de acceso sobre toda la infraestructura, independientemente del formato o de su ubicación. De ese modo, reúne múltiples fuentes de datos, las hace accesibles desde una sola capa y proporciona capacidades de trazabilidad para supervisar los cambios en los datos.
En este webinar aprenderás a:
- Acelerar la integración de datos provenientes de fuentes de datos fragmentados en los sistemas internos y externos y obtener una vista integral de la información.
- Activar en toda la empresa una sola capa de acceso a los datos con medidas de protección.
- Cómo la virtualización de datos proporciona los pilares para cumplir con las normativas actuales de protección de datos mediante auditoría, catálogo y seguridad de datos.
How Data Virtualization Puts Machine Learning into Production (APAC)
Watch full webinar here: https://bit.ly/3mJJ4w9
Advanced data science techniques, like machine learning, have proven an extremely useful tool to derive valuable insights from existing data. Platforms like Spark, and complex libraries for R, Python and Scala put advanced techniques at the fingertips of the data scientists. However, these data scientists spend most of their time looking for the right data and massaging it into a usable format. Data virtualization offers a new alternative to address these issues in a more efficient and agile way.
Attend this session to learn how companies can use data virtualization to:
- Create a logical architecture to make all enterprise data available for advanced analytics exercise
- Accelerate data acquisition and massaging, providing the data scientist with a powerful tool to complement their practice
- Integrate popular tools from the data science ecosystem: Spark, Python, Zeppelin, Jupyter, etc
An Encyclopedic Overview Of Big Data Analytics
This document provides an overview of big data analytics. It discusses the characteristics of big data, known as the 5 V's: volume, velocity, variety, veracity, and value. It describes how Hadoop has become the standard for storing and processing large datasets across clusters of servers. The challenges of big data are also summarized, such as dealing with the speed, scale, and inconsistencies of data from a variety of structured and unstructured sources.
Big data issues and challenges
Big data is everywhere , although sometimes we may not immediately realize it . First thing to be believed is that most of us don't deal with large amount of data in our life except in unusual circumstance. Lacking this immediate experience, we often fail to understand both opportunities as well challenges presented by big data. There are currently a number of issues and challenges in addressing these characteristics going forward.
Agile Data Management with Enterprise Data Fabric (ASEAN)
Watch full webinar here: https://bit.ly/3juxqaw
In a world where machine learning and artificial intelligence are changing our everyday lives, digital transformation tops the strategic agenda in many private and government organizations. Data is becoming the lifeblood of a company, flowing seamlessly through it to enable deep business insights, create new opportunities, and optimize operations.
Chief Data Officers and Data Architects are under continuous pressure to find the best ways to manage the overwhelming volumes of the data that tend to become more and more distributed and diverse.
Moving data physically to a single location for reporting and analytics is not an option anymore – this is the fact accepted by the majority of the data professionals.
Join us for this webinar to know about the modern virtual data landscapes including:
- Virtual Data Fabric
- Data Mesh
- Multi-Cloud Hybrid architecture
- and to learn how to leverage the Denodo Data Virtualization platform to implement these modern data architectures.
Solving The Data Growth Crisis: Solix Big Data Suite
Today’s Chief Information Officer operates in a perfect storm of data growth. Left unchecked data growth negatively impacts application performance, compliance goals and IT costs. Yet, this very same data is the lifeblood of today’s organizations. .
What is Big Data Pipe?
"Data pipelines" are a collection of processes that transmit data from one location to another location.
The end-to-end process of gathering data, turning it into insights and models, disseminating insights, and applying the model whenever and wherever the action is required to achieve the business goal is stitched together by a data pipeline.
Architects and developers have had to adjust to "big data" because of the significantly increased volume, diversity, and velocity of data in recent years.
In memory big data management and processing
This document provides an overview of big data, including its definition, characteristics, sources, tools, benefits, and future. Big data refers to the large volumes of diverse data that are growing exponentially and can be analyzed to reveal insights. The key characteristics are volume, velocity, and variety. Big data comes from a variety of sources and is processed using distributed servers, storage, and processing as well as high-performance databases. Analyzing big data can improve customer service, increase efficiency, and enhance accuracy. The future of big data includes industry standards and security improvements.
Big Data et eGovernment
A l'occasion de l'eGov Innovation Day 2014 - DONNÉES DE L’ADMINISTRATION, UNE MINE (qui) D’OR(t) - Philippe Cudré-Mauroux présente Big Data et eGovernment.
Similar to Big Data Maturity and its Evolution (20)
Essential Prerequisites for Maximizing Success from Big Data
Essential Prerequisites for Maximizing Success from Big Data
Big Data: Beyond the Hype - Why Big Data Matters to You
Big Data: Beyond the Hype - Why Big Data Matters to You
How Data Virtualization Puts Enterprise Machine Learning Programs into Produc...
How Data Virtualization Puts Enterprise Machine Learning Programs into Produc...
¿En qué se parece el Gobierno del Dato a un parque de atracciones?
¿En qué se parece el Gobierno del Dato a un parque de atracciones?
How Data Virtualization Puts Machine Learning into Production (APAC)
How Data Virtualization Puts Machine Learning into Production (APAC)
Agile Data Management with Enterprise Data Fabric (ASEAN)
Agile Data Management with Enterprise Data Fabric (ASEAN)
Solving The Data Growth Crisis: Solix Big Data Suite
Solving The Data Growth Crisis: Solix Big Data Suite
Recently uploaded
End-to-end pipeline agility - Berlin Buzzwords 2024
We describe how we achieve high change agility in data engineering by eliminating the fear of breaking downstream data pipelines through end-to-end pipeline testing, and by using schema metaprogramming to safely eliminate boilerplate involved in changes that affect whole pipelines.
A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more.
A long time ago, we suffered at Spotify from fear of changing pipelines due to not knowing what the impact might be downstream. We made plans for a technical solution to test pipelines end-to-end to mitigate that fear, but the effort failed for cultural reasons. We eventually solved this challenge, but in a different context. In this presentation we will describe how we test full pipelines effectively by manipulating workflow orchestration, which enables us to make changes in pipelines without fear of breaking downstream.
Making schema changes that affect many jobs also involves a lot of toil and boilerplate. Using schema-on-read mitigates some of it, but has drawbacks since it makes it more difficult to detect errors early. We will describe how we have rejected this tradeoff by applying schema metaprogramming, eliminating boilerplate but keeping the protection of static typing, thereby further improving agility to quickly modify data pipelines without fear.
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】
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176555708】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！
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...
The Modern Marketing Reckoner (MMR) is a comprehensive resource packed with POVs from 60+ industry leaders on how AI is transforming the 4 key pillars of marketing – product, place, price and promotions.
一比一原版(UCSF文凭证书)旧金山分校毕业证如何办理
毕业原版【微信:176555708】【(UCSF毕业证书)旧金山分校毕业证】【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】
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176555708】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！
06-12-2024-BudapestDataForum-BuildingReal-timePipelineswithFLaNK AIM
06-12-2024-BudapestDataForum-BuildingReal-timePipelineswithFLaNK AIM
by
Timothy Spann
Principal Developer Advocate
https://budapestdata.hu/2024/en/
https://budapestml.hu/2024/en/
tim.spann@zilliz.com
https://www.linkedin.com/in/timothyspann/
https://x.com/paasdev
https://github.com/tspannhw
https://www.youtube.com/@flank-stack
milvus
vector database
gen ai
generative ai
deep learning
machine learning
apache nifi
apache pulsar
apache kafka
apache flink
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(UMN文凭证书)明尼苏达大学毕业证如何办理
毕业原版【微信:176555708】【(UMN毕业证书)明尼苏达大学毕业证】【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】
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176555708】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！
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
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
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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
Predictably Improve Your B2B Tech Company's Performance by Leveraging Data
Harness the power of AI-backed reports, benchmarking and data analysis to predict trends and detect anomalies in your marketing efforts.
Peter Caputa, CEO at Databox, reveals how you can discover the strategies and tools to increase your growth rate (and margins!).
From metrics to track to data habits to pick up, enhance your reporting for powerful insights to improve your B2B tech company's marketing.
- - -
This is the webinar recording from the June 2024 HubSpot User Group (HUG) for B2B Technology USA.
Watch the video recording at https://youtu.be/5vjwGfPN9lw
Sign up for future HUG events at https://events.hubspot.com/b2b-technology-usa/
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"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."
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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.
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
学校原件一模一样【微信：741003700 】《(英国UCA毕业证书)创意艺术大学毕业证》【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
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
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三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
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◇回国马上就要找工作，办给用人单位看；
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◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
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
Open Source Contributions to Postgres: The Basics POSETTE 2024
Postgres is the most advanced open-source database in the world and it's supported by a community, not a single company. So how does this work? How does code actually get into Postgres? I recently had a patch submitted and committed and I want to share what I learned in that process. I’ll give you an overview of Postgres versions and how the underlying project codebase functions. I’ll also show you the process for submitting a patch and getting that tested and committed.
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Big Data Maturity and its Evolution
- 1. Big Data Maturity – Action Plan
- 2. Big Data Introduction History and 3 V’s Industry wise implementation Maturity Phase Data Science and IOT Agenda
- 3. History • Web Companies – enormous amount of Data • Google Storage • Read time not fast enough • Need to read from multiple disks • Distributed file system Challenges • Disk Failure • Re-combining data Knowing more about me, gives you better predictions than an awesome algorithm with less data. History & Storage Size Price
- 4. 3 V’s of Big Data Velocity Volume Variety Veracity? Big Data
- 5. Byte Kilo Byte Mega Byte (106) Giga Byte(109) Tera Byte(1012) Peta Byte(1015) Exa Byte(1018) 3 V’s
- 6. Big Data describes the realization of greater business intelligence by storing, processing, and analyzing data that was previously ignored or siloed due to the limitations of traditional data management technologies. Note: If you are only storing massive quantities of data, you don’t necessarily need “Big Data”
- 7. Big Data implementation: Business Fostering a Data Driven Culture. Economist Intelligence Unit. http://www.tableausoftware.com/sites/default/files/whitepapers/tableau_dataculture_130219.pdf?signin=a3841a8f840546fced0c759806b7a208 Importance of Data Analysis to the different parts of the organization (% respondents)
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- 10. Data Maturity
- 11. DATA SCIENCE: DEFINITION Drew Conway http://drewconway.com/zia/2013/3/26/the-data-science-venn-diagram
- 13. Evolution
- 14. Value – Time Curve
- 15. Define Responsibilities Get the business functions to ask the right questions Take stock of all data “worth analyzing.” Select the business functions best positioned to lead the way. Match big data initiatives with compatible business functions. Determine whether big data will yield valuable information Assess complexities and prioritize Access your technology architecture Start building a team Making Big data – The “Next Big Thing”
- 16. References • Big Data slides • Google images • Learning modules • https://www.hse.ru/data/2015/07/09/1082894102/Big%20Data%20Systems%20presentation %202015.pptx • http://pwc.blogs.com/files/data-management-maturity_are-you-ready-for-big-data.pdf • https://www.census.gov/fedcasic/fc2017/ppt/ACN_FedCASIC_CyberFinal_31Mar17v1.0- Hunt.pdf