Today's data economics is flawed. There is a need for a fundamental change in the way we produce, distribute and consume data. This presentation describes a solution with TileDB that can shape the future of data management.
Debunking "Purpose-Built Data Systems:": Enter the Universal DatabaseStavros Papadopoulos
Purpose-built databases and platforms have actually created more complexity, effort, and unnecessary reinvention. The status quo is a big mess. TileDB took the opposite approach.
In this presentation, Stavros, the original creator of TileDB, shared the underlying principles of the TileDB universal database built on multi-dimensional arrays, making the case for it as a true first in the data management industry.
Data is produced at a phenomenal rate
Our ability to store has grown
Users expect more sophisticated information
How?
Objective: Fit data to a model
Potential Result: Higher-level meta information that may not be obvious when looking at raw data
Similar terms
Exploratory data analysis
Data driven discovery
Deductive learning
Today's data economics is flawed. There is a need for a fundamental change in the way we produce, distribute and consume data. This presentation describes a solution with TileDB that can shape the future of data management.
Debunking "Purpose-Built Data Systems:": Enter the Universal DatabaseStavros Papadopoulos
Purpose-built databases and platforms have actually created more complexity, effort, and unnecessary reinvention. The status quo is a big mess. TileDB took the opposite approach.
In this presentation, Stavros, the original creator of TileDB, shared the underlying principles of the TileDB universal database built on multi-dimensional arrays, making the case for it as a true first in the data management industry.
Data is produced at a phenomenal rate
Our ability to store has grown
Users expect more sophisticated information
How?
Objective: Fit data to a model
Potential Result: Higher-level meta information that may not be obvious when looking at raw data
Similar terms
Exploratory data analysis
Data driven discovery
Deductive learning
At Softroniics we provide job oriented training for freshers in IT sector. We are Pioneers in all leading technologies like Android, Java, .NET, PHP, Python, Embedded Systems, Matlab, NS2, VLSI etc. We are specializiling in technologies like Big Data, Cloud Computing, Internet Of Things (iOT), Data Mining, Networking, Information Security, Image Processing, Mechanical, Automobile automation and many other. We are providing long term and short term internship also.
We are providing short term in industrial training, internship and inplant training for Btech/Bsc/MCA/MTech students. Attached is the list of Topics for Mechanical, Automobile and Mechatronics areas.
MD MANIKANDAN-9037291113,04954021113
softroniics@gmail.com
Big Data has been around long enough that there are some common issues that occur whenever an organization tries to implement and integrate it into their ecosystem. This presentation covers some of those pitfalls, which also impact traditional data warehouses/business intelligence ecosystems
Building next generation data warehousesAlex Meadows
All Things Open 2016 Talk - discussing technologies used to augment traditional data warehousing. Those technologies are:
* data vault
* anchor modeling
* linked data
* NoSQL
* data virtualization
* textual disambiguation
Join Stormpath Head of Product, Tom Abbott, to demo our new custom data search feature, answering any questions along the way. The demo will cover how to store, update, and retrieve the contents of custom data objects. This is a great way for current users to ramp up on this powerful, and much-anticipated feature.
Topics Covered:
- Storing and updating custom data
- What you can store
- Retrieving custom data
- Custom data search queries
On Friday, September 25th Devin Hopps lead us through a presentation on an Introduction to Big Data and how technology has evolved to harness the power of Big Data.
Triple stores are finally seeing mainstream use, but what exactly is all this talk about linked data? In this deck, we discuss what the semantic web is and how to map your relational data sets into a triple store database using open source software.
How Linked Data Can Speed Information DiscoveryAlex Meadows
Linked data platforms are now making it easier than ever to perform data exploration and discovery without having to wait to get the data integrated into the data warehouse. In this presentation, we discuss what linked data is and show a case study on integrating separate source systems so that scientists don't have to learn the source systems structures to get to their data.
Business Project Report on Nishat Textile Mills PakistanMuhammad Shahid
This is a complete Business Project Report of the Nishat Textile Mills Pakistan including Organization Introduction, Industry Introduction, Industry Analysis, Market Analysis, Pest Analysis, Environmental Analysis, SWOT Analysis.
At Softroniics we provide job oriented training for freshers in IT sector. We are Pioneers in all leading technologies like Android, Java, .NET, PHP, Python, Embedded Systems, Matlab, NS2, VLSI etc. We are specializiling in technologies like Big Data, Cloud Computing, Internet Of Things (iOT), Data Mining, Networking, Information Security, Image Processing, Mechanical, Automobile automation and many other. We are providing long term and short term internship also.
We are providing short term in industrial training, internship and inplant training for Btech/Bsc/MCA/MTech students. Attached is the list of Topics for Mechanical, Automobile and Mechatronics areas.
MD MANIKANDAN-9037291113,04954021113
softroniics@gmail.com
Big Data has been around long enough that there are some common issues that occur whenever an organization tries to implement and integrate it into their ecosystem. This presentation covers some of those pitfalls, which also impact traditional data warehouses/business intelligence ecosystems
Building next generation data warehousesAlex Meadows
All Things Open 2016 Talk - discussing technologies used to augment traditional data warehousing. Those technologies are:
* data vault
* anchor modeling
* linked data
* NoSQL
* data virtualization
* textual disambiguation
Join Stormpath Head of Product, Tom Abbott, to demo our new custom data search feature, answering any questions along the way. The demo will cover how to store, update, and retrieve the contents of custom data objects. This is a great way for current users to ramp up on this powerful, and much-anticipated feature.
Topics Covered:
- Storing and updating custom data
- What you can store
- Retrieving custom data
- Custom data search queries
On Friday, September 25th Devin Hopps lead us through a presentation on an Introduction to Big Data and how technology has evolved to harness the power of Big Data.
Triple stores are finally seeing mainstream use, but what exactly is all this talk about linked data? In this deck, we discuss what the semantic web is and how to map your relational data sets into a triple store database using open source software.
How Linked Data Can Speed Information DiscoveryAlex Meadows
Linked data platforms are now making it easier than ever to perform data exploration and discovery without having to wait to get the data integrated into the data warehouse. In this presentation, we discuss what linked data is and show a case study on integrating separate source systems so that scientists don't have to learn the source systems structures to get to their data.
Business Project Report on Nishat Textile Mills PakistanMuhammad Shahid
This is a complete Business Project Report of the Nishat Textile Mills Pakistan including Organization Introduction, Industry Introduction, Industry Analysis, Market Analysis, Pest Analysis, Environmental Analysis, SWOT Analysis.
Research conducted as an assignment in our class Mobile Reputations, Collaborative Consumption in Sharing Economy, at Panteion University. Featuring results about Gamers, collaborating activities in gaming, their gaming and social media habits, their mobile-self and how all of these are combined in their life. Project by Sofia-Maria Russu, Agapi Mirgioti, Nadia Sinekoglou and Elena Constadinidy.
Desenvolvendo aplicações Cross-Platform com XamarinJúnior Porfirio
Desenvolver em múltiplas plataformas tem sido um desafio para os desenvolvedores e corporações. Com Xamarin esse desafio se torna mais simples. Objetivo dessa palestra é realizar uma introdução ao tema e demonstrar através de demos o poder dessa tecnologia para as plataformas IOS, Windows Phone e Android.
Statistical Analysis of Interrelationship between Money Supply Exchange Rates...Atif Ahmed
Several researches have been conducted to study the impact of different macro-economic variables and their influence on government expenditure. By using different statistical tools researchers have examined that how money supply and exchange rate influence the government expenditure. Few other studies also conducted work on the quarterly time series data to examine the long run equilibrium association between the macroeconomic variables.
All about Big Data components and the best tools to ingest, process, store and visualize the data.
This is a keynote from the series "by Developer for Developers" powered by eSolutionsGrup.
This deck talks about the basic overview of NoSQL technologies, implementation vendors/products, case studies, and some of the core implementation algorithms. The presentation also describes a quick overview of "Polyglot Persistency", "NewSQL" like emerging trends.
The deck is targeted to beginners who wants to get an overview of NoSQL databases.
Data Lakehouse, Data Mesh, and Data Fabric (r1)James Serra
So many buzzwords of late: Data Lakehouse, Data Mesh, and Data Fabric. What do all these terms mean and how do they compare to a data warehouse? In this session I’ll cover all of them in detail and compare the pros and cons of each. I’ll include use cases so you can see what approach will work best for your big data needs.
Overview of MongoDB and Other Non-Relational DatabasesAndrew Kandels
My Minnesota PHP Usergroup (mnphp.org) presentation where I give an overview on MongoDB and other non-relational databases and their ability to solve unique, complex problems.
Data Lakehouse, Data Mesh, and Data Fabric (r2)James Serra
So many buzzwords of late: Data Lakehouse, Data Mesh, and Data Fabric. What do all these terms mean and how do they compare to a modern data warehouse? In this session I’ll cover all of them in detail and compare the pros and cons of each. They all may sound great in theory, but I'll dig into the concerns you need to be aware of before taking the plunge. I’ll also include use cases so you can see what approach will work best for your big data needs. And I'll discuss Microsoft version of the data mesh.
Data Warehouse Design and Best PracticesIvo Andreev
A data warehouse is a database designed for query and analysis rather than for transaction processing. An appropriate design leads to scalable, balanced and flexible architecture that is capable to meet both present and long-term future needs. This session covers a comparison of the main data warehouse architectures together with best practices for the logical and physical design that support staging, load and querying.
NoSQL Data Stores in Research and Practice - ICDE 2016 Tutorial - Extended Ve...Felix Gessert
The unprecedented scale at which data is consumed and generated today has shown a large demand for scalable data management and given rise to non-relational, distributed "NoSQL" database systems. Two central problems triggered this process: 1) vast amounts of user-generated content in modern applications and the resulting requests loads and data volumes 2) the desire of the developer community to employ problem-specific data models for storage and querying. To address these needs, various data stores have been developed by both industry and research, arguing that the era of one-size-fits-all database systems is over. The heterogeneity and sheer amount of these systems - now commonly referred to as NoSQL data stores - make it increasingly difficult to select the most appropriate system for a given application. Therefore, these systems are frequently combined in polyglot persistence architectures to leverage each system in its respective sweet spot. This tutorial gives an in-depth survey of the most relevant NoSQL databases to provide comparative classification and highlight open challenges. To this end, we analyze the approach of each system to derive its scalability, availability, consistency, data modeling and querying characteristics. We present how each system's design is governed by a central set of trade-offs over irreconcilable system properties. We then cover recent research results in distributed data management to illustrate that some shortcomings of NoSQL systems could already be solved in practice, whereas other NoSQL data management problems pose interesting and unsolved research challenges.
If you'd like to use these slides for e.g. teaching, contact us at gessert at informatik.uni-hamburg.de - we'll send you the PowerPoint.
This was a very interesting conference, TIC students oriented where I take him to the azure ecosystem for data warehousing architecture and best practices to reach powerful Business Intelligence Solutions according to the new era
Extract business value by analyzing large volumes of multi-structured data from various sources such as databases, websites, blogs, social media, smart sensors...
Relational databases vs Non-relational databasesJames Serra
There is a lot of confusion about the place and purpose of the many recent non-relational database solutions ("NoSQL databases") compared to the relational database solutions that have been around for so many years. In this presentation I will first clarify what exactly these database solutions are, compare them, and discuss the best use cases for each. I'll discuss topics involving OLTP, scaling, data warehousing, polyglot persistence, and the CAP theorem. We will even touch on a new type of database solution called NewSQL. If you are building a new solution it is important to understand all your options so you take the right path to success.
«Как научить Ruby / как научиться Ruby», Виктор Шепелев (Team Lead at BrandSp...Alina Vilk
Тезисы/рассматриваемые вопросы:
Стать рубистом с нуля — как? Самообразование, книги, учителя
Задать направление развития младшему коллеге и подчиненному — как?
Как определить необходимый объем знаний? Как осознать свой уровень?
Когда и зачем мы прекращаем учиться? Зачем повышать уровень?
Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Instructions for Submissions thorugh G- Classroom.pptxJheel Barad
This presentation provides a briefing on how to upload submissions and documents in Google Classroom. It was prepared as part of an orientation for new Sainik School in-service teacher trainees. As a training officer, my goal is to ensure that you are comfortable and proficient with this essential tool for managing assignments and fostering student engagement.
Palestine last event orientationfvgnh .pptxRaedMohamed3
An EFL lesson about the current events in Palestine. It is intended to be for intermediate students who wish to increase their listening skills through a short lesson in power point.
Acetabularia Information For Class 9 .docxvaibhavrinwa19
Acetabularia acetabulum is a single-celled green alga that in its vegetative state is morphologically differentiated into a basal rhizoid and an axially elongated stalk, which bears whorls of branching hairs. The single diploid nucleus resides in the rhizoid.
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdfTechSoup
In this webinar you will learn how your organization can access TechSoup's wide variety of product discount and donation programs. From hardware to software, we'll give you a tour of the tools available to help your nonprofit with productivity, collaboration, financial management, donor tracking, security, and more.
Honest Reviews of Tim Han LMA Course Program.pptxtimhan337
Personal development courses are widely available today, with each one promising life-changing outcomes. Tim Han’s Life Mastery Achievers (LMA) Course has drawn a lot of interest. In addition to offering my frank assessment of Success Insider’s LMA Course, this piece examines the course’s effects via a variety of Tim Han LMA course reviews and Success Insider comments.
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
Introduction to AI for Nonprofits with Tapp NetworkTechSoup
Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
2. Agenda
[Big]Data Source: when it becomes Big?
What cluster is? Horizontal and vertical scaling
[Big]Data Storage challenges
Disadvantages
NoSQL = Not only SQL
Most popular and trendy
3. Big Data Storage Concepts
Only stores facts (events), doesn’t analyze it
Immutable
Time series data (based on timestamps and, maybe, origin)
Store everything, delete nothing
Where: Messages (email, twitter), social networks, Sensor data (IoT), Log files,
Locations
4. Cluster. Horizontal and vertical scaling
What cluster is?
Load balancer
Communication: master/slave
architecture
Fault tolerance and replication
factor
5. Size (keep and search huge
amount of data)
Speed (data acquisition, data
search)
Availability (fault tolerance,
partition tolerance)
Big Data Storage Challenges
6. Disadvantages of Big Data Storages
No transactions (ACID)
Less mature
Big variety of concepts, lack of standardization
No BI or analytics in queries
Administration
14. Apache Cassandra: basics
Masterless architecture with read/write anywhere design
All nodes are the same
No single point of failure
Zone support
Linear scalability
CQL - cassandra query language
Availability and Partition Tolerance but Eventual Consistency
Materialized view, functions, procedures and triggers в RDBMS и что от этого ушли (пример про Oracle и финансовый отчет)
Отказ от UPDATE в пользу INSERT за счет обновленного таймстемпа
В силу предыдущего пункта данные принято называть time series
Т.к. аналитика происходит за пределами БД (batch jobs), то желательно ничего не удалять, т.к. если в наших джобах будут какие-то ошибки или проблемы - мы всегда можем их прогнать снова и получить новые результаты
Рассказать про основные источники time series данных
Определение
Коммуникационные протоколы -> master/slave architecture
Single point of failure
Распределение данных по кластеру, отказоустойчивость и репликация
Напоминание про CAP теорему
++ Меня потом спрашивали после лекции, Нужно еще раз пояснить, что это не догма, а скорее важный принцип о котором не следует забывать Трактовать тот же Consistency можно по разному
Проговорить традиционное понятие транзакции, расшифровать ACID
Пройтись по пунктам: атомарность, консистентность, изолированность, доступность (пример: перевод денег на счет)
Big Data storages появились относительно недавно, по сравнению с RDBMS
Большое кол-во концепций и реализаций для разных задач
Нормальные формы БД в RDBMS, здесь их нет, для аналитики вам нужны другие компоненты (а значит и их изучение, финансы на запуск и администрирование)
Администрирование кластера само по себе более сложная вещь
S3 - web service, HDFS - software
S3 provides eventual consistency (read-after-write)
S3 communication: REST and SOAP
S3 replication: you don’t control it, but you can enable cross-region replication
HDFS - master-slave architecture (Namenodes, datanodes)
HDFS: files splitted into parts - blocks
HDFS: automatic recovery
Adding nodes to cluster is ok, but deleting is a challenge
Здесь рассказать, почему sql запросы невозможно выполнять на NoSQL DBs
(расшифровать понятие, пройтись по UPDATE, DELETE, COMMIT, ROLLBACK для примера)
Здесь сказать про кеш на примере Redis:
Open source
In memory (Redis holds its database entirely in memory, using the disk only for persistence)
Scalable
All the Redis operations are atomic
Rich set of data types
Пример: MongoDB
JSON-based documents (set of key-value pairs)
Have dynamic schema
Supports indexing and aggregation queries
Нет смысла хранить все данные на каждом из узлов
Как распределить их по кластеру, Hash Ring
Вопрос сохранности данных: репликация
Репликация асинхронна
Протокол общения между нодам - Gossip
Каждая нода может обрабатывать запросы. Нода, на которую пришел запрос, является координатором этого запроса
Hinted handoff - если нода отпала, то какое-то время информация, которую ей нужно было передать, хранится и ждет, пока нода снова появится
![Agenda
[Big]Data Source: when it becomes Big?
What cluster is? Horizontal and vertical scaling
[Big]Data Storage challenges
Disadvantages
NoSQL = Not only SQL
Most popular and trendy](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)