20 липня відбувся вебінар від Java Community – “Zaloni’s Architecture for Data-Driven Design” by Максим Дем’яновський — Software Engineer, GlobalLogic.
Доповідь надасть уявлення про Data-Driven Design, основні його переваги і практичну користь, а також покаже як його можна реалізувати на практиці.
BIG DATA
Prepared By
Muhammad Abrar Uddin
Introduction
· Big Data may well be the Next Big Thing in the IT world.
· Big data burst upon the scene in the first decade of the 21st century.
· The first organizations to embrace it were online and startup firms. Firms like Google, eBay, LinkedIn, and Facebook were built around big data from the beginning.
· Like many new information technologies, big data can bring about dramatic cost reductions, substantial improvements in the time required to perform a computing task, or new product and service offerings.
What is BIG DATA?
· ‘Big Data’ is similar to ‘small data’, but bigger in
size
· but having data bigger it requires different approaches:
– Techniques, tools and architecture
· an aim to solve new problems or old problems in a better way
· Big Data generates value from the storage and processing of very large quantities of digital information that cannot be analyzed with traditional computing techniques.
What is BIG DATA
· Walmart handles more than 1 million customer transactions every hour.
· Facebook handles 40 billion photos from its user base.
· Decoding the human genome originally took 10years to process; now it can be achieved in one week.
Three Characteristics of Big Data V3s
(
Volume
Data
quantity
) (
Velocity
Data
Speed
) (
Variety
Data
Types
)
1st Character of Big Data
Volume
· A typical PC might have had 10 gigabytes of storage in 2000.
· Today, Facebook ingests 500 terabytes of new data every day.
· Boeing 737 will generate 240 terabytes of flight data during a single
flight across the US.
· The smart phones, the data they create and consume; sensors embedded into everyday objects will soon result in billions of new, constantly-updated data feeds containing environmental, location, and other information, including video.
2nd Character of Big Data
Velocity
· Clickstreams and ad impressions capture user behavior at millions of events per second
· high-frequency stock trading algorithms reflect market changes within microseconds
· machine to machine processes exchange data between billions of devices
· infrastructure and sensors generate massive log data in real- time
· on-line gaming systems support millions of concurrent users, each producing multiple inputs per second.
3rd Character of Big Data
Variety
· Big Data isn't just numbers, dates, and strings. Big Data is also geospatial data, 3D data, audio and video, and unstructured text, including log files and social media.
· Traditional database systems were designed to address smaller volumes of structured data, fewer updates or a predictable, consistent data structure.
· Big Data analysis includes different types of data
Storing Big Data
· Analyzing your data characteristics
· Selecting data sources for analysis
· Eliminating redundant data
· Establishing the role of NoSQL
· Overview of Big Data stores
· Data models: key value, graph, document, column-family
· Hadoop Distributed File System
· H.
Watch full webinar here: https://bit.ly/3puUCIc
What is Data Virtualization and why do I care? In this webinar we intend to help you understand not only what Data Virtualization is but why it's a critical component of any organization's data fabric and how it fits. How data virtualization liberates and empowers your business users via data discovery, data wrangling to generation of reusable reporting objects and data services. Digital transformation demands that we empower all consumers of data within the organization, it also demands agility too. Data Virtualization gives you meaningful access to information that can be shared by a myriad of consumers.
Watch on-demand this session to learn:
- What is Data Virtualization?
- Why do I need Data Virtualization in my organization?
- How do I implement Data Virtualization in my enterprise? Where does it fit?
Watch full webinar here: https://bit.ly/2Y0vudM
What is Data Virtualization and why do I care? In this webinar we intend to help you understand not only what Data Virtualization is but why it's a critical component of any organization's data fabric and how it fits. How data virtualization liberates and empowers your business users via data discovery, data wrangling to generation of reusable reporting objects and data services. Digital transformation demands that we empower all consumers of data within the organization, it also demands agility too. Data Virtualization gives you meaningful access to information that can be shared by a myriad of consumers.
Register to attend this session to learn:
- What is Data Virtualization?
- Why do I need Data Virtualization in my organization?
- How do I implement Data Virtualization in my enterprise?
BIG DATA
Prepared By
Muhammad Abrar Uddin
Introduction
· Big Data may well be the Next Big Thing in the IT world.
· Big data burst upon the scene in the first decade of the 21st century.
· The first organizations to embrace it were online and startup firms. Firms like Google, eBay, LinkedIn, and Facebook were built around big data from the beginning.
· Like many new information technologies, big data can bring about dramatic cost reductions, substantial improvements in the time required to perform a computing task, or new product and service offerings.
What is BIG DATA?
· ‘Big Data’ is similar to ‘small data’, but bigger in
size
· but having data bigger it requires different approaches:
– Techniques, tools and architecture
· an aim to solve new problems or old problems in a better way
· Big Data generates value from the storage and processing of very large quantities of digital information that cannot be analyzed with traditional computing techniques.
What is BIG DATA
· Walmart handles more than 1 million customer transactions every hour.
· Facebook handles 40 billion photos from its user base.
· Decoding the human genome originally took 10years to process; now it can be achieved in one week.
Three Characteristics of Big Data V3s
(
Volume
Data
quantity
) (
Velocity
Data
Speed
) (
Variety
Data
Types
)
1st Character of Big Data
Volume
· A typical PC might have had 10 gigabytes of storage in 2000.
· Today, Facebook ingests 500 terabytes of new data every day.
· Boeing 737 will generate 240 terabytes of flight data during a single
flight across the US.
· The smart phones, the data they create and consume; sensors embedded into everyday objects will soon result in billions of new, constantly-updated data feeds containing environmental, location, and other information, including video.
2nd Character of Big Data
Velocity
· Clickstreams and ad impressions capture user behavior at millions of events per second
· high-frequency stock trading algorithms reflect market changes within microseconds
· machine to machine processes exchange data between billions of devices
· infrastructure and sensors generate massive log data in real- time
· on-line gaming systems support millions of concurrent users, each producing multiple inputs per second.
3rd Character of Big Data
Variety
· Big Data isn't just numbers, dates, and strings. Big Data is also geospatial data, 3D data, audio and video, and unstructured text, including log files and social media.
· Traditional database systems were designed to address smaller volumes of structured data, fewer updates or a predictable, consistent data structure.
· Big Data analysis includes different types of data
Storing Big Data
· Analyzing your data characteristics
· Selecting data sources for analysis
· Eliminating redundant data
· Establishing the role of NoSQL
· Overview of Big Data stores
· Data models: key value, graph, document, column-family
· Hadoop Distributed File System
· H.
Watch full webinar here: https://bit.ly/3puUCIc
What is Data Virtualization and why do I care? In this webinar we intend to help you understand not only what Data Virtualization is but why it's a critical component of any organization's data fabric and how it fits. How data virtualization liberates and empowers your business users via data discovery, data wrangling to generation of reusable reporting objects and data services. Digital transformation demands that we empower all consumers of data within the organization, it also demands agility too. Data Virtualization gives you meaningful access to information that can be shared by a myriad of consumers.
Watch on-demand this session to learn:
- What is Data Virtualization?
- Why do I need Data Virtualization in my organization?
- How do I implement Data Virtualization in my enterprise? Where does it fit?
Watch full webinar here: https://bit.ly/2Y0vudM
What is Data Virtualization and why do I care? In this webinar we intend to help you understand not only what Data Virtualization is but why it's a critical component of any organization's data fabric and how it fits. How data virtualization liberates and empowers your business users via data discovery, data wrangling to generation of reusable reporting objects and data services. Digital transformation demands that we empower all consumers of data within the organization, it also demands agility too. Data Virtualization gives you meaningful access to information that can be shared by a myriad of consumers.
Register to attend this session to learn:
- What is Data Virtualization?
- Why do I need Data Virtualization in my organization?
- How do I implement Data Virtualization in my enterprise?
Content1. Introduction2. What is Big Data3. Characte.docxdickonsondorris
Content
1. Introduction
2. What is Big Data
3. Characteristic of Big Data
4. Storing,selecting and processing of Big Data
5. Why Big Data
6. How it is Different
7. Big Data sources
8. Tools used in Big Data
9. Application of Big Data
10. Risks of Big Data
11. Benefits of Big Data
12. How Big Data Impact on IT
13. Future of Big Data
Introduction
• Big Data may well be the Next Big Thing in the IT
world.
• Big data burst upon the scene in the first decade of the
21st century.
• The first organizations to embrace it were online and
startup firms. Firms like Google, eBay, LinkedIn, and
Facebook were built around big data from the
beginning.
• Like many new information technologies, big data can
bring about dramatic cost reductions, substantial
improvements in the time required to perform a
computing task, or new product and service offerings.
• ‘Big Data’ is similar to ‘small data’, but bigger in
size
• but having data bigger it requires different
approaches:
– Techniques, tools and architecture
• an aim to solve new problems or old problems in a
better way
• Big Data generates value from the storage and
processing of very large quantities of digital
information that cannot be analyzed with
traditional computing techniques.
What is BIG DATA?
What is BIG DATA
• Walmart handles more than 1 million customer
transactions every hour.
• Facebook handles 40 billion photos from its user base.
• Decoding the human genome originally took 10years to
process; now it can be achieved in one week.
Three Characteristics of Big Data V3s
Volume
• Data
quantity
Velocity
• Data
Speed
Variety
• Data
Types
1st Character of Big Data
Volume
•A typical PC might have had 10 gigabytes of storage in 2000.
•Today, Facebook ingests 500 terabytes of new data every day.
•Boeing 737 will generate 240 terabytes of flight data during a single
flight across the US.
• The smart phones, the data they create and consume; sensors
embedded into everyday objects will soon result in billions of new,
constantly-updated data feeds containing environmental, location,
and other information, including video.
2nd Character of Big Data
Velocity
• Clickstreams and ad impressions capture user behavior at
millions of events per second
• high-frequency stock trading algorithms reflect market
changes within microseconds
• machine to machine processes exchange data between
billions of devices
• infrastructure and sensors generate massive log data in real-
time
• on-line gaming systems support millions of concurrent
users, each producing multiple inputs per second.
3rd Character of Big Data
Variety
• Big Data isn't just numbers, dates, and strings. Big
Data is also geospatial data, 3D data, audio and
video, and unstructured text, including log files and
social media.
• Traditional database systems were designed to
address smaller volumes of structured data, fewer
updates or a predictable, consistent data stru.
seminar on Big Data Technology
report on big data technology
webinar on big data technology
topic on big data technology
ppt presentation on big data technology
Learning Objective: Discuss the upcoming trends of information technology
This seminar looks at the forefront of technology trends in the community for technology leaders. As a technology professional, staying on top of trends is crucial. Below is a list of technology topics that this seminar will cover.
1. Emergence of the Mobile Cloud
Mobile distributed computing paradigm will lead to explosion of new services.
2. From Internet of Things to Web of Things
Need connectivity, internetworking to link physical and digital.
3. From Big Data to Extreme Data
Simpler analytics tools needed to leverage the data deluge.
4. The Revolution Will Be 3D
New tools; techniques bring 3D printing power to masses.
5. Supporting New Learning Styles
Online courses demand seamless, ubiquitous approach.
6. Next-generation mobile networks
Mobile infrastructure must catch up with user needs.
7. Balancing Identity and Privacy
Growing risks and concerns about social networks.
8. Smart and Connected Healthcare
Intelligent systems, assistive devices will improve health.
9. E-Government
Interoperability a big challenge to delivering information.
10. Scientific Cloud Computing
Key to solving grand challenges, pursuing breakthroughs.
At the end of this seminar, participants will be able to:
a. Explore the multiple uses of the internet.
b. Identify ways that technology can make our society more productive.
c. Examine what we give up when we advance technologically.
Geospatial Intelligence Middle East 2013_Big Data_Steven RamageSteven Ramage
Some initial considerations and discussion points around geospatial big data. Location adds context and relevance. Need to consider a number of V factors including Value.
Advanced Analytics and Machine Learning with Data VirtualizationDenodo
Watch full webinar here: https://bit.ly/32c6TnG
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 spent 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 webinar and learn:
- How data virtualization can accelerate data acquisition and massaging, providing the data scientist with a powerful tool to complement their practice
- How popular tools from the data science ecosystem: Spark, Python, Zeppelin, Jupyter, etc. integrate with Denodo
- How you can use the Denodo Platform with large data volumes in an efficient way
- About the success McCormick has had as a result of seasoning the Machine Learning and Blockchain Landscape with data virtualization
A Logical Architecture is Always a Flexible Architecture (ASEAN)Denodo
Watch full webinar here: https://bit.ly/3joZa0a
The current data landscape is fragmented, not just in location but also in terms of processing paradigms: data lakes, IoT architectures, NoSQL, and graph data stores, SaaS applications, etc. are found coexisting with relational databases to fuel the needs of modern analytics, ML, and AI. The physical consolidation of enterprise data into a central repository, although possible, is both expensive and time-consuming. A logical data warehouse is a modern data architecture that allows organizations to leverage all of their data irrespective of where the data is stored, what format it is stored in, and what technologies or protocols are used to store and access the data.
Watch this session to understand:
- What is a logical data warehouse and how to architect one
- The benefits of logical data warehouse – speed with agility
- Customer use case depicting logical architecture implementation
Watch full webinar here: https://bit.ly/2SaBj5l
You will often hear that "data is the new gold". In this context, data management is one of the areas that has received more attention by the software community in recent years. From Artificial Intelligence and Machine Learning to new ways to store and process data, the landscape for data management is in constant evolution. From the privileged perspective of an enterprise middleware platform, we at Denodo have the advantage of seeing many of these changes happen.
In this webinar we will discuss the technology trends that will drive the enterprise data strategies in the years to come. Don't miss it if you want to keep yourself informed about how to convert your data to strategic assets in order to complete the data-driven transformation in your company.
Join us for an exciting session that will cover:
- The most interesting trends in data management
- How to build a logical data fabric architecture?
- How to manage your data integration strategy in the new hybrid world?
- Our predictions on how those trends will change the data management world
- How can companies monetize the data through data-as-a-service infrastructure?
- What is the role of the voice computing in the future of data analytics?
GlobalLogic Embedded Community x ROS Ukraine Webinar "Surgical Robots"GlobalLogic Ukraine
Доповідь присвячена медицині майбутнього, малоінвазивній хірургії: розглянемо рішення із використанням роботів хірургів. Оглянемо інструментарій та звернемо увагу на речі, які можна відтворити для експериментів у домашніх умовах.
GlobalLogic Java Community Webinar #17 “SpringJDBC vs JDBC. Is Spring a Hero?”GlobalLogic Ukraine
Доповідь присвячена розгляду Spring JDBC у порівнянні зі стандартним JDBC у Java. Спікерка покаже на конкретних прикладах розподіл логіки коду за класами та як використання Spring JDBC скорочує кількість коду, який необхідно написати, і чому це відбувається.
Відео та деталі заходу: https://bit.ly/3wqEjCx
More Related Content
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Content1. Introduction2. What is Big Data3. Characte.docxdickonsondorris
Content
1. Introduction
2. What is Big Data
3. Characteristic of Big Data
4. Storing,selecting and processing of Big Data
5. Why Big Data
6. How it is Different
7. Big Data sources
8. Tools used in Big Data
9. Application of Big Data
10. Risks of Big Data
11. Benefits of Big Data
12. How Big Data Impact on IT
13. Future of Big Data
Introduction
• Big Data may well be the Next Big Thing in the IT
world.
• Big data burst upon the scene in the first decade of the
21st century.
• The first organizations to embrace it were online and
startup firms. Firms like Google, eBay, LinkedIn, and
Facebook were built around big data from the
beginning.
• Like many new information technologies, big data can
bring about dramatic cost reductions, substantial
improvements in the time required to perform a
computing task, or new product and service offerings.
• ‘Big Data’ is similar to ‘small data’, but bigger in
size
• but having data bigger it requires different
approaches:
– Techniques, tools and architecture
• an aim to solve new problems or old problems in a
better way
• Big Data generates value from the storage and
processing of very large quantities of digital
information that cannot be analyzed with
traditional computing techniques.
What is BIG DATA?
What is BIG DATA
• Walmart handles more than 1 million customer
transactions every hour.
• Facebook handles 40 billion photos from its user base.
• Decoding the human genome originally took 10years to
process; now it can be achieved in one week.
Three Characteristics of Big Data V3s
Volume
• Data
quantity
Velocity
• Data
Speed
Variety
• Data
Types
1st Character of Big Data
Volume
•A typical PC might have had 10 gigabytes of storage in 2000.
•Today, Facebook ingests 500 terabytes of new data every day.
•Boeing 737 will generate 240 terabytes of flight data during a single
flight across the US.
• The smart phones, the data they create and consume; sensors
embedded into everyday objects will soon result in billions of new,
constantly-updated data feeds containing environmental, location,
and other information, including video.
2nd Character of Big Data
Velocity
• Clickstreams and ad impressions capture user behavior at
millions of events per second
• high-frequency stock trading algorithms reflect market
changes within microseconds
• machine to machine processes exchange data between
billions of devices
• infrastructure and sensors generate massive log data in real-
time
• on-line gaming systems support millions of concurrent
users, each producing multiple inputs per second.
3rd Character of Big Data
Variety
• Big Data isn't just numbers, dates, and strings. Big
Data is also geospatial data, 3D data, audio and
video, and unstructured text, including log files and
social media.
• Traditional database systems were designed to
address smaller volumes of structured data, fewer
updates or a predictable, consistent data stru.
seminar on Big Data Technology
report on big data technology
webinar on big data technology
topic on big data technology
ppt presentation on big data technology
Learning Objective: Discuss the upcoming trends of information technology
This seminar looks at the forefront of technology trends in the community for technology leaders. As a technology professional, staying on top of trends is crucial. Below is a list of technology topics that this seminar will cover.
1. Emergence of the Mobile Cloud
Mobile distributed computing paradigm will lead to explosion of new services.
2. From Internet of Things to Web of Things
Need connectivity, internetworking to link physical and digital.
3. From Big Data to Extreme Data
Simpler analytics tools needed to leverage the data deluge.
4. The Revolution Will Be 3D
New tools; techniques bring 3D printing power to masses.
5. Supporting New Learning Styles
Online courses demand seamless, ubiquitous approach.
6. Next-generation mobile networks
Mobile infrastructure must catch up with user needs.
7. Balancing Identity and Privacy
Growing risks and concerns about social networks.
8. Smart and Connected Healthcare
Intelligent systems, assistive devices will improve health.
9. E-Government
Interoperability a big challenge to delivering information.
10. Scientific Cloud Computing
Key to solving grand challenges, pursuing breakthroughs.
At the end of this seminar, participants will be able to:
a. Explore the multiple uses of the internet.
b. Identify ways that technology can make our society more productive.
c. Examine what we give up when we advance technologically.
Geospatial Intelligence Middle East 2013_Big Data_Steven RamageSteven Ramage
Some initial considerations and discussion points around geospatial big data. Location adds context and relevance. Need to consider a number of V factors including Value.
Advanced Analytics and Machine Learning with Data VirtualizationDenodo
Watch full webinar here: https://bit.ly/32c6TnG
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 spent 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 webinar and learn:
- How data virtualization can accelerate data acquisition and massaging, providing the data scientist with a powerful tool to complement their practice
- How popular tools from the data science ecosystem: Spark, Python, Zeppelin, Jupyter, etc. integrate with Denodo
- How you can use the Denodo Platform with large data volumes in an efficient way
- About the success McCormick has had as a result of seasoning the Machine Learning and Blockchain Landscape with data virtualization
A Logical Architecture is Always a Flexible Architecture (ASEAN)Denodo
Watch full webinar here: https://bit.ly/3joZa0a
The current data landscape is fragmented, not just in location but also in terms of processing paradigms: data lakes, IoT architectures, NoSQL, and graph data stores, SaaS applications, etc. are found coexisting with relational databases to fuel the needs of modern analytics, ML, and AI. The physical consolidation of enterprise data into a central repository, although possible, is both expensive and time-consuming. A logical data warehouse is a modern data architecture that allows organizations to leverage all of their data irrespective of where the data is stored, what format it is stored in, and what technologies or protocols are used to store and access the data.
Watch this session to understand:
- What is a logical data warehouse and how to architect one
- The benefits of logical data warehouse – speed with agility
- Customer use case depicting logical architecture implementation
Watch full webinar here: https://bit.ly/2SaBj5l
You will often hear that "data is the new gold". In this context, data management is one of the areas that has received more attention by the software community in recent years. From Artificial Intelligence and Machine Learning to new ways to store and process data, the landscape for data management is in constant evolution. From the privileged perspective of an enterprise middleware platform, we at Denodo have the advantage of seeing many of these changes happen.
In this webinar we will discuss the technology trends that will drive the enterprise data strategies in the years to come. Don't miss it if you want to keep yourself informed about how to convert your data to strategic assets in order to complete the data-driven transformation in your company.
Join us for an exciting session that will cover:
- The most interesting trends in data management
- How to build a logical data fabric architecture?
- How to manage your data integration strategy in the new hybrid world?
- Our predictions on how those trends will change the data management world
- How can companies monetize the data through data-as-a-service infrastructure?
- What is the role of the voice computing in the future of data analytics?
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Доповідь присвячена медицині майбутнього, малоінвазивній хірургії: розглянемо рішення із використанням роботів хірургів. Оглянемо інструментарій та звернемо увагу на речі, які можна відтворити для експериментів у домашніх умовах.
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Доповідь присвячена розгляду Spring JDBC у порівнянні зі стандартним JDBC у Java. Спікерка покаже на конкретних прикладах розподіл логіки коду за класами та як використання Spring JDBC скорочує кількість коду, який необхідно написати, і чому це відбувається.
Відео та деталі заходу: https://bit.ly/3wqEjCx
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Про що лекція:
- Пошук схожих зображень за допомогою ШІ
- Як ШІ видаляє задній фон на фото. Розв’язання задачі сегментації.
- Ефективне навчання ШІ на основі великого масиву даних (фото).
Спікер: Олександр Мірошниченко, Senior Software Engineer, має понад 7 років досвіду в ІТ. Напрям діяльності — нейронні мережі та Deep Learning.
Що треба вивчати, щоб стати розробником штучного інтелекту та нейромереж.pptxGlobalLogic Ukraine
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- Що таке штучний інтелект зсередини та чим зумовлена його популярність
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25 квітня відбувся вебінар від JavaScript Community – “Why Is Git Rebase?”
Ганна Ліхтман — Senior Software Engineer, GlobalLogic.
Під час вебінару дізнались, що таке git history, та чому важливо тримати її в чистоті і порядку. Яка різниця між merge та rebase. Що таке інтерактивний rebase та в чому його сила не тільки на словах, але й на практиці.
GlobalLogic .NET Community Webinar #3 "Exploring Serverless with Azure Functi...GlobalLogic Ukraine
29 березня відбувся вебінар від .NET Community – “Exploring Serverless with Azure Functions”.
Спікер: Євген Павленко – Senior Software Engineer, GlobalLogic.
Поговорили на ті теми:
- Вступ до Azure Functions та Serverless;
- Типи хмарного обчислення;
- Переваги serverless;
- Функції та можливості Azure Functions.
Страх і сила помилок - IT Inside від GlobalLogic EducationGlobalLogic Ukraine
Ви дізнаєтесь:
- Що знаходиться за кулісами успішного успіху;
- Страх, що контролює тебе та робота з ним;
- Звідки береться невпевненість у власних силах;
- Чого власні помилки демотивують.
ℹ️IT Inside — це серія 30-хвилинних лекцій для охочих розпочати кар'єру в ІТ. Наші експерти відкриють залаштунки айтішного життя, обговорять поширені думки про ІТ-сферу й розкажуть те, що самі б хотіли почути на старті кар'єри.
🎬Переглянути записи попередніх лекцій IT Inside (https://youtube.com/playlist?list=PLipGbz33Ay3H5ynlB0YQ6P-16IX-pRvce).
GlobalLogic .NET Webinar #2 “Azure RBAC and Managed Identity”GlobalLogic Ukraine
24 листопада відбувся вебінар від .NET Community – “Azure RBAC and Managed Identity”.
Спікер: Євген Павленко – Senior Software Engineer, GlobalLogic.
Розповіли, що таке Azure RBAC (Role Base Access Control) і як він працює, для чого нам Azure Managed Identity та як звільнитись від використання паролів-секретів при використанні Azure.
Деталі заходу: https://bit.ly/3GSBvRx
Відкриті .NET-позиції у GlobalLogic: https://bit.ly/3ilJYCq
Долучитись до .NET Community у Facebook: https://www.facebook.com/groups/communitydotnet
GlobalLogic QA Webinar “What does it take to become a Test Engineer”GlobalLogic Ukraine
We considered:
- What attracts you to testing?
- What set of skills does the tester need?
- How to find your niche?
- Truth and fiction about testing
- Resume as a way to success
- Recommended materials
Discussed the capabilities, advantages and disadvantages of Keycloak, made a basic understanding of how it can be applied and integrated into various systems.
Speaker - Ihor Didyk, Software Engineer, GlobalLogic.
GlobalLogic Machine Learning Webinar “Advanced Statistical Methods for Linear...GlobalLogic Ukraine
31 травня відбувся вебінар для ML-спеціалістів - “Advanced Statistical Methods for Linear Regression” від спікера Віталія Мірошниченка! Ця доповідь для тих, хто добре ознайомлений із найпоширенішими моделями даних та підходами у машинному навчанні і хоче розширити знання іншими підходами.
У доповіді ми розглянули:
- Нагадування. Модель лінійної регресії і підгонка параметрів;
- Навчання батчами (великі об’єми вибірок);
- Оптимізація розрахунків у каскаді моделей;
- Модель суміші лінійних регресій;
- Оцінки методом складеного ножа матриць коваріацій.
Про спікера:
Віталій Мірошниченко — Senior ML Software Engineer, GlobalLogic. Має більше 6 років досвіду, який отримав здебільшого на проєктах, пов’язаних із Telecom, Cyber security, Retail. Активний учасник змагань Kaggle, та Аспірант КНУ.
Деталі заходу: https://bit.ly/3HkqhDB
Відкриті ML позиції у GlobalLogic: https://bit.ly/3MPC9yo
GlobalLogic Machine Learning Webinar “Statistical learning of linear regressi...GlobalLogic Ukraine
24 травня відбувся GlobalLogic Machine Learning Webinar “Statistical learning of linear regression model” від спікера Віталія Мірошніченка.
Під час вебінару ми обговорили такі теми:
- Модель лінійної регресії;
- Підгонка параметрів моделі (custom, sklearn, scipy);
- Основні теореми та асимптотика параметрів;
- Дискриптивні статистики (візуалізація результатів);
- Тести та їх інтерпретація;
- Приклади з Machine Learning.
Відео та деталі заходу - https://www.globallogic.com/ua/about/events/statistical-learning-of-linear-regression-model/?utm_source=youtube-organic&utm_medium=social&utm_campaign=statistical-learning-of-linear-regression-model
Попередня реєстрація на GL BaseCamp - https://bit.ly/BaseCampwaitinglist
GlobalLogic C++ Webinar “The Minimum Knowledge to Become a C++ Developer”GlobalLogic Ukraine
18 травня відбувся GlobalLogic C++ Webinar “The Minimum Knowledge to Become a C++ Developer” від спікера Романа Івасишина.
У доповіді ми розглянули:
- Список тем, які повинен знати С++ розробник (синтаксис мови, класи, STL, а також дізнались, для чого вчити темплейти та багатопотоковість);
- На що потрібно звернути увагу при вивченні мови;
- Деякі приховані аспекти мови;
- Практичні приклади з С++.
Відео та деталі заходу: https://bit.ly/3Gxmkee
Приєднатись до спільноти: https://www.facebook.com/groups/EmbeddedCommunity
Відкриті C++ позиції у GlobalLogic: https://bit.ly/3GzW03c
22 лютого відбувся Embedded Webinar #17 “Low-level Network Testing in Embedded Devices Development” від спікера Сергія Корнієнка.
Під час вебінару ми говорили на такі теми:
- Підхід до низькорівневого тестування мережевих протоколів;
- Інструменти, які можна використати в реальних проєктах;
- Знайдені баги та способи знаходження корневих причин на прикладі реального R&D проєкту.
Відео та деталі заходу: https://bit.ly/embedded_webinar_17
Приєднатись до спільноти: https://www.facebook.com/groups/EmbeddedCommunity
Відкриті Embedded-позиції у GlobalLogic: https://bit.ly/Embedded_Positions
11 січня відбувся вебінар “Introduction to Embedded QA”.
Під час вебінару ми поговорили на такі теми:
Огляд вбудованих систем;
Основні складнощі, що виникають під час їх тестування;
Основні напрямки та технології, які необхідно відслідковувати під час роботи з вбудованими системами.
Більше про захід: https://www.globallogic.com/ua/about/events/globallogic-webinar-introduction-to-embedded-qa/
Приємного перегляду і не забудьте залишити коментар про враження від вебінару!
9 грудня відбувся вебінар “Why Should You Learn C++ in 2021-22?”
Розглянули, наскільки популярною є C/C++ і де її можна використовувати. Поговорили про основні переваги та недоліки цієї мови програмування. Розповіли, як розвивається C/C++ і, нарешті, ми зрозуміли, як почати вивчати C/C++.
Більше про захід: https://www.globallogic.com/ua/about/events/c-webinar-why-you-should-learn-c-in-2021-22/
Приємного перегляду і не забудьте залишити коментар про враження від вебінару!
GlobalLogic Test Automation Live Testing Session “Android Behind UI — Testing...GlobalLogic Ukraine
В рамках GlobalLogic Test Automation Advent Calendar нещодавно відбувся GlobalLogic Test Automation Live Testing Session “Android Behind UI — Testing Challenges” від Дмитра Токарського, Lead Test Engineer, Quality Assurance, GlobalLogic.
Під час заходу ми говорили про те, як працює Android Debug Bridge, що стоїть за вбудованими фреймворками тестування UI та як спілкуватися з додатками та системою, якщо немає UI. Окремо поговорили про Bluetooth й окреслили бібліотеки Python для роботи с Bluetooth та сервісами Android.
Більше про захід: https://www.globallogic.com/ua/about/events/globallogic-test-automation-live-testing-session-android-behind-ui-testing-challenges/
Приємного перегляду і не забудьте залишити коментар про враження від вебінару!
Ця активність — частина заходів в рамках GlobalLogic Test Automation Advent Calendar, ще більше заходів та цікавинок за посиланням: https://bit.ly/AdventCalendar_fb
GlobalLogic Test Automation Online TechTalk “Test Driven Development as a Per...GlobalLogic Ukraine
16 грудня 2021 року відбувся GlobalLogic Test Automation Online TechTalk “Test Driven Development as a Personal Skill”! Анатолій Сахно (Software Testing Consultant, GlobalLogic) розібрав принципи TDD (розробки, керованої тестами) та приклади їх застосування. Крім того, поговорили про:
- Ефективне використання модульних тестів у повсякденних задачах;
- Використання TDD при розробці тестових фреймворків;
- Застосування принципів TDD при написанні функціональних автотестів.
Більше про захід: https://www.globallogic.com/ua/about/events/globallogic-test-automation-online-techtalk-test-driven-development-as-a-personal-skill/
Приємного перегляду і не забудьте залишити коментар про враження від TechTalk!
Ця активність — частина заходів в рамках GlobalLogic Test Automation Advent Calendar, ще більше заходів та цікавинок за посиланням: https://bit.ly/AdventCalendar_fb
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
3. Main stages of information evolution
1. The first revolution is associated with the invention of writing, which led to a giant qualitative and quantitative leap. It
became possible to transfer knowledge from generation to generation
2. The second (mid-16th century) was caused by the invention of printing, which radically changed industrial society,
culture, and the organization of activities
3. The third (the end of the 19th century) was caused by the invention of electricity, thanks to which the telegraph, the
telephone, and the radio appeared, allowing the rapid transmission and accumulation of information in any volume
4. The fourth (Information explosion) (70s of XX century) is the invention of microprocessor technology and the
appearance of the personal computer. Computers, computer networks, data transmission systems (information
communications) are created on microprocessors and integrated circuits
3
4. You have to realize that for instance the amount of
information produced by humanity before 2003 year is less
than the amount of data produced by one day in 2023
And you have to realize how much data is produced by end
of 2022: 97 zettabytes
By the end of 2022, there were 94 zettabytes of data in the
world. (Source: Bernard Marr & Co.) 1 ZB is the equivalent of
1,000 exabytes.
Do you know how much 181 zettabytes is? Let’s put it this
way: If you ever tried downloading it by yourself, it’d take you
about two billion years!
The amount of data produced by humanity
4
5. Data usage facts
● A single person generates 1.7 MB of data every second
● Facebook generates 4 PB of data daily
● One person generates 49.8 GB of IP traffic every month
● YouTubers upload 500 hours per minute means 30,000 hours of content every hour
● Video traffic makes up 82% of all consumer internet traffic
● 50% of all data will be in the cloud by 2025
● Every day created no less than 2.5 quintillion bytes! (That’s two exabytes plus 500 petabytes.)
● AWS Snowmobile has a capacity up to 100 petabytes
5
6. Data is not only numbers
We can see that we have a lot of data and garbage in
that data, by them self it does not have any sense.
And to make it became a useful information we have to
clean that data (fixing or removing incorrect, corrupted,
incorrectly formatted, duplicate, or incomplete data
within a dataset), and perform statistics for cleaned
data.
And when we will have structured information draw
conclusions for measures. And make that process
continuously help to reach incredible goals.
6
7. ● Help you make better AND smarter decisions
● Keep your business up-to-date
● Improved financial management
● Better performance & more efficient internal operations
● Creates a data-driven culture
● Better customer service
Why data is so important?
7
8. How companies use data to make decisions
Using Data To Create New Blockbuster Hit Series
They intelligently utilized the power of their data to run predictive analyses to learn what
exactly their customers would be receptive to and interested to watch.
Providing Faster & More Efficient Ride With Data
The company is able to analyze historical data and key metrics that include the number of
ride requests and trips getting fulfilled in different parts of a city as well as the time when this
is happening. This helps to gain insight into areas that have a supply crunch, allowing them
to pre-emptively inform drivers to move to areas ahead of time in order to capitalize on the
inevitable rise in demand.
Uses geographic information systems to analyze factors such as demographic
information, and traffic flow information to choose the best locations to expand into. Not only
does it help with choosing locations but it optimizes which product would best sell in
a given area. 8
9. Who makes decisions?
● Medical diagnosis
● Legal matters
● Human resources
● Ethical decision-making
● Creative industries
● Fraud detection
● Customer service
● Trading and investment
● Route management systems
● Advertising decisions
9
11. High level of component diagram
● Web and Mobile apps
● Services
● Devices and IoT
● Logs and Metrics
● Apache Spark
● Google BigQuery
● AWS Athena
● Azure Data Factory
● Data Lake
● Data Warehouse
● Databases
● Files
● Tableau
● Power BI
● Analysts
● 3th party services
Producer Storage Data Processing Analize
11
12. Future-proofing data lake stack
● Data collection and integration: allow for the collection and
integration of various types of data from different sources
● Real-time data processing: enable real-time data processing
● Data analysis: allow for the analysis of large amounts of data.
● Scalability: Data lakes can scale to meet the needs of the business.
● Efficiency: Data lakes allow for the efficient use of existing
resources, reducing costs associated with data processing and
storage
● Ease of use: Data lakes provide quick and easy access to data,
allowing users to retrieve information easily and quickly
12
13. Zaloni Data Lake architecture
● Understanding industry best practices
● Providing a template for solutioning
● Tracking a process
● Understanding structures and elements
13
15. ● Can be complex to implement and may require specialized expertise
● Architecture may be overkill for smaller organizations or those with limited data needs
● May not be well-suited for organizations that require real-time or near-real-time data processing
● Architecture may not be easily customizable to fit specific business needs or use cases
Pros and cons of Zaloni architecture
● Intuitively clear
● Access to raw and formatted data
● Flexible and scalable architecture that can accommodate different data types, formats, and sources
● Offers a modular and extensible architecture that can be customized to meet the specific needs
15
16. ● Lambda Architecture
● Kappa Architecture
● Data Mesh Architecture
● Virtualized Data Architecture
Alternative approaches
16
17. Summary
● Data is important for businesses because it can help inform decision-making, improve
operational efficiency, and identify new business opportunities
● Real-life examples of data-driven decisions include optimizing website design, improving app
usability, and informing product development
● Data storage options vary, and a data lake is a suitable choice when dealing with diverse and
unstructured data from multiple sources. It provides flexibility and agility for storing
and analyzing data
● Zaloni Data Lake architectures help to build Flexible and scalable architecture
17