SAS DataFlux Management studio training,Technical support ,Outsourcing ,DataFlux Data Management Platform
Overview of DataFlux Data Management Studio
DataFlux Methodology: Plan, Act, and Monitor
Managing Repositories
Different types of Data Connections
Creating and Managing Data Collections
Creating , Setting , Working with Data Explorations
Introduction ,Creating Business Rules and Custom Metrics
Overview, Creating , Preparing of Data Profiles
Sas dataflux management studio Training ,data flux corporate trainig bidwhm
Introduction to SAS Dataflux Management studio ,Advantages, total slides are 210 About Data job nodes, Creating business rules,Data quality case studies,DataFlux Data Management Platform
Overview of DataFlux Data Management Studio
DataFlux Methodology: Plan, Act, and Monitor
Managing Repositories
Different types of Data Connections
Creating and Managing Data Collections
Creating , Setting , Working with Data Explorations
Introduction ,Creating Business Rules and Custom Metrics
Overview, Creating , Preparing of Data Profiles
만들자! 데이터 기반의 스마트 팩토리 - 문태양 AWS 솔루션즈 아키텍트 / 배권 팀장, OCI 정보통신 :: AWS Summit Seou...Amazon Web Services Korea
제조 산업의 데이터는 내부 장치 및 장비에 담겨있기 때문에 활용되지 못하는 경우가 많습니다. AWS IoT로 산업 현장의 원격 감시 제어 데이터 (SCADA)를 수집하고 전사적 자원관리 (ERP), 제조 실행 시스템 (MES)의 데이터와 산업 현장의 데이터를 통합하여 대시보드에서 거의 실시간에 가까운 운영 메트릭을 모니터링하여 비즈니스 인사이트를 얻은 사례를 살펴봅니다.
Основы создания витрин данных - создание схемы звезда и снежинкаSergey Sukharev
Краткий курс посвященный основам моделирования хранилищ данных, построенных по классической схеме - звезда или снежинка (Dimensional Modeling). Дает представление в целом об архитектурах хранилищ данных и их компонентах.
Building the Data Lake with Azure Data Factory and Data Lake AnalyticsKhalid Salama
In essence, a data lake is commodity distributed file system that acts as a repository to hold raw data file extracts of all the enterprise source systems, so that it can serve the data management and analytics needs of the business. A data lake system provides means to ingest data, perform scalable big data processing, and serve information, in addition to manage, monitor and secure the it environment. In these slide, we discuss building data lakes using Azure Data Factory and Data Lake Analytics. We delve into the architecture if the data lake and explore its various components. We also describe the various data ingestion scenarios and considerations. We introduce the Azure Data Lake Store, then we discuss how to build Azure Data Factory pipeline to ingest the data lake. After that, we move into big data processing using Data Lake Analytics, and we delve into U-SQL.
For those contemplating re-architecting or greenfields data lakes/data hubs/data warehouses in a cloud environment, talk to our Altis AWS Practice Lead - Guillaume Jaudouin about why you should be considering the "tour de force" combination of AWS and Snowflake.
Power BI Full Course | Power BI Tutorial for Beginners | EdurekaEdureka!
YouTube Link: https://youtu.be/3u7MQz1EyPY
** Power BI Training - https://www.edureka.co/power-bi-training **
This Edureka PPT on "Power BI Full Course" will help you understand and learn Power BI in detail. This Power BI Tutorial is ideal for both beginners as well as professionals who want to master up their Power BI concepts.
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.
Sas dataflux management studio Training ,data flux corporate trainig bidwhm
Introduction to SAS Dataflux Management studio ,Advantages, total slides are 210 About Data job nodes, Creating business rules,Data quality case studies,DataFlux Data Management Platform
Overview of DataFlux Data Management Studio
DataFlux Methodology: Plan, Act, and Monitor
Managing Repositories
Different types of Data Connections
Creating and Managing Data Collections
Creating , Setting , Working with Data Explorations
Introduction ,Creating Business Rules and Custom Metrics
Overview, Creating , Preparing of Data Profiles
만들자! 데이터 기반의 스마트 팩토리 - 문태양 AWS 솔루션즈 아키텍트 / 배권 팀장, OCI 정보통신 :: AWS Summit Seou...Amazon Web Services Korea
제조 산업의 데이터는 내부 장치 및 장비에 담겨있기 때문에 활용되지 못하는 경우가 많습니다. AWS IoT로 산업 현장의 원격 감시 제어 데이터 (SCADA)를 수집하고 전사적 자원관리 (ERP), 제조 실행 시스템 (MES)의 데이터와 산업 현장의 데이터를 통합하여 대시보드에서 거의 실시간에 가까운 운영 메트릭을 모니터링하여 비즈니스 인사이트를 얻은 사례를 살펴봅니다.
Основы создания витрин данных - создание схемы звезда и снежинкаSergey Sukharev
Краткий курс посвященный основам моделирования хранилищ данных, построенных по классической схеме - звезда или снежинка (Dimensional Modeling). Дает представление в целом об архитектурах хранилищ данных и их компонентах.
Building the Data Lake with Azure Data Factory and Data Lake AnalyticsKhalid Salama
In essence, a data lake is commodity distributed file system that acts as a repository to hold raw data file extracts of all the enterprise source systems, so that it can serve the data management and analytics needs of the business. A data lake system provides means to ingest data, perform scalable big data processing, and serve information, in addition to manage, monitor and secure the it environment. In these slide, we discuss building data lakes using Azure Data Factory and Data Lake Analytics. We delve into the architecture if the data lake and explore its various components. We also describe the various data ingestion scenarios and considerations. We introduce the Azure Data Lake Store, then we discuss how to build Azure Data Factory pipeline to ingest the data lake. After that, we move into big data processing using Data Lake Analytics, and we delve into U-SQL.
For those contemplating re-architecting or greenfields data lakes/data hubs/data warehouses in a cloud environment, talk to our Altis AWS Practice Lead - Guillaume Jaudouin about why you should be considering the "tour de force" combination of AWS and Snowflake.
Power BI Full Course | Power BI Tutorial for Beginners | EdurekaEdureka!
YouTube Link: https://youtu.be/3u7MQz1EyPY
** Power BI Training - https://www.edureka.co/power-bi-training **
This Edureka PPT on "Power BI Full Course" will help you understand and learn Power BI in detail. This Power BI Tutorial is ideal for both beginners as well as professionals who want to master up their Power BI concepts.
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.
An Intro to NoSQL Databases -- NoSQL databases will not become the new dominators. Relational will still be popular, and used in the majority of situations. They, however, will no longer be the automatic choice. (source : http://martinfowler.com/)
Actionable Insights with AI - Snowflake for Data ScienceHarald Erb
Talk @ ScaleUp 360° AI Infrastructures DACH, 2021: Data scientists spend 80% and more of their time searching for and preparing data. This talk explains Snowflake’s Platform capabilities like near-unlimited data storage and instant and near-infinite compute resources and how the platform can be used to seamlessly integrate and support the machine learning libraries and tools data scientists rely on.
Introduction to Snowflake Datawarehouse and Architecture for Big data company. Centralized data management. Snowpipe and Copy into a command for data loading. Stream loading and Batch Processing.
Today’s organisations require a data storage and analytics solution that offers more agility and flexibility than traditional data management systems. Data Lake is a new and increasingly popular way to store all of your data, structured and unstructured, in one, centralised repository. Since data can be stored as-is, there is no need to convert it to a predefined schema and you no longer need to know what questions you want to ask of your data beforehand.
In this webinar, you will discover how AWS gives you fast access to flexible and low-cost IT resources, so you can rapidly scale and build your data lake that can power any kind of analytics such as data warehousing, clickstream analytics, fraud detection, recommendation engines, event-driven ETL, serverless computing, and internet-of-things processing regardless of volume, velocity and variety of data.
Learning Objectives:
• Discover how you can rapidly scale and build your data lake with AWS.
• Explore the key pillars behind a successful data lake implementation.
• Learn how to use the Amazon Simple Storage Service (S3) as the basis for your data lake.
• Learn about the new AWS services recently launched, Amazon Athena and Amazon Redshift Spectrum, that help customers directly query that data lake.
Big data architectures and the data lakeJames Serra
With so many new technologies it can get confusing on the best approach to building a big data architecture. The data lake is a great new concept, usually built in Hadoop, but what exactly is it and how does it fit in? In this presentation I'll discuss the four most common patterns in big data production implementations, the top-down vs bottoms-up approach to analytics, and how you can use a data lake and a RDBMS data warehouse together. We will go into detail on the characteristics of a data lake and its benefits, and how you still need to perform the same data governance tasks in a data lake as you do in a data warehouse. Come to this presentation to make sure your data lake does not turn into a data swamp!
AWS Glue는 고객이 분석을 위해 손쉽게 데이터를 준비하고 로드할 수 있게 지원하는 완전관리형 ETL(추출, 변환 및 로드) 서비스입니다. AWS 관리 콘솔에서 클릭 몇 번으로 ETL 작업을 생성하고 실행할 수 있습니다. 빅데이터 분석 시 다양한 데이터 소스에 대한 전처리 작업을 할 때, 별도의 데이터 처리용 서버나 인프라를 관리할 필요가 없습니다. 본 세션에서는 지난 5월 서울 리전에 출시한 Glue 서비스에 대한 자세한 소개와 함께 다양한 활용 팁을 데모와 함께 소개해 드립니다.
Standing on the Shoulders of Open-Source Giants: The Serverless Realtime Lake...HostedbyConfluent
"Unlike just a few years ago, today the lakehouse architecture is an established data platform embraced by all major cloud data companies such as AWS, Azure, Google, Oracle, Microsoft, Snowflake and Databricks.
This session kicks off with a technical, no-nonsense introduction to the lakehouse concept, dives deep into the lakehouse architecture and recaps how a data lakehouse is built from the ground up with streaming as a first-class citizen.
Then we focus on serverless for streaming use cases. Serverless concepts are well-known from developers triggering hundreds of thousands of AWS Lambda functions at a negligible cost. However, the same concept becomes more interesting when looking at data platforms.
We have all heard about the principle ""It runs best on Powerpoint"", so I decided to skip slides here and bring a serverless demo instead:
A hands-on, fun, and interactive serverless streaming use case example where we ingest live events from hundreds of mobile devices (don't miss out - bring your phone and be part of it!!). Based on this use case I will critically explore how much of a modern lakehouse is serverless and how we implemented that at Databricks (spoiler alert: serverless is everywhere from data pipelines, workflows, optimized Spark APIs, to ML).
TL;DR benefits for the Data Practitioners:
-Recap the OSS foundation of the Lakehouse architecture and understand its appeal
- Understand the benefits of leveraging a lakehouse for streaming and what's there beyond Spark Structured Streaming.
- Meat of the talk: The Serverless Lakehouse. I give you the tech bits beyond the hype. How does a serverless lakehouse differ from other serverless offers?
- Live, hands-on, interactive demo to explore serverless data engineering data end-to-end. For each step we have a critical look and I explain what it means, e.g for you saving costs and removing operational overhead."
Netflix Data Engineering @ Uber Engineering MeetupBlake Irvine
People, Platform, Projects: these slides overview how Netflix works with Big Data. I share how our teams are organized, the roles we typically have on the teams, an overview of our Big Data Platform, and two example projects.
IT organizations today need to support a modern, flexible, global workforce and ensure their users can be productive from anywhere. Moving desktops and applications to AWS offers improved security, scale, and performance with cloud economics. In this session, we provide an overview of Amazon WorkSpaces and discuss the use cases for it. Then, we dive deep into best practices for implementing Amazon WorkSpaces, including integrating with your existing identity, security, networking, and storage solutions.
Diego Magalhaes, Senior Solutions Architect, Amazon Web Services
Lambda Architecture in the Cloud with Azure Databricks with Andrei VaranovichDatabricks
The term “Lambda Architecture” stands for a generic, scalable and fault-tolerant data processing architecture. As the hyper-scale now offers a various PaaS services for data ingestion, storage and processing, the need for a revised, cloud-native implementation of the lambda architecture is arising.
In this talk we demonstrate the blueprint for such an implementation in Microsoft Azure, with Azure Databricks — a PaaS Spark offering – as a key component. We go back to some core principles of functional programming and link them to the capabilities of Apache Spark for various end-to-end big data analytics scenarios.
We also illustrate the “Lambda architecture in use” and the associated tread-offs using the real customer scenario – Rijksmuseum in Amsterdam – a terabyte-scale Azure-based data platform handles data from 2.500.000 visitors per year.
A simplified version of my presentation:
- PowerBI solution architecture
- Key steps to visualize data in PowerBI
- PowerBI Demo
- R in PowerBI
- Custom Visuals
- PowerBI Report Server
- Azure services and Power BI
Amazon Elastic File System(EFS)은 클라우드 기반 네트워크 파일 시스템으로서 손쉽게 확장 가능한 탄력적인 파일 스토리지를 제공합니다. 다수의 Amazon EC2에 탑재하거나 Direct Connect를 통해 온프레미스와 연동 가능하며, 웹 콘텐츠, 엔터프라이즈 애플리케이션, 미디어 및 DB 백업 등 다양한 워크로드에서 활용 가능합니다. 본 세션에서는 지난 5월 서울 리전에 출시한 EFS 서비스에 대한 자세한 소개와 함께 다양한 활용 팁을 데모와 함께 소개해 드립니다.
An Intro to NoSQL Databases -- NoSQL databases will not become the new dominators. Relational will still be popular, and used in the majority of situations. They, however, will no longer be the automatic choice. (source : http://martinfowler.com/)
Actionable Insights with AI - Snowflake for Data ScienceHarald Erb
Talk @ ScaleUp 360° AI Infrastructures DACH, 2021: Data scientists spend 80% and more of their time searching for and preparing data. This talk explains Snowflake’s Platform capabilities like near-unlimited data storage and instant and near-infinite compute resources and how the platform can be used to seamlessly integrate and support the machine learning libraries and tools data scientists rely on.
Introduction to Snowflake Datawarehouse and Architecture for Big data company. Centralized data management. Snowpipe and Copy into a command for data loading. Stream loading and Batch Processing.
Today’s organisations require a data storage and analytics solution that offers more agility and flexibility than traditional data management systems. Data Lake is a new and increasingly popular way to store all of your data, structured and unstructured, in one, centralised repository. Since data can be stored as-is, there is no need to convert it to a predefined schema and you no longer need to know what questions you want to ask of your data beforehand.
In this webinar, you will discover how AWS gives you fast access to flexible and low-cost IT resources, so you can rapidly scale and build your data lake that can power any kind of analytics such as data warehousing, clickstream analytics, fraud detection, recommendation engines, event-driven ETL, serverless computing, and internet-of-things processing regardless of volume, velocity and variety of data.
Learning Objectives:
• Discover how you can rapidly scale and build your data lake with AWS.
• Explore the key pillars behind a successful data lake implementation.
• Learn how to use the Amazon Simple Storage Service (S3) as the basis for your data lake.
• Learn about the new AWS services recently launched, Amazon Athena and Amazon Redshift Spectrum, that help customers directly query that data lake.
Big data architectures and the data lakeJames Serra
With so many new technologies it can get confusing on the best approach to building a big data architecture. The data lake is a great new concept, usually built in Hadoop, but what exactly is it and how does it fit in? In this presentation I'll discuss the four most common patterns in big data production implementations, the top-down vs bottoms-up approach to analytics, and how you can use a data lake and a RDBMS data warehouse together. We will go into detail on the characteristics of a data lake and its benefits, and how you still need to perform the same data governance tasks in a data lake as you do in a data warehouse. Come to this presentation to make sure your data lake does not turn into a data swamp!
AWS Glue는 고객이 분석을 위해 손쉽게 데이터를 준비하고 로드할 수 있게 지원하는 완전관리형 ETL(추출, 변환 및 로드) 서비스입니다. AWS 관리 콘솔에서 클릭 몇 번으로 ETL 작업을 생성하고 실행할 수 있습니다. 빅데이터 분석 시 다양한 데이터 소스에 대한 전처리 작업을 할 때, 별도의 데이터 처리용 서버나 인프라를 관리할 필요가 없습니다. 본 세션에서는 지난 5월 서울 리전에 출시한 Glue 서비스에 대한 자세한 소개와 함께 다양한 활용 팁을 데모와 함께 소개해 드립니다.
Standing on the Shoulders of Open-Source Giants: The Serverless Realtime Lake...HostedbyConfluent
"Unlike just a few years ago, today the lakehouse architecture is an established data platform embraced by all major cloud data companies such as AWS, Azure, Google, Oracle, Microsoft, Snowflake and Databricks.
This session kicks off with a technical, no-nonsense introduction to the lakehouse concept, dives deep into the lakehouse architecture and recaps how a data lakehouse is built from the ground up with streaming as a first-class citizen.
Then we focus on serverless for streaming use cases. Serverless concepts are well-known from developers triggering hundreds of thousands of AWS Lambda functions at a negligible cost. However, the same concept becomes more interesting when looking at data platforms.
We have all heard about the principle ""It runs best on Powerpoint"", so I decided to skip slides here and bring a serverless demo instead:
A hands-on, fun, and interactive serverless streaming use case example where we ingest live events from hundreds of mobile devices (don't miss out - bring your phone and be part of it!!). Based on this use case I will critically explore how much of a modern lakehouse is serverless and how we implemented that at Databricks (spoiler alert: serverless is everywhere from data pipelines, workflows, optimized Spark APIs, to ML).
TL;DR benefits for the Data Practitioners:
-Recap the OSS foundation of the Lakehouse architecture and understand its appeal
- Understand the benefits of leveraging a lakehouse for streaming and what's there beyond Spark Structured Streaming.
- Meat of the talk: The Serverless Lakehouse. I give you the tech bits beyond the hype. How does a serverless lakehouse differ from other serverless offers?
- Live, hands-on, interactive demo to explore serverless data engineering data end-to-end. For each step we have a critical look and I explain what it means, e.g for you saving costs and removing operational overhead."
Netflix Data Engineering @ Uber Engineering MeetupBlake Irvine
People, Platform, Projects: these slides overview how Netflix works with Big Data. I share how our teams are organized, the roles we typically have on the teams, an overview of our Big Data Platform, and two example projects.
IT organizations today need to support a modern, flexible, global workforce and ensure their users can be productive from anywhere. Moving desktops and applications to AWS offers improved security, scale, and performance with cloud economics. In this session, we provide an overview of Amazon WorkSpaces and discuss the use cases for it. Then, we dive deep into best practices for implementing Amazon WorkSpaces, including integrating with your existing identity, security, networking, and storage solutions.
Diego Magalhaes, Senior Solutions Architect, Amazon Web Services
Lambda Architecture in the Cloud with Azure Databricks with Andrei VaranovichDatabricks
The term “Lambda Architecture” stands for a generic, scalable and fault-tolerant data processing architecture. As the hyper-scale now offers a various PaaS services for data ingestion, storage and processing, the need for a revised, cloud-native implementation of the lambda architecture is arising.
In this talk we demonstrate the blueprint for such an implementation in Microsoft Azure, with Azure Databricks — a PaaS Spark offering – as a key component. We go back to some core principles of functional programming and link them to the capabilities of Apache Spark for various end-to-end big data analytics scenarios.
We also illustrate the “Lambda architecture in use” and the associated tread-offs using the real customer scenario – Rijksmuseum in Amsterdam – a terabyte-scale Azure-based data platform handles data from 2.500.000 visitors per year.
A simplified version of my presentation:
- PowerBI solution architecture
- Key steps to visualize data in PowerBI
- PowerBI Demo
- R in PowerBI
- Custom Visuals
- PowerBI Report Server
- Azure services and Power BI
Amazon Elastic File System(EFS)은 클라우드 기반 네트워크 파일 시스템으로서 손쉽게 확장 가능한 탄력적인 파일 스토리지를 제공합니다. 다수의 Amazon EC2에 탑재하거나 Direct Connect를 통해 온프레미스와 연동 가능하며, 웹 콘텐츠, 엔터프라이즈 애플리케이션, 미디어 및 DB 백업 등 다양한 워크로드에서 활용 가능합니다. 본 세션에서는 지난 5월 서울 리전에 출시한 EFS 서비스에 대한 자세한 소개와 함께 다양한 활용 팁을 데모와 함께 소개해 드립니다.
SAS Visual Analytics is a high-performance, in-memory solution for
exploring massive amounts of data very quickly. It enables you to spot
patterns, identify opportunities for further analysis and convey visual
results via Web reports or a mobile platform such as iPad® or Androidbased
tablets.This presentation is a very brief overview of the many features and
capabilities of SAS Visual Analytics. It is meant to get you started
quickly, with a relatively modest data set example (only 1.4 million
rows).Insight Toy Company is an organization that produces and sells toys to
resellers (“vendors”). The data is made up of 34 years of Sales information,
covering 128 cities across the world.
For each row of data (transaction) we have:
Information on the items sold (product brand, line, make, style, SKU)
The sale value (“order total”) and various related costs (distribution, marketing, product)
Information on the sales representative (rating, sales target, actual to date, etc.)
Geographic information (on the vendors as well as the selling facility)
Information on the vendors (rating, satisfaction, distance to nearest facility)
Text Notes taken at the moment of the order taking, based on conversation with the vendor.
FirstPartner's 2016 Blockchain Ecosystem Market Map gives a clear visual overview of the emerging blockchain landscape, highlighting key companies, projects, technologies and trends.
The Map summarises three main areas of focus emerging around the core blockchain or distributed ledger protocols:
Bitcoin and Cryptocurrencies: Providing an alternative to centrally managed "fiat" currencies, this sector includes Bitcoin exchanges, Bitcoin wallets, miners and cryptocurrency payment processors. The map illustrates how these companies interact and features some leading players including Coinbase, Circle, Kraken and 21 Inc.
The Financial Services Blockchain: This has been the main area of focus over the last 12 months as attention shifts from Bitcoin to Financial Services applications. An increasing number of players are focussing on commercialising blockchain technologies for banks, securities, derivatives and asset markets and institutional investors - and are attracting VC funding to do so. Ripple and Ethereum are leading candidate protocols for payment processing and smart contracts and players including Ripple, Chain and Digital Asset Holdings are gaining traction with Financial Institutions. The Map highlights leading technology companies and some of the banks, card schemes and processors who are investing in or evaluating distributed ledger technologies.
Other Use Cases: The distributed ledger concept and its ability to support transparent and tamperproof asset registration, proof of ownership and asset transfer transactions makes it potentially applicable to multiple non-financial use cases. The Map highlights a number of candidate use cases including publishing, legal, distributed data storage, document management and IoT. Some of the pioneering initiatives and companies exploring these applications are included.
Crucially the Map also provides a clear pictorial explanation and summary of the leading protocols at the heart of the ecosystem and concepts including coloured coins and smart contracts that supplement them to make a number of the proposed services possible.
You can download a copy of the map at www.firstpartner.net.
More info: https://blockchainhub.net/
Ethereum for Beginners: History of the Blockchain & Ethereum, Components, Outlook, Web 3.0, Serverless, Decetralized Universal World Computer
An introduction to Ethereum, the peer to peer computing framework based on the blockchain design. It describes how Ethereum relates to earlier blockchain technologies and how it represents an evolution of these technologies
UNIT - 1 Part 2: Data Warehousing and Data MiningNandakumar P
DBMS Schemas for Decision Support , Star Schema, Snowflake Schema, Fact Constellation Schema, Schema Definition, Data extraction, clean up and transformation tools.
Power bi slide share pdf it is a very importantSatyabratarath5
It is first pdf I am Satyabrata rath my 1st pdf in power bi it is most wonderful pdf .A basic knowledge in power bi
Power bi most wonderful pdf.power bi is business purposes tool
What is OLAP -Data Warehouse Concepts - IT Online Training @ NewyorksysNEWYORKSYS-IT SOLUTIONS
NEWYORKSYSTRAINING are destined to offer quality IT online training and comprehensive IT consulting services with complete business service delivery orientation.
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
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.
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
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.
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.
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
A Strategic Approach: GenAI in EducationPeter 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.
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
1. OUTSOURCING RESOURCES & TECHNICAL SUPPORT
CORPORATE & INDIVIDUAL TRAINING BIDWHM@GMAIL.COM
INSTALLATION & ADMIN SUPPORT
INSTALLATION
ADMINISTRATION
ONLINE /CLASS ROOM /CORPORATE TRAINING
TECHNICAL SUPPORT
2. DF Content
DataFlux Data Management Platform
Overview of DataFlux Data Management Studio
DataFlux Methodology: Plan, Act, and Monitor
Managing Repositories
Different types of Data Connections
Creating and Managing Data Collections
Creating , Setting , Working with Data Explorations
Introduction ,Creating Business Rules and Custom Metrics
Overview, Creating , Preparing of Data Profiles
4. Score Board ,Model validation ,Logistic, Linear
,Cluster ,GML ,Comparing Models ,Predictive
Modelling, BASEL II Model Validation & technical
support ,Segmentation analysis
5. DataFlux Expression Engine Language
Array Functions
Blue Fusion Functions
Boolean Functions
Database Functions
Data Input Functions
Date and Time Functions
Event Functions
Execution Functions
External File Functions
Information/Conversion Functions
Logging Functions
Testing and Evaluating
Selecting Output Fields
Sub-Setting
Initializing and Declaring Variables
Saving Expressions
Counting Records
Debugging and Printing Error Messages
Creating Groups
Retrieving and Converting Binary Data
6. Defining business rules
Data profiling with business rules and alerts
Data jobs with business rules
Data jobs with monitoring tasks
Working Through the MONITOR Phase of the DataFlux
Methodology(Business Rule creation)
14. DataFlux Data Management
The DataFlux Data Management
Platform enables you to discover,
design, deploy and maintain data
across your enterprise in a centralized
way.
The following diagram illustrates the
components of the platform
15. Overview of Data Management Studio
DataFlux Data Management Studio is a
data management suite that combines
Data quality,
Data integration,
Master data management.
It provides a process and technology
framework to deliver a single, accurate
and consistent view of your enterprise
data.
Data Management Studio gives you the
ability to:
Merge customer, product, or other
enterprise data
Unify disparate data through a variety of
data integration methods (batch, real
time, virtual)
Verify and complete address information
Integrate disparate data sets and ensure
data quality
Transform and standardize product codes
Monitor data for compliance in batch or
real time
Manage metadata hierarchy and visibility
16. DataFlux Methodology: Plan, Act, and Monitor
The main activities in the DataFlux methodology are as follows:
I. Plan - Identify patterns and problems in your data.
II. Act - Create processes to improve data quality and data integration.
III. Monitor - Monitor your processes for data quality and data integration
17. Overview
1Main Menu — Enables you to select features that are active in
the current context. For more information, see Main Menu.
2Navigation Pane —Enables you to navigate riser bars, trees, and
folders.
3Navigation Riser Bar — Enables you to select riser bars that
display a set of related features. For more information, see
Information Riser Bar, Data Riser Bar, Folders Riser Bar, Business
Data Riser Bar, Data Management Servers Riser Bar, or
Administration Riser Bar.
4Status Bar — Displays status messages, current server logins,
and similar information.
5Information Pane — Contains one or more portlets that display
information, such as a list of the files that were last accessed, or
details about a selected item.
6Title Bar — Displays the product name.
7Portlets — Components that display information, such as a list
of the files that were last accessed, or details about a selected
item.
8Toolbar — A set of icons that enable you to access context-
sensitive features with one click.
18. Overview of the Job Dialog
1Home Tab — Click this tab to return to the main window, so that
you can select or open another item from the Riser Bars.
2Resource Pane — Contains components such as the Nodes tree,
the Folders tree, and the
3Help Area. Help Area — When a node is selected in the Nodes
tree, a brief description of the node is displayed in the Help Area,
along with a link to the help topic for that node.
4Details Pane — Displays tabs for the selected node, a log for the
current job, and other information about the selected item.
5Work Area — The area where you build flows for data jobs and
process jobs.
6Secondary Toolbar — A set of icons that enable you to access
context-sensitive features that are appropriate for the work area.
7Secondary Tabs — A set of tabs for the current job or a node
that is selected within the job.
8Detach Tab — Click this tab to detach the job dialog from the
main Data Integration Studio window.
9Primary Tabs — Each open job has a primary tab.
19. Total slides are 210
OUTSOURCING RESOURCES & TECHNICAL SUPPORT
CORPORATE & INDIVIDUAL TRAINING BIDWHM@GMAIL.COM
INSTALLATION & ADMIN SUPPORT