Intellipaat Online Courses on top trending IT technologies : https://intellipaat.com/course-cat/big-data-analytics-courses/
Expert written Tutorials : https://intellipaat.com/blog/tutorials/
Latest Blogs : https://intellipaat.com/blog/blog-category/
How to Become Business Intelligence Analyst?Intellipaat
The document provides an overview of business intelligence (BI), including its core concepts, why it's important, career paths and salaries. It discusses how BI converts raw data into meaningful insights, has a direct impact on strategic decisions and covers processes of collecting, storing and analyzing business data. The document outlines the step-by-step learning path for becoming a BI analyst, from exploring BI tools to understanding technologies like ETL, SQL and data visualization, and solving real-world business problems using tools. Finally, it provides average salaries for BI analysts globally and contact information for further learning.
IoT at the Edge - Greengrass and More - AWS PS Summit Canberra 2017Amazon Web Services
This session focuses on the business and strategic implications of leveraging IoT, and provides starting points in the AWS Cloud to accelerate your time to value. Learn how to build IoT solutions with AWS Greengrass to connect different types of cloud devices and reap the benefits of communicating your data across platforms to better respond to events, ensure secure communication, and reduce the cost of running IoT applications.
Speaker: Craig Lawton, Solutions Architect, Amazon Web Services
Level: 200
Hadoop Essentials -- The What, Why and How to Meet Agency ObjectivesCloudera, Inc.
This session will provide an executive overview of the Apache Hadoop ecosystem, its basic concepts, and its real-world applications. Attendees will learn how organizations worldwide are using the latest tools and strategies to harness their enterprise information to solve business problems and the types of data analysis commonly powered by Hadoop. Learn how various projects make up the Apache Hadoop ecosystem and the role each plays to improve data storage, management, interaction, and analysis. This is a valuable opportunity to gain insights into Hadoop functionality and how it can be applied to address compelling business challenges in your agency.
Skymind is a company that provides deep learning tools and services to help enterprises extract value from their data. Their flagship product is Deeplearning4j, an open-source deep learning library for Java and Scala that can be used on distributed systems. Skymind also offers consulting services and training to help companies develop and deploy deep learning models for tasks like computer vision, natural language processing, and fraud detection. Their goal is to make advanced deep learning techniques accessible and useful for businesses.
Inteligencia artificial - Quebrando el paradigma de la amnesia empresarialMarcos Quezada
Deep Learning (DL) es la sub-categoría dentro del Machine Learning que más crece y evoluciona en el campo de la Inteligencia Artificial (AI).
Deep Learning usa redes neuronales basadas en software para desarrollar patrones de análisis que permiten contar con una capacidad predictiva: En pocas palabras, Deep Learning es una plataforma que le permite aprender a aprender. Es la manera de sacar el mayor provecho de sus datos. Hoy las empresas deben aprovechar sus datos a la misma velocidad con la que los producen. Usando Deep Learning podemos desarrollar nuevas capacidades analíticas incluyendo:
- Visión artificial
- Detección de objetos
- Procesamiento de lenguaje natural
- Detección de anomalías y fraude
En el corazón de estos casos de uso hay capacidades sofisticadas de reconocimiento de patrones y clasificación, que dan lugar a aplicaciones revolucionarias y abren una ventana hacia el futuro.
En esta presentación les cuento cómo estamos llevando Deep Learning más allá de sus raíces basadas en frameworks open source y como la plataforma PowerAI le puede ayudar a su empresa a poner producción estas herramientas poderosas en ahora mismo.
How to Become Business Intelligence Analyst?Intellipaat
The document provides an overview of business intelligence (BI), including its core concepts, why it's important, career paths and salaries. It discusses how BI converts raw data into meaningful insights, has a direct impact on strategic decisions and covers processes of collecting, storing and analyzing business data. The document outlines the step-by-step learning path for becoming a BI analyst, from exploring BI tools to understanding technologies like ETL, SQL and data visualization, and solving real-world business problems using tools. Finally, it provides average salaries for BI analysts globally and contact information for further learning.
IoT at the Edge - Greengrass and More - AWS PS Summit Canberra 2017Amazon Web Services
This session focuses on the business and strategic implications of leveraging IoT, and provides starting points in the AWS Cloud to accelerate your time to value. Learn how to build IoT solutions with AWS Greengrass to connect different types of cloud devices and reap the benefits of communicating your data across platforms to better respond to events, ensure secure communication, and reduce the cost of running IoT applications.
Speaker: Craig Lawton, Solutions Architect, Amazon Web Services
Level: 200
Hadoop Essentials -- The What, Why and How to Meet Agency ObjectivesCloudera, Inc.
This session will provide an executive overview of the Apache Hadoop ecosystem, its basic concepts, and its real-world applications. Attendees will learn how organizations worldwide are using the latest tools and strategies to harness their enterprise information to solve business problems and the types of data analysis commonly powered by Hadoop. Learn how various projects make up the Apache Hadoop ecosystem and the role each plays to improve data storage, management, interaction, and analysis. This is a valuable opportunity to gain insights into Hadoop functionality and how it can be applied to address compelling business challenges in your agency.
Skymind is a company that provides deep learning tools and services to help enterprises extract value from their data. Their flagship product is Deeplearning4j, an open-source deep learning library for Java and Scala that can be used on distributed systems. Skymind also offers consulting services and training to help companies develop and deploy deep learning models for tasks like computer vision, natural language processing, and fraud detection. Their goal is to make advanced deep learning techniques accessible and useful for businesses.
Inteligencia artificial - Quebrando el paradigma de la amnesia empresarialMarcos Quezada
Deep Learning (DL) es la sub-categoría dentro del Machine Learning que más crece y evoluciona en el campo de la Inteligencia Artificial (AI).
Deep Learning usa redes neuronales basadas en software para desarrollar patrones de análisis que permiten contar con una capacidad predictiva: En pocas palabras, Deep Learning es una plataforma que le permite aprender a aprender. Es la manera de sacar el mayor provecho de sus datos. Hoy las empresas deben aprovechar sus datos a la misma velocidad con la que los producen. Usando Deep Learning podemos desarrollar nuevas capacidades analíticas incluyendo:
- Visión artificial
- Detección de objetos
- Procesamiento de lenguaje natural
- Detección de anomalías y fraude
En el corazón de estos casos de uso hay capacidades sofisticadas de reconocimiento de patrones y clasificación, que dan lugar a aplicaciones revolucionarias y abren una ventana hacia el futuro.
En esta presentación les cuento cómo estamos llevando Deep Learning más allá de sus raíces basadas en frameworks open source y como la plataforma PowerAI le puede ayudar a su empresa a poner producción estas herramientas poderosas en ahora mismo.
For the full video of this presentation, please visit:
https://www.edge-ai-vision.com/2021/07/the-data-driven-engineering-revolution-a-presentation-from-edge-impulse/
Zach Shelby, Co-founder and CEO of Edge Impulse, presents the “Data-Driven Engineering Revolution” tutorial at the May 2021 Embedded Vision Summit.
In this talk, IoT industry pioneer and Edge Impulse co-founder Zach Shelby shares insights about how machine learning is revolutionizing embedded engineering. Advances in silicon and deep learning are enabling embedded machine learning (TinyML) to be deployed where data is born, from industrial sensor data to audio and video.
Shelby explains the new paradigm of data-driven engineering with ML, showing how developers are using data instead of code to drive algorithm innovation. To support widespread deployment, ML workloads need to run on embedded computing targets from MCUs to GPUs, with MLOps processes to support efficient development and deployment. Industrial, logistics and health markets are particularly ripe to deploy this data-driven approach, and Shelby highlights several exciting case studies.
What is the role of data engineer in any organization and how can one become a data engineer can be understood by this article.
https://www.janbasktraining.com/hadoop-big-data-analytics
The document is a presentation about big data by Sanjay Sharma from Impetus Technologies. It discusses big data concepts and technologies like Hadoop, NoSQL, and MPP databases. It also covers big data tools and ecosystems, use cases, careers, and the impact of big data on IT and businesses. The presentation contains 41 slides covering these topics in detail with examples and diagrams.
Device to Intelligence, IOT and Big Data in OracleJunSeok Seo
The document discusses Internet of Things (IoT) and big data in the context of Oracle technologies. It provides examples of how Oracle solutions have helped companies in various industries like transportation, healthcare, manufacturing, and telecommunications manage IoT and big data. Specifically, it highlights how Oracle technologies allow for efficient processing, analysis and management of large volumes of data from IoT devices and sensor networks in real-time.
This document discusses the transition from small data to big data. In the small data era, companies relied on relational databases, OLAP, ETL and reporting. The big data era introduced new technologies like Cassandra, Solr, Hadoop, and Storm to enable deeper analytics, streaming analytics and real-time analytics. Impetus Technologies is an expert consulting firm with over 1400 employees that helps companies implement big data technologies and solutions.
Powering the Internet of Things with Apache HadoopCloudera, Inc.
Without the right data management strategy, investments in Internet of Things (IoT) can yield limited results. Apache Hadoop has emerged as a key architectural component that can help make sense of IoT data, enabling never before seen data products and solutions.
1) The document discusses digital transformation and AI at the edge for industrial applications. It describes challenges like processing vast amounts of distributed data in real-time while ensuring security and reliability with limited resources.
2) Edge computing is important for industrial IoT as it allows data processing and AI inferencing close to where data is generated, improving latency, security, and scalability. The document outlines several open source edge computing projects and technologies being developed.
3) Achieving digital transformation requires bridging gaps between IT and operational systems through approaches like collecting telemetry data, predictive maintenance, and building a data-informed culture across the organization. Standards like OPC UA are also important for interoperability.
Cloud expo june 2013: Building a Real Time Analytics Platform on Big Data in ...Sanjay Sharma
This document discusses building a real-time analytics platform on big data in the cloud. It outlines the need to move from batch processing to real-time analytics using approaches like streaming analytics and in-memory analytics. It provides examples of software that can be used for these approaches. The document also presents a reference architecture for a real-time analytics strategy, including components for data ingestion, processing, storage and business rules management. Impetus is introduced as a company that provides consulting and services for big data analytics.
Here we discuss about how Edge AI and On-Prem AI varies from each other based on their functionalities and the domains where both Edge AI and On-Prem AI are utilized.
To read more about the post check out our website (https://visailabs.com/) now!
Democratization - New Wave of Data Science (홍운표 상무, DataRobot) :: AWS Techfor...Amazon Web Services Korea
This document discusses the democratization of data science and machine learning using automated machine learning tools. It provides examples of how DataRobot has helped customers in various industries build predictive models faster and with less coding than traditional approaches. Specifically, it summarizes how DataRobot has helped customers in banking, insurance, retail, and other industries with use cases like predictive maintenance, sales forecasting, fraud detection, customer churn prediction, and insurance underwriting.
Extreme Sports & Beyond: Exploring a new frontier in data with GoProCloudera, Inc.
GoPro is a powerful global brand, thanks in large part to its innovative cameras and accessories that capture moments other cameras just miss: surfing in Maui, skiing in Tahoe, recording your child’s first steps. And today, the company is nearly as well known for its user-generated social and content networks.
Join us for this special webinar hosted by Tableau, Trifacta, and Cloudera—featuring GoPro. We’ll dive into GoPro’s data strategy and architecture, from ingest and processing to data prep and reporting, all on AWS.
This document discusses big data and the Internet of Things (IoT). It states that while IoT data can be big data, big data strategies and technologies apply regardless of data source or industry. It defines big data as occurring when the size of data becomes problematic to store, move, extract, analyze, etc. using traditional methods. It recommends distributing and parallelizing data using approaches like Hadoop and discusses how technologies like SQL on Hadoop, Pig, Spark, HBase, queues, stream processing, and complex architectures can be used to handle big IoT and other big data.
Cloud-Native Machine Learning: Emerging Trends and the Road AheadDataWorks Summit
Big data platforms are being asked to support an ever increasing range of workloads and compute environments, including large-scale machine learning and public and private clouds. In this talk, we will discuss some emerging capabilities around cloud-native machine learning and data engineering, including running machine learning and Spark workloads directly on Kubernetes, and share our vision of the road ahead for ML and AI in the cloud.
ICIC 2013 Conference Proceedings Tony Trippe PatinformaticsDr. Haxel Consult
The document discusses how big data and data science techniques are bringing a "sea change" to patent analytics. These new techniques, like Hadoop and R, allow for analyzing the huge amounts of patent data in ways that were not possible before. Examples discussed include next-generation citation analysis using network diagrams, word trees to analyze patent claims, and tag clouds for discovering hedge words and synonyms. The advent of these big data analytics tools is creating new opportunities for growth in the patent analysis field.
Momentum provides easy to use platform for processing large volume of data streams in realtime. This is an ideal solution for IoT and click stream analytics
This document discusses climbing the AI ladder and preparing data for artificial intelligence. It notes that 81% of organizations do not yet understand the data required for AI. The first step in the ladder is to ensure data is properly structured and accessible. Future steps include applying machine learning everywhere, scaling insights on demand, and building a trusted analytics foundation. The document promotes IBM tools for data science, machine learning, and building a Hadoop data platform to analyze large volumes and varieties of data. It presents a vision of enabling SQL queries of all data, provisioning data as a utility, and using DevOps practices for data science.
How to Build Continuous Ingestion for the Internet of ThingsCloudera, Inc.
The Internet of Things is moving into the mainstream and this new world of data-driven products is transforming a vast number of industry sectors and technologies.
However, IoT creates a new challenge: how to build and operationalize continual data ingestion from such a wide and ever-changing array of endpoints so that the data arrives consumption-ready and can drive analysis and action within the business.
In this webinar, Sean Anderson from Cloudera and Kirit Busu, Director of Product Management at StreamSets, will discuss Hadoop's ecosystem and IoT capabilities and provide advice about common patterns and best practices. Using specific examples, they will demonstrate how to build and run end-to-end IOT data flows using StreamSets and Cloudera infrastructure.
Gianluigi Viganò - How to use HP HEAVEN-on-demand functions for Big Data appsCodemotion
HP Haven, the industry’s first comprehensive, scalable, open, and secure platform for Big Data analytics enables you to deliver actionable insight where and when it is needed to drive superior business outcomes and gain competitive advantage. It includes HP Haven OnHadoop the most comprehensive array of SQL functions with any Hadoop distribution.
In this presentation I do a review of the architecture of an AI application for IoT environments.
Since specific modeling and training aspects also have an impact on the final implementation of an enterprise ready solution, such solutions become very complex pretty soon.
The complexity of AI system for IoT is a big challenge – thus, I want to break this complexity down into particular views, which emphasize the individual but still interconnected aspects more clearly.
Arocom is a consulting and solution engineering company with expertise in providing engineering services for AI & Machine Learning, Data Operations & Analytics, MLOps and Cloud Computing.
Our clients include companies within biotech, drug discovery, therapeutics, manufacturing, retail and startups. Our consultants are best in their skills and offer hands-on talent to our clients in achieving their goals.
For the full video of this presentation, please visit:
https://www.edge-ai-vision.com/2021/07/the-data-driven-engineering-revolution-a-presentation-from-edge-impulse/
Zach Shelby, Co-founder and CEO of Edge Impulse, presents the “Data-Driven Engineering Revolution” tutorial at the May 2021 Embedded Vision Summit.
In this talk, IoT industry pioneer and Edge Impulse co-founder Zach Shelby shares insights about how machine learning is revolutionizing embedded engineering. Advances in silicon and deep learning are enabling embedded machine learning (TinyML) to be deployed where data is born, from industrial sensor data to audio and video.
Shelby explains the new paradigm of data-driven engineering with ML, showing how developers are using data instead of code to drive algorithm innovation. To support widespread deployment, ML workloads need to run on embedded computing targets from MCUs to GPUs, with MLOps processes to support efficient development and deployment. Industrial, logistics and health markets are particularly ripe to deploy this data-driven approach, and Shelby highlights several exciting case studies.
What is the role of data engineer in any organization and how can one become a data engineer can be understood by this article.
https://www.janbasktraining.com/hadoop-big-data-analytics
The document is a presentation about big data by Sanjay Sharma from Impetus Technologies. It discusses big data concepts and technologies like Hadoop, NoSQL, and MPP databases. It also covers big data tools and ecosystems, use cases, careers, and the impact of big data on IT and businesses. The presentation contains 41 slides covering these topics in detail with examples and diagrams.
Device to Intelligence, IOT and Big Data in OracleJunSeok Seo
The document discusses Internet of Things (IoT) and big data in the context of Oracle technologies. It provides examples of how Oracle solutions have helped companies in various industries like transportation, healthcare, manufacturing, and telecommunications manage IoT and big data. Specifically, it highlights how Oracle technologies allow for efficient processing, analysis and management of large volumes of data from IoT devices and sensor networks in real-time.
This document discusses the transition from small data to big data. In the small data era, companies relied on relational databases, OLAP, ETL and reporting. The big data era introduced new technologies like Cassandra, Solr, Hadoop, and Storm to enable deeper analytics, streaming analytics and real-time analytics. Impetus Technologies is an expert consulting firm with over 1400 employees that helps companies implement big data technologies and solutions.
Powering the Internet of Things with Apache HadoopCloudera, Inc.
Without the right data management strategy, investments in Internet of Things (IoT) can yield limited results. Apache Hadoop has emerged as a key architectural component that can help make sense of IoT data, enabling never before seen data products and solutions.
1) The document discusses digital transformation and AI at the edge for industrial applications. It describes challenges like processing vast amounts of distributed data in real-time while ensuring security and reliability with limited resources.
2) Edge computing is important for industrial IoT as it allows data processing and AI inferencing close to where data is generated, improving latency, security, and scalability. The document outlines several open source edge computing projects and technologies being developed.
3) Achieving digital transformation requires bridging gaps between IT and operational systems through approaches like collecting telemetry data, predictive maintenance, and building a data-informed culture across the organization. Standards like OPC UA are also important for interoperability.
Cloud expo june 2013: Building a Real Time Analytics Platform on Big Data in ...Sanjay Sharma
This document discusses building a real-time analytics platform on big data in the cloud. It outlines the need to move from batch processing to real-time analytics using approaches like streaming analytics and in-memory analytics. It provides examples of software that can be used for these approaches. The document also presents a reference architecture for a real-time analytics strategy, including components for data ingestion, processing, storage and business rules management. Impetus is introduced as a company that provides consulting and services for big data analytics.
Here we discuss about how Edge AI and On-Prem AI varies from each other based on their functionalities and the domains where both Edge AI and On-Prem AI are utilized.
To read more about the post check out our website (https://visailabs.com/) now!
Democratization - New Wave of Data Science (홍운표 상무, DataRobot) :: AWS Techfor...Amazon Web Services Korea
This document discusses the democratization of data science and machine learning using automated machine learning tools. It provides examples of how DataRobot has helped customers in various industries build predictive models faster and with less coding than traditional approaches. Specifically, it summarizes how DataRobot has helped customers in banking, insurance, retail, and other industries with use cases like predictive maintenance, sales forecasting, fraud detection, customer churn prediction, and insurance underwriting.
Extreme Sports & Beyond: Exploring a new frontier in data with GoProCloudera, Inc.
GoPro is a powerful global brand, thanks in large part to its innovative cameras and accessories that capture moments other cameras just miss: surfing in Maui, skiing in Tahoe, recording your child’s first steps. And today, the company is nearly as well known for its user-generated social and content networks.
Join us for this special webinar hosted by Tableau, Trifacta, and Cloudera—featuring GoPro. We’ll dive into GoPro’s data strategy and architecture, from ingest and processing to data prep and reporting, all on AWS.
This document discusses big data and the Internet of Things (IoT). It states that while IoT data can be big data, big data strategies and technologies apply regardless of data source or industry. It defines big data as occurring when the size of data becomes problematic to store, move, extract, analyze, etc. using traditional methods. It recommends distributing and parallelizing data using approaches like Hadoop and discusses how technologies like SQL on Hadoop, Pig, Spark, HBase, queues, stream processing, and complex architectures can be used to handle big IoT and other big data.
Cloud-Native Machine Learning: Emerging Trends and the Road AheadDataWorks Summit
Big data platforms are being asked to support an ever increasing range of workloads and compute environments, including large-scale machine learning and public and private clouds. In this talk, we will discuss some emerging capabilities around cloud-native machine learning and data engineering, including running machine learning and Spark workloads directly on Kubernetes, and share our vision of the road ahead for ML and AI in the cloud.
ICIC 2013 Conference Proceedings Tony Trippe PatinformaticsDr. Haxel Consult
The document discusses how big data and data science techniques are bringing a "sea change" to patent analytics. These new techniques, like Hadoop and R, allow for analyzing the huge amounts of patent data in ways that were not possible before. Examples discussed include next-generation citation analysis using network diagrams, word trees to analyze patent claims, and tag clouds for discovering hedge words and synonyms. The advent of these big data analytics tools is creating new opportunities for growth in the patent analysis field.
Momentum provides easy to use platform for processing large volume of data streams in realtime. This is an ideal solution for IoT and click stream analytics
This document discusses climbing the AI ladder and preparing data for artificial intelligence. It notes that 81% of organizations do not yet understand the data required for AI. The first step in the ladder is to ensure data is properly structured and accessible. Future steps include applying machine learning everywhere, scaling insights on demand, and building a trusted analytics foundation. The document promotes IBM tools for data science, machine learning, and building a Hadoop data platform to analyze large volumes and varieties of data. It presents a vision of enabling SQL queries of all data, provisioning data as a utility, and using DevOps practices for data science.
How to Build Continuous Ingestion for the Internet of ThingsCloudera, Inc.
The Internet of Things is moving into the mainstream and this new world of data-driven products is transforming a vast number of industry sectors and technologies.
However, IoT creates a new challenge: how to build and operationalize continual data ingestion from such a wide and ever-changing array of endpoints so that the data arrives consumption-ready and can drive analysis and action within the business.
In this webinar, Sean Anderson from Cloudera and Kirit Busu, Director of Product Management at StreamSets, will discuss Hadoop's ecosystem and IoT capabilities and provide advice about common patterns and best practices. Using specific examples, they will demonstrate how to build and run end-to-end IOT data flows using StreamSets and Cloudera infrastructure.
Gianluigi Viganò - How to use HP HEAVEN-on-demand functions for Big Data appsCodemotion
HP Haven, the industry’s first comprehensive, scalable, open, and secure platform for Big Data analytics enables you to deliver actionable insight where and when it is needed to drive superior business outcomes and gain competitive advantage. It includes HP Haven OnHadoop the most comprehensive array of SQL functions with any Hadoop distribution.
In this presentation I do a review of the architecture of an AI application for IoT environments.
Since specific modeling and training aspects also have an impact on the final implementation of an enterprise ready solution, such solutions become very complex pretty soon.
The complexity of AI system for IoT is a big challenge – thus, I want to break this complexity down into particular views, which emphasize the individual but still interconnected aspects more clearly.
Arocom is a consulting and solution engineering company with expertise in providing engineering services for AI & Machine Learning, Data Operations & Analytics, MLOps and Cloud Computing.
Our clients include companies within biotech, drug discovery, therapeutics, manufacturing, retail and startups. Our consultants are best in their skills and offer hands-on talent to our clients in achieving their goals.
How to reinvent your organization in an iterative and pragmatic way? This is the result of using our digital toolbox. It allows you to transform your business model, expand your ecosystem by setting up your digital platform. This reinvention is also supported by the adaptation of your governance allowing you to innovate while guaranteeing the performance of your organization. For any information / suggestion / collaboration - william.poos@nrb.be
Comment réinventer votre organisation de manière itérative et pragmatique ? C'est le résultat de l'utilisation de notre boîte à outils digitale. Elle vous permet de transformer votre modèle métier, d'étendre votre écosystème en mettant en place votre plateforme digitale. Cette réinvention est également supportée par l'adaptation de votre gouvernance vous permettant d'innover tout en garantissant la performance de votre organisation. Pour toute information / suggestion / collaboration - william.poos@nrb.be
Artificial Intelligence and Machine Learning with the Oracle Data Science CloudJuarez Junior
The document discusses Oracle's Data Science Cloud, which is described as an end-to-end platform that enables data science teams to organize their work, easily access data and computing resources, and build, train, deploy, and manage models on Oracle Cloud. It provides key capabilities like data exploration, model building and training using open source tools, model deployment, and model management. The platform aims to make data science teams more productive by simplifying workflows, expediting access to resources, and facilitating collaboration.
Innovating to Create a Brighter Future for AI, HPC, and Big Datainside-BigData.com
In this deck from the DDN User Group at ISC 2019, Alex Bouzari from DDN presents: Innovating to Create a Brighter Future for AI, HPC, and Big Data.
"In this rapidly changing landscape of HPC, DDN brings fresh innovation with the stability and support experience you need. Stay in front of your challenges with the most reliable long term partner in data at scale."
Watch the video: https://wp.me/p3RLHQ-kxm
Learn more: http://ddn.com
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
The document discusses possibility thinking around cloud computing. It provides an overview of cloud computing concepts including service and delivery models. It outlines some limitations and challenges around cloud adoption. The document also discusses Intel's journey with cloud computing, moving from basic virtualization to a future state of hybrid and federated clouds. It suggests focusing on what is possible rather than limitations when considering cloud options.
Data Integration for Big Data (OOW 2016, Co-Presented With Oracle)Rittman Analytics
Oracle Data Integration Platform is a cornerstone for big data solutions that provides five core capabilities: business continuity, data movement, data transformation, data governance, and streaming data handling. It includes eight core products that can operate in the cloud or on-premise, and is considered the most innovative in areas like real-time/streaming integration and extract-load-transform capabilities with big data technologies. The platform offers a comprehensive architecture covering key areas like data ingestion, preparation, streaming integration, parallel connectivity, and governance.
This document is a presentation on Big Data by Oleksiy Razborshchuk from Oracle Canada. The presentation covers Big Data concepts, Oracle's Big Data solution including its differentiators compared to DIY Hadoop clusters, and use cases and implementation examples. The agenda includes discussing Big Data, Oracle's solution, and use cases. Key points covered are the value of Oracle's Big Data Appliance which provides faster time to value and lower costs compared to building your own Hadoop cluster, and how Oracle provides an integrated Big Data environment and analytics platform. Examples of Big Data solutions for financial services are also presented.
Oracle Big Data Appliance and Big Data SQL for advanced analyticsjdijcks
Overview presentation showing Oracle Big Data Appliance and Oracle Big Data SQL in combination with why this really matters. Big Data SQL brings you the unique ability to analyze data across the entire spectrum of system, NoSQL, Hadoop and Oracle Database.
This document discusses how telecom companies can leverage artificial intelligence and analytics to drive digital transformation. It identifies key opportunities for AI including improving the customer experience, fraud mitigation, and predictive maintenance. It then outlines the components of a telecom data lake that can support these advanced analytics initiatives. Examples of AI use cases for different telecom business functions like marketing, network operations, and security are also provided. The document argues that a data lake platform optimized for analytics can help telecom companies achieve business and innovation goals through improved operations, new revenue streams, and lower costs.
TAKE A LOOK AT THE TOP 7 SKILLS THAT A DATA ENGINEER CERTAINLY HAS TO HAVEEmilySmith271958
These days, everyone aspires to have a career in data science. What about those who work as data engineers? The reality of the matter is that a Data Scientist is only as good as the quality of the data they are given to work with.
Dell NVIDIA AI Roadshow - South Western OntarioBill Wong
- Artificial intelligence (AI) is mimicking human intelligence through machine algorithms like those used for chess and facial recognition. Machine learning (ML) is a subset of AI that uses algorithms to parse data, learn from data, and make predictions. Deep learning (DL) uses artificial neural networks to develop relationships in data and is used for applications like driverless cars and cybersecurity.
- AI technologies are enabling digital transformation and require infrastructure like edge computing, GPUs, FPGAs, deep learning accelerators, and specialized hardware to power applications of AI, ML, and DL. Dell Technologies provides platforms and solutions to accelerate AI workloads and support digital transformation.
Transform Banking with Big Data and Automated Machine Learning 9.12.17Cloudera, Inc.
Banks are rich in valuable data and can build and maintain a competitive advantage by identifying and executing on high-value machine learning projects leveraging the rich data available.This webinar will describe use cases fit for big data and machine learning in the banking sector (commercial, consumer, regulatory, and markets) and the impact they can have for your organization.
3 things to learn:
* How to create a next generation data platform and why it is important
* How to monetize big data using predictive modeling and machine learning
* What is needed for automated machine learning as a sustainable, cost-effective, and efficient solution
Solving the Really Big Tech Problems with IoTEric Kavanagh
The Briefing Room with Dr. Robin Bloor and HPE Security
The Internet of Things brings new technological problems: sensor communications are bi-directional, the scale of data generation points has no precedent and, in this new world, security, privacy and data protection need to go out to the edge. Likely, most of that data lands in Hadoop and Big Data platforms. With the need for rapid analytics never greater, companies try to seize opportunities in tighter time windows. Yet, cyber-threats are at an all-time high, targeting the most valuable of assets—the data.
Register for this episode of The Briefing Room to hear Analyst Dr. Robin Bloor explain the implications of today's divergent data forces. He’ll be briefed by Reiner Kappenberger of HPE, who will discuss how a recent innovation -- NiFi -- is revolutionizing the big data ecosystem. He’ll explain how this technology dramatically simplifies data flow design, enabling a new era of business-driven analysis, while also protecting sensitive data.
Cw13 big data and apache hadoop by amr awadallah-clouderainevitablecloud
This document provides an introduction to big data and Apache Hadoop from Cloudera. It discusses how data has changed with 90% now being unstructured, and how Hadoop can address this by allowing storage and analysis of large amounts of diverse data types. It summarizes Cloudera's Hadoop-based platform for batch and real-time processing across industries. Key benefits of Hadoop discussed include flexibility, scalability, and economics for cost-effectively storing and analyzing large amounts of data.
This document provides an overview and agenda for a presentation on big data landscape and implementation strategies. It defines big data, describes its key characteristics of volume, velocity and variety. It outlines the big data technology landscape including data acquisition, storage, organization and analysis tools. Finally it discusses an integrated big data architecture and considerations for implementation.
This document discusses computer vision and the internet of things (IoT) market. It provides estimates that the camera market will be worth $350 billion annually by 2020, while central management, archiving and analytics software will be worth $5 billion annually. It also discusses Intel's portfolio for computer vision, including smart cameras, video gateways, and datacenter/cloud capabilities. The document outlines the computer vision processing pipeline and differences between application, algorithm, and performance developers. It explores cloud versus client deployment and the evolution of programming models for computer vision.
Top 10 Most Demand IT Certifications Course in 2020 - MildainTrainingsMildain Solutions
The professionals in the field of Information Technology understands the importance of certification to their career and growth.
The information provided in this guide is backed by real data. Let us look at the top IT certifications that will remain to be a trend in 2020.
Mildaintrainings https://mildaintrainings.com/ offers Several trainings all over the world.
The Future of Data Management: The Enterprise Data HubCloudera, Inc.
The document discusses the future of data management through the use of an enterprise data hub (EDH). It notes that an EDH provides a centralized platform for ingesting, storing, exploring, processing, analyzing and serving diverse data from across an organization on a large scale in a cost effective manner. This approach overcomes limitations of traditional data silos and enables new analytic capabilities.
Similar to Top 5 In-demand technologies to Learn in 2020 (20)
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Full-RAG: A modern architecture for hyper-personalizationZilliz
Mike Del Balso, CEO & Co-Founder at Tecton, presents "Full RAG," a novel approach to AI recommendation systems, aiming to push beyond the limitations of traditional models through a deep integration of contextual insights and real-time data, leveraging the Retrieval-Augmented Generation architecture. This talk will outline Full RAG's potential to significantly enhance personalization, address engineering challenges such as data management and model training, and introduce data enrichment with reranking as a key solution. Attendees will gain crucial insights into the importance of hyperpersonalization in AI, the capabilities of Full RAG for advanced personalization, and strategies for managing complex data integrations for deploying cutting-edge AI solutions.
Building RAG with self-deployed Milvus vector database and Snowpark Container...Zilliz
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Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
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TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
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Maruthi Prithivirajan, Head of ASEAN & IN Solution Architecture, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
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01
04
02
03
05
TOP 5 technologies of 2020 Jobs and Average Salary
Learning PathSkills Required
Further Learning
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Why consider Cloud Computing?
$128,134/annum 6,80,375 INR/annum £60,000/annum
Source: Zip recruiter & Indeed.com
Average salary of a Cloud Engineer
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Skills to have: Cloud Computing
• Knowing a popular cloud
service provider like AWS,
Azure or GCP
• Important services like EC2,
Azure App Services, Google
Cloud Engine and RDS
• Cloud architecture designing
skills are a huge positive
Cloud Service Providers Networking
• A good understanding of how to
connect the on-premises network to
a cloud IaaS network
• Being familiar with the cloud
providers performance optimization
techniques
• Automating large configuration
changes
Linux skills
• Command line Linux skills are
important because 90% of the cloud
servers run on Linux
• Monitoring tools on Linux like Top
and PS
• Shell scripting to run scripts in the
server for a repetitive task
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Skills to have: Cloud Computing
• Containerization tools will be a
huge help, for example, Docker
and Kubernetes
• Knowledge of version control tools
• Good understanding of a CI/CD
pipeline and tools like Azure
DevOps
DevOps Skills Programming & Database skills
• Any programming language will help,
especially Python or Java
• Being familiar with Database tools lie
RDS will help a lot as every company
needs a database
• Connecting servers and database
using backend code
Security and Disaster Recovery
• Data encryption skills and key
management
• Access management skills where
you can create users and give
permissions
• Deep knowledge about disaster
recovery techniques
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Career paths: Cloud Computing
1
CLOUD SOLUTIONS
ARCHITECT
2
CLOUD
DEVELOPER
CLOUD SYSOPS
ADMIN
3 4
CLOUD DEVOPS
ENGINEER
5
CLOUD SUPPORT
ENGINEER
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What is Data Science?
Data science is a multi-disciplinary field that uses scientific methods, processes, algorithms
and systems to extract knowledge and insights from structured and unstructured data
Data Scientists make data a friendlier entity in today’s world!
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Average Salary: Data Scientist
$ 88,500 ₹ 10 LPAJunior Data Scientist
$ 125,500 ₹ 20 LPASenior Data Scientist
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What is DevOps
DevOps can be thought of as a set of concepts and practices involving both operations and
development engineers, where they participate together throughout the entire life cycle of
software, from the design stage to the development stage till the production support.
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Roles and Responsibilities
Various job roles in DevOps are as follows
Software Tester
DevOps Architect
Automation Engineer
Security Engineer
Integration Specialist
Release Manager
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What is Big Data?
Big Data is a collection of extremely large datasets that may be analyzed computationally
to reveal patterns, trends, and associations, especially relating to human behavior and
interactions.
It captures voices of the flight crew, recordings of microphones
and earphones, and the performance information of the
aircraft.
Social media such as Facebook and Twitter hold information
and the views posted by millions of people across the globe.
Black Box Data
Social Media Data
Stock Exchange
Data
Search Engine
Data
Transport DataPower Grid
Data
Similar
Examples:
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Top Trending technologies
Business Intelligence and ETL
BI is the technology, the applications and practices of which are deployed for the collection, integration, analysis, and
presentation of business data with the purpose of supporting better business decision-making.’