Big Data é um termo que se refere a soluções destinadas a armazenar e processar grandes conjuntos de dados. Desenvolvido inicialmente pelo Google, essas soluções Big Data evoluíram e inspiraram outros projetos similares, muitos dos quais estão disponíveis como opensource. Alguns exemplos incluem Apache Hadoop, Cassandra e Cloudera Impala. De acordo com os relatórios da Gartner, BigData é o próximo grande passo no TI logo após a Cloud Computing e será uma tendência líder nos próximos anos. Nossos curso de BigData começam com uma introdução aos conceitos elementares de Big Data, em seguida, progridem nas linguagens de programação e metodologias utilizadas para realizar análise de dados. As ferramentas e a infra-estrutura para permitir armazenamento de grandes dimensões, processamento distribuído e escalabilidade são discutidas, comparadas e implementadas em sessões de demonstração. O treinamento BigData está disponível em vários formatos, incluindo treinamento ao vivo no local e treinamento online ao vivo e interativo. O treinamento local BigData pode ser realizado nas instalações do cliente no Brasil ou nos centros de treinamento locais NobleProg no Brasil. O treinamento ao vivo remoto é realizado por meio de uma área de trabalho remota e interativa.
NobleProg -- Seu Provedor de Treinamento Local
Declaração de Clientes
O fato de que todos os dados e software estavam prontos para uso em uma VM já preparada, fornecida pelo instrutor em discos externos.
vyzVoice
Curso: Hadoop for Developers and Administrators
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variedade de material
Maciej Jonczyk
Curso: From Data to Decision with Big Data and Predictive Analytics
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sistematizando conhecimento no campo de ML
Orange Polska
Curso: From Data to Decision with Big Data and Predictive Analytics
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Muitas questões que podem ser exploradas após o treinamento
Klaudia Kłębek
Curso: Data Mining z wykorzystaniem R
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O treinador foi tão experiente e áreas incluídas eu estava interessado em.
Mohamed Salama
Curso: Data Mining & Machine Learning with R
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Muito adaptado às necessidades.
Yashan Wang
Curso: Data Mining with R
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Richard é muito calmo e metódico, com uma visão analítica - exatamente as qualidades necessárias para apresentar esse tipo de curso.
Kieran Mac Kenna
Curso: Spark for Developers
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Eu gosto dos exercícios feitos.
Nour Assaf
Curso: Data Mining and Analysis
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O exercício prático e a capacidade do instrutor de explicar tópicos complexos em termos simples.
youssef chamoun
Curso: Data Mining and Analysis
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As informações fornecidas foram interessantes e a melhor parte foi no final quando recebemos os dados da Durex e trabalhamos na Data com a qual estamos familiarizados e realizamos operações para obter resultados.
Jessica Chaar
Curso: Data Mining and Analysis
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Eu gostei principalmente do treinador dando exemplos reais ao vivo.
Simon Hahn
Curso: Administrator Training for Apache Hadoop
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Eu gostei genuinamente das grandes competências do Trainer.
Grzegorz Gorski
Curso: Administrator Training for Apache Hadoop
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Eu realmente gostei das muitas sessões práticas.
Jacek Pieczątka
Curso: Administrator Training for Apache Hadoop
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compartilhar diagrama de conceito e também amostra para mãos sujas
Mark Yang - FMR
Curso: Spark for Developers
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Eu pensei que a informação era interessante.
Allison May
Curso: Data Visualization
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Eu realmente gostei que Jeff utilizou dados e exemplos que eram aplicáveis aos dados educacionais. Ele tornou interessante e interativo.
Carol Wells Bazzichi
Curso: Data Visualization
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Aprendendo sobre todos os tipos de gráficos e para que eles são usados. Aprendendo o valor da desordem. Aprendendo sobre os métodos para mostrar dados de tempo.
Susan Williams
Curso: Data Visualization
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Treinador estava entusiasmado.
Diane Lucas
Curso: Data Visualization
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Eu realmente gostei do conteúdo / instrutor.
Craig Roberson
Curso: Data Visualization
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Eu sou um aluno prático e isso foi algo que ele fez muito.
Lisa Comfort
Curso: Data Visualization
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Eu gostei dos exemplos.
Peter Coleman
Curso: Data Visualization
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Eu geralmente me beneficiava dos exemplos.
Peter Coleman
Curso: Data Visualization
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Gostei dos bons exemplos do mundo real, revisões de relatórios existentes.
Ronald Parrish
Curso: Data Visualization
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Cenários e casos aplicáveis
zhaopeng liu - Fmr
Curso: Spark for Developers
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Análise de caso
国栋 张
Curso: Spark for Developers
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todas as partes desta sessão
Eric Han - Fmr
Curso: Spark for Developers
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Eu realmente me beneficiei da disposição do treinador em compartilhar mais.
Balaram Chandra Paul
Curso: A practical introduction to Data Analysis and Big Data
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Sabemos muito mais sobre todo o ambiente.
John Kidd
Curso: Spark for Developers
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O treinador tornou a aula interessante e divertida, o que ajuda bastante com o treinamento durante todo o dia.
Ryan Speelman
Curso: Spark for Developers
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Eu acho que o treinador teve um excelente estilo de combinar humor e histórias da vida real para tornar os assuntos em mãos muito acessíveis. Eu recomendaria altamente este professor no futuro.
Curso: Spark for Developers
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Gostei muito da forma interativa de aprender.
Luigi Loiacono
Curso: Data Analysis with Hive/HiveQL
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Foi um treinamento muito prático, gostei dos exercícios práticos.
Proximus
Curso: Data Analysis with Hive/HiveQL
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Eu fui beneficiado pela boa visão geral, bom equilíbrio entre teoria e exercícios.
Proximus
Curso: Data Analysis with Hive/HiveQL
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Gostei da interação dinâmica e “hands-on” do assunto, graças à Máquina Virtual, muito estimulante !.
Philippe Job
Curso: Data Analysis with Hive/HiveQL
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Ernesto fez um ótimo trabalho explicando os conceitos de alto nível do uso do Spark e seus vários módulos.
Michael Nemerouf
Curso: Spark for Developers
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Eu me beneficiei da competência e conhecimento do treinador.
Jonathan Puvilland
Curso: Data Analysis with Hive/HiveQL
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Eu geralmente me beneficiava da apresentação de tecnologias.
Continental AG / Abteilung: CF IT Finance
Curso: A practical introduction to Data Analysis and Big Data
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Em geral, o conteúdo foi bom.
Sameer Rohadia
Curso: A practical introduction to Data Analysis and Big Data
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Exemplo de exercícios; compartilhamento de experiências de trabalho prático
澳新银行
Curso: Data Vault: Building a Scalable Data Warehouse
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Cubo e DV
Alan Xie
Curso: Data Vault: Building a Scalable Data Warehouse
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O conhecimento do professor no data warehouse é abrangente, e ele o elogia!
澳新银行
Curso: Data Vault: Building a Scalable Data Warehouse
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A professora explicou em detalhes e discutiu a atmosfera
澳新银行
Curso: Data Vault: Building a Scalable Data Warehouse
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Michael o treinador é muito experiente e hábil sobre o assunto de Big Data e R. Ele é muito flexível e personaliza rapidamente o treinamento de atender às necessidades dos clientes. Ele também é muito capaz de resolver problemas técnicos e de assuntos em movimento. Treinamento fantástico e profissional !.
Xiaoyuan Geng - Ottawa Research and Development Center, Science Technology Branch, Agriculture and Agri-Food Canada
Curso: Programming with Big Data in R
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Gostei muito da introdução de novos pacotes.
Ottawa Research and Development Center, Science Technology Branch, Agriculture and Agri-Food Canada
Curso: Programming with Big Data in R
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O tutor, o Sr. Michael Yan, interagiu muito bem com o público, a instrução foi clara. O tutor também se esforça para adicionar mais informações com base nas solicitações dos alunos durante o treinamento.
Ottawa Research and Development Center, Science Technology Branch, Agriculture and Agri-Food Canada
Curso: Programming with Big Data in R
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O assunto e o ritmo eram perfeitos.
Tim - Ottawa Research and Development Center, Science Technology Branch, Agriculture and Agri-Food Canada
Curso: Programming with Big Data in R
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O exemplo e o material de treinamento foram suficientes e tornaram mais fácil entender o que você está fazendo.
Teboho Makenete
Curso: Data Science for Big Data Analytics
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Este é um dos melhores trabalhos práticos com os cursos de programação de exercícios que já fiz.
Laura Kahn
Curso: Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP
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Este é um dos treinamentos online de melhor qualidade que já fiz em minha carreira de 13 anos. Continue com o ótimo trabalho!.
Curso: Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP
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Foi muito prático, passamos a metade do tempo realmente fazendo as coisas no Clouded / Hardtop, executando diferentes comandos, verificando o sistema e assim por diante. Os materiais extras (livros, sites, etc.) foram muito apreciados, teremos que continuar aprendendo. As instalações foram muito divertidas e muito úteis, a configuração do cluster a partir do zero foi muito boa.
Ericsson
Curso: Administrator Training for Apache Hadoop
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Eu acho que o treinador teve um excelente estilo de combinar humor e histórias da vida real para tornar os assuntos em mãos muito acessíveis. Eu recomendaria altamente este professor no futuro.
Curso: Spark for Developers
Machine Translated
Este é um dos treinamentos online de melhor qualidade que já fiz em minha carreira de 13 anos. Continue com o ótimo trabalho!.
Curso: Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP
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Nossos Clientes
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_ireland.gif "environmental protection agency (epa) ireland")
Programas do curso Big Data
Nome do Curso
Duração
Visão geral
Nome do Curso
Duração
Visão geral
35 horas
Visão geral
Os avanços nas tecnologias e a crescente quantidade de informações estão transformando a forma como os negócios são conduzidos em muitos setores, inclusive no governo As taxas de geração de dados e arquivamento digital do governo estão em ascensão devido ao rápido crescimento de dispositivos e aplicativos móveis, sensores e dispositivos inteligentes, soluções de computação em nuvem e portais de proteção de cidadãos À medida que a informação digital se expande e se torna mais complexa, o gerenciamento, processamento, armazenamento, segurança e disposição da informação tornam-se mais complexos também Novas ferramentas de captura, pesquisa, descoberta e análise estão ajudando as organizações a obter insights de seus dados não estruturados O mercado do governo está em um ponto crítico, percebendo que a informação é um ativo estratégico, e o governo precisa proteger, alavancar e analisar informações estruturadas e não estruturadas para melhor atender e atender aos requisitos da missão À medida que os líderes do governo se esforçam para evoluir organizações de dados para cumprir com sucesso a missão, eles estão estabelecendo as bases para correlacionar dependências entre eventos, pessoas, processos e informações Soluções governamentais de alto valor serão criadas a partir de um mashup das tecnologias mais disruptivas: Dispositivos e aplicativos móveis Serviços na nuvem Tecnologias de negócios sociais e redes Big Data e analítica A IDC prevê que, até 2020, o setor de TI chegará a US $ 5 trilhões, aproximadamente US $ 1,7 trilhão a mais do que hoje, e que 80% do crescimento do setor será impulsionado por essas tecnologias da 3ª plataforma No longo prazo, essas tecnologias serão ferramentas fundamentais para lidar com a complexidade do aumento da informação digital O Big Data é uma das soluções inteligentes do setor e permite que o governo tome melhores decisões, tomando medidas baseadas em padrões revelados pela análise de grandes volumes de dados relacionados e não relacionados, estruturados e não estruturados Mas realizar essas proezas leva muito mais do que simplesmente acumular quantidades massivas de dados ”A compreensão dos volumes de BigDate traz ferramentas e tecnologias de ponta que podem analisar e extrair conhecimento útil de fluxos vastos e diversos de informações”, Tom Kalil e Fen Zhao da Escritório da Casa Branca de Política de Ciência e Tecnologia escreveu em um post no Blog OSTP A Casa Branca deu um passo no sentido de ajudar as agências a encontrar essas tecnologias quando estabeleceu a Iniciativa Nacional de Pesquisa e Desenvolvimento de Big Data em 2012 A iniciativa incluiu mais de US $ 200 milhões para aproveitar ao máximo a explosão do Big Data e as ferramentas necessárias para analisá-lo Os desafios que o Big Data apresenta são quase tão assustadores quanto sua promessa é encorajadora Armazenar dados de forma eficiente é um desses desafios Como sempre, os orçamentos são apertados, por isso as agências devem minimizar o preço permegabyte de armazenamento e manter os dados de fácil acesso para que os usuários possam obtê-lo quando quiserem e como precisam Fazer o backup de grandes quantidades de dados aumenta o desafio Analisar os dados de forma eficaz é outro grande desafio Muitas agências empregam ferramentas comerciais que permitem filtrar as montanhas de dados, identificando tendências que podem ajudá-las a operar com mais eficiência (Um estudo recente da MeriTalk descobriu que os executivos federais de TI acham que o Big Data poderia ajudar as agências a economizar mais de US $ 500 bilhões, além de cumprir os objetivos da missão) As ferramentas de big data desenvolvidas especialmente também permitem que as agências abordem a necessidade de analisar seus dados Por exemplo, o Laboratório de Análise de Dados Computacionais do Laboratório Nacional de Oak Ridge disponibilizou seu sistema de análise de dados Piranha para outras agências O sistema ajudou pesquisadores médicos a encontrar um elo que pode alertar os médicos sobre aneurismas da aorta antes que eles ataquem Ele também é usado para tarefas mais comuns, como examinar currículos para conectar candidatos a empregos a gerentes de contratação .
35 horas
Visão geral
Overview
Communications service providers (CSP) are facing pressure to reduce costs and maximize average revenue per user (ARPU), while ensuring an excellent customer experience, but data volumes keep growing. Global mobile data traffic will grow at a compound annual growth rate (CAGR) of 78 percent to 2016, reaching 10.8 exabytes per month.
Meanwhile, CSPs are generating large volumes of data, including call detail records (CDR), network data and customer data. Companies that fully exploit this data gain a competitive edge. According to a recent survey by The Economist Intelligence Unit, companies that use data-directed decision-making enjoy a 5-6% boost in productivity. Yet 53% of companies leverage only half of their valuable data, and one-fourth of respondents noted that vast quantities of useful data go untapped. The data volumes are so high that manual analysis is impossible, and most legacy software systems can’t keep up, resulting in valuable data being discarded or ignored.
With Big Data & Analytics’ high-speed, scalable big data software, CSPs can mine all their data for better decision making in less time. Different Big Data products and techniques provide an end-to-end software platform for collecting, preparing, analyzing and presenting insights from big data. Application areas include network performance monitoring, fraud detection, customer churn detection and credit risk analysis. Big Data & Analytics products scale to handle terabytes of data but implementation of such tools need new kind of cloud based database system like Hadoop or massive scale parallel computing processor ( KPU etc.)
This course work on Big Data BI for Telco covers all the emerging new areas in which CSPs are investing for productivity gain and opening up new business revenue stream. The course will provide a complete 360 degree over view of Big Data BI in Telco so that decision makers and managers can have a very wide and comprehensive overview of possibilities of Big Data BI in Telco for productivity and revenue gain.
Course objectives
Main objective of the course is to introduce new Big Data business intelligence techniques in 4 sectors of Telecom Business (Marketing/Sales, Network Operation, Financial operation and Customer Relation Management). Students will be introduced to following:
- Introduction to Big Data-what is 4Vs (volume, velocity, variety and veracity) in Big Data- Generation, extraction and management from Telco perspective
- How Big Data analytic differs from legacy data analytic
- In-house justification of Big Data -Telco perspective
- Introduction to Hadoop Ecosystem- familiarity with all Hadoop tools like Hive, Pig, SPARC –when and how they are used to solve Big Data problem
- How Big Data is extracted to analyze for analytics tool-how Business Analysis’s can reduce their pain points of collection and analysis of data through integrated Hadoop dashboard approach
- Basic introduction of Insight analytics, visualization analytics and predictive analytics for Telco
- Customer Churn analytic and Big Data-how Big Data analytic can reduce customer churn and customer dissatisfaction in Telco-case studies
- Network failure and service failure analytics from Network meta-data and IPDR
- Financial analysis-fraud, wastage and ROI estimation from sales and operational data
- Customer acquisition problem-Target marketing, customer segmentation and cross-sale from sales data
- Introduction and summary of all Big Data analytic products and where they fit into Telco analytic space
- Conclusion-how to take step-by-step approach to introduce Big Data Business Intelligence in your organization
Target Audience
- Network operation, Financial Managers, CRM managers and top IT managers in Telco CIO office.
- Business Analysts in Telco
- CFO office managers/analysts
- Operational managers
- QA managers
Communications service providers (CSP) are facing pressure to reduce costs and maximize average revenue per user (ARPU), while ensuring an excellent customer experience, but data volumes keep growing. Global mobile data traffic will grow at a compound annual growth rate (CAGR) of 78 percent to 2016, reaching 10.8 exabytes per month.
Meanwhile, CSPs are generating large volumes of data, including call detail records (CDR), network data and customer data. Companies that fully exploit this data gain a competitive edge. According to a recent survey by The Economist Intelligence Unit, companies that use data-directed decision-making enjoy a 5-6% boost in productivity. Yet 53% of companies leverage only half of their valuable data, and one-fourth of respondents noted that vast quantities of useful data go untapped. The data volumes are so high that manual analysis is impossible, and most legacy software systems can’t keep up, resulting in valuable data being discarded or ignored.
With Big Data & Analytics’ high-speed, scalable big data software, CSPs can mine all their data for better decision making in less time. Different Big Data products and techniques provide an end-to-end software platform for collecting, preparing, analyzing and presenting insights from big data. Application areas include network performance monitoring, fraud detection, customer churn detection and credit risk analysis. Big Data & Analytics products scale to handle terabytes of data but implementation of such tools need new kind of cloud based database system like Hadoop or massive scale parallel computing processor ( KPU etc.)
This course work on Big Data BI for Telco covers all the emerging new areas in which CSPs are investing for productivity gain and opening up new business revenue stream. The course will provide a complete 360 degree over view of Big Data BI in Telco so that decision makers and managers can have a very wide and comprehensive overview of possibilities of Big Data BI in Telco for productivity and revenue gain.
Course objectives
Main objective of the course is to introduce new Big Data business intelligence techniques in 4 sectors of Telecom Business (Marketing/Sales, Network Operation, Financial operation and Customer Relation Management). Students will be introduced to following:
- Introduction to Big Data-what is 4Vs (volume, velocity, variety and veracity) in Big Data- Generation, extraction and management from Telco perspective
- How Big Data analytic differs from legacy data analytic
- In-house justification of Big Data -Telco perspective
- Introduction to Hadoop Ecosystem- familiarity with all Hadoop tools like Hive, Pig, SPARC –when and how they are used to solve Big Data problem
- How Big Data is extracted to analyze for analytics tool-how Business Analysis’s can reduce their pain points of collection and analysis of data through integrated Hadoop dashboard approach
- Basic introduction of Insight analytics, visualization analytics and predictive analytics for Telco
- Customer Churn analytic and Big Data-how Big Data analytic can reduce customer churn and customer dissatisfaction in Telco-case studies
- Network failure and service failure analytics from Network meta-data and IPDR
- Financial analysis-fraud, wastage and ROI estimation from sales and operational data
- Customer acquisition problem-Target marketing, customer segmentation and cross-sale from sales data
- Introduction and summary of all Big Data analytic products and where they fit into Telco analytic space
- Conclusion-how to take step-by-step approach to introduce Big Data Business Intelligence in your organization
Target Audience
- Network operation, Financial Managers, CRM managers and top IT managers in Telco CIO office.
- Business Analysts in Telco
- CFO office managers/analysts
- Operational managers
- QA managers
21 horas
Visão geral
Público
Se você tentar entender os dados aos quais tem acesso ou quiser analisar dados não estruturados disponíveis na rede (como o Twitter, Linked in, etc ...), este curso é para você.
É principalmente destinado a tomadores de decisão e pessoas que precisam escolher quais dados valem a pena coletar e o que vale a pena analisar.
Não se destina a pessoas que configuram a solução, essas pessoas irão se beneficiar do quadro geral.
Modo de entrega
Durante o curso, os delegados serão apresentados a exemplos de trabalho, principalmente de tecnologias de código aberto.
Aulas curtas serão seguidas de apresentação e exercícios simples pelos participantes
Conteúdo e Software utilizados
Todo o software utilizado é atualizado cada vez que o curso é executado, por isso, verificamos as versões mais recentes possíveis.
Abrange o processo de obter, formatar, processar e analisar os dados, para explicar como automatizar o processo de tomada de decisão com o aprendizado de máquina.
Se você tentar entender os dados aos quais tem acesso ou quiser analisar dados não estruturados disponíveis na rede (como o Twitter, Linked in, etc ...), este curso é para você.
É principalmente destinado a tomadores de decisão e pessoas que precisam escolher quais dados valem a pena coletar e o que vale a pena analisar.
Não se destina a pessoas que configuram a solução, essas pessoas irão se beneficiar do quadro geral.
Modo de entrega
Durante o curso, os delegados serão apresentados a exemplos de trabalho, principalmente de tecnologias de código aberto.
Aulas curtas serão seguidas de apresentação e exercícios simples pelos participantes
Conteúdo e Software utilizados
Todo o software utilizado é atualizado cada vez que o curso é executado, por isso, verificamos as versões mais recentes possíveis.
Abrange o processo de obter, formatar, processar e analisar os dados, para explicar como automatizar o processo de tomada de decisão com o aprendizado de máquina.
35 horas
Visão geral
Day 1 - provides a high-level overview of essential Big Data topic areas. The module is divided into a series of sections, each of which is accompanied by a hands-on exercise.
Day 2 - explores a range of topics that relate analysis practices and tools for Big Data environments. It does not get into implementation or programming details, but instead keeps coverage at a conceptual level, focusing on topics that enable participants to develop a comprehensive understanding of the common analysis functions and features offered by Big Data solutions.
Day 3 - provides an overview of the fundamental and essential topic areas relating to Big Data solution platform architecture. It covers Big Data mechanisms required for the development of a Big Data solution platform and architectural options for assembling a data processing platform. Common scenarios are also presented to provide a basic understanding of how a Big Data solution platform is generally used.
Day 4 - builds upon Day 3 by exploring advanced topics relatng to Big Data solution platform architecture. In particular, different architectural layers that make up the Big Data solution platform are introduced and discussed, including data sources, data ingress, data storage, data processing and security.
Day 5 - covers a number of exercises and problems designed to test the delegates ability to apply knowledge of topics covered Day 3 and 4.
Day 2 - explores a range of topics that relate analysis practices and tools for Big Data environments. It does not get into implementation or programming details, but instead keeps coverage at a conceptual level, focusing on topics that enable participants to develop a comprehensive understanding of the common analysis functions and features offered by Big Data solutions.
Day 3 - provides an overview of the fundamental and essential topic areas relating to Big Data solution platform architecture. It covers Big Data mechanisms required for the development of a Big Data solution platform and architectural options for assembling a data processing platform. Common scenarios are also presented to provide a basic understanding of how a Big Data solution platform is generally used.
Day 4 - builds upon Day 3 by exploring advanced topics relatng to Big Data solution platform architecture. In particular, different architectural layers that make up the Big Data solution platform are introduced and discussed, including data sources, data ingress, data storage, data processing and security.
Day 5 - covers a number of exercises and problems designed to test the delegates ability to apply knowledge of topics covered Day 3 and 4.
21 horas
Visão geral
Este curso é um curso base de programação para todos aqueles interessados em trabalhar com BigData e tem o objetivo de madquirir conhecimentos em R, uma linguagem muito usada e funcional no contexto das Tecnologías de Informação.
14 horas
Visão geral
When traditional storage technologies don't handle the amount of data you need to store there are hundereds of alternatives. This course try to guide the participants what are alternatives for storing and analyzing Big Data and what are theirs pros and cons.
This course is mostly focused on discussion and presentation of solutions, though hands-on exercises are available on demand.
This course is mostly focused on discussion and presentation of solutions, though hands-on exercises are available on demand.
14 horas
Visão geral
The course is part of the Data Scientist skill set (Domain: Data and Technology).
35 horas
Visão geral
Big data is data sets that are so voluminous and complex that traditional data processing application software are inadequate to deal with them. Big data challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating and information privacy.
35 horas
Visão geral
Participants who complete this instructor-led, live training in Brasil will gain a practical, real-world understanding of Big Data and its related technologies, methodologies and tools.
Participants will have the opportunity to put this knowledge into practice through hands-on exercises. Group interaction and instructor feedback make up an important component of the class.
The course starts with an introduction to elemental concepts of Big Data, then progresses into the programming languages and methodologies used to perform Data Analysis. Finally, we discuss the tools and infrastructure that enable Big Data storage, Distributed Processing, and Scalability.
Participants will have the opportunity to put this knowledge into practice through hands-on exercises. Group interaction and instructor feedback make up an important component of the class.
The course starts with an introduction to elemental concepts of Big Data, then progresses into the programming languages and methodologies used to perform Data Analysis. Finally, we discuss the tools and infrastructure that enable Big Data storage, Distributed Processing, and Scalability.
14 horas
Visão geral
Vespa is an open-source big data processing and serving engine created by Yahoo. It is used to respond to user queries, make recommendations, and provide personalized content and advertisements in real-time.
This instructor-led, live training introduces the challenges of serving large-scale data and walks participants through the creation of an application that can compute responses to user requests, over large datasets in real-time.
By the end of this training, participants will be able to:
- Use Vespa to quickly compute data (store, search, rank, organize) at serving time while a user waits
- Implement Vespa into existing applications involving feature search, recommendations, and personalization
- Integrate and deploy Vespa with existing big data systems such as Hadoop and Storm.
Audience
- Developers
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
This instructor-led, live training introduces the challenges of serving large-scale data and walks participants through the creation of an application that can compute responses to user requests, over large datasets in real-time.
By the end of this training, participants will be able to:
- Use Vespa to quickly compute data (store, search, rank, organize) at serving time while a user waits
- Implement Vespa into existing applications involving feature search, recommendations, and personalization
- Integrate and deploy Vespa with existing big data systems such as Hadoop and Storm.
Audience
- Developers
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
14 horas
Visão geral
To meet compliance of the regulators, CSPs (Communication service providers) can tap into Big Data Analytics which not only help them to meet compliance but within the scope of same project they can increase customer satisfaction and thus reduce the churn. In fact since compliance is related to Quality of service tied to a contract, any initiative towards meeting the compliance, will improve the “competitive edge” of the CSPs. Therefore, it is important that Regulators should be able to advise/guide a set of Big Data analytic practice for CSPs that will be of mutual benefit between the regulators and CSPs.
The course consists of 8 modules (4 on day 1, and 4 on day 2)
The course consists of 8 modules (4 on day 1, and 4 on day 2)
35 horas
Visão geral
Advances in technologies and the increasing amount of information are transforming how law enforcement is conducted. The challenges that Big Data pose are nearly as daunting as Big Data's promise. Storing data efficiently is one of these challenges; effectively analyzing it is another.
In this instructor-led, live training, participants will learn the mindset with which to approach Big Data technologies, assess their impact on existing processes and policies, and implement these technologies for the purpose of identifying criminal activity and preventing crime. Case studies from law enforcement organizations around the world will be examined to gain insights on their adoption approaches, challenges and results.
By the end of this training, participants will be able to:
- Combine Big Data technology with traditional data gathering processes to piece together a story during an investigation
- Implement industrial big data storage and processing solutions for data analysis
- Prepare a proposal for the adoption of the most adequate tools and processes for enabling a data-driven approach to criminal investigation
Audience
- Law Enforcement specialists with a technical background
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
In this instructor-led, live training, participants will learn the mindset with which to approach Big Data technologies, assess their impact on existing processes and policies, and implement these technologies for the purpose of identifying criminal activity and preventing crime. Case studies from law enforcement organizations around the world will be examined to gain insights on their adoption approaches, challenges and results.
By the end of this training, participants will be able to:
- Combine Big Data technology with traditional data gathering processes to piece together a story during an investigation
- Implement industrial big data storage and processing solutions for data analysis
- Prepare a proposal for the adoption of the most adequate tools and processes for enabling a data-driven approach to criminal investigation
Audience
- Law Enforcement specialists with a technical background
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
14 horas
Visão geral
This classroom based training session will explore Big Data. Delegates will have computer based examples and case study exercises to undertake with relevant big data tools
14 horas
Visão geral
Objective : This training course aims at helping attendees understand why Big Data is changing our lives and how it is altering the way businesses see us as consumers. Indeed, users of big data in businesses find that big data unleashes a wealth of information and insights which translate to higher profits, reduced costs, and less risk. However, the downside was frustration sometimes when putting too much emphasis on individual technologies and not enough focus on the pillars of big data management.
Attendees will learn during this course how to manage the big data using its three pillars of data integration, data governance and data security in order to turn big data into real business value. Different exercices conducted on a case study of customer management will help attendees to better understand the underlying processes.
Attendees will learn during this course how to manage the big data using its three pillars of data integration, data governance and data security in order to turn big data into real business value. Different exercices conducted on a case study of customer management will help attendees to better understand the underlying processes.
7 horas
Visão geral
This instructor-led, live training in Brasil (online or onsite) is aimed at technical persons who wish to learn how to implement a machine learning strategy while maximizing the use of big data.
By the end of this training, participants will:
- Understand the evolution and trends for machine learning.
- Know how machine learning is being used across different industries.
- Become familiar with the tools, skills and services available to implement machine learning within an organization.
- Understand how machine learning can be used to enhance data mining and analysis.
- Learn what a data middle backend is, and how it is being used by businesses.
- Understand the role that big data and intelligent applications are playing across industries.
By the end of this training, participants will:
- Understand the evolution and trends for machine learning.
- Know how machine learning is being used across different industries.
- Become familiar with the tools, skills and services available to implement machine learning within an organization.
- Understand how machine learning can be used to enhance data mining and analysis.
- Learn what a data middle backend is, and how it is being used by businesses.
- Understand the role that big data and intelligent applications are playing across industries.
7 horas
Visão geral
This instructor-led, live training in Brasil (online or onsite) is aimed at software engineers who wish to use Sqoop and Flume for big data.
By the end of this training, participants will be able to:
- Ingest big data with Sqoop and Flume.
- Ingest data from multiple data sources.
- Move data from relational databases to HDFS and Hive.
- Export data from HDFS to a relational database.
By the end of this training, participants will be able to:
- Ingest big data with Sqoop and Flume.
- Ingest data from multiple data sources.
- Move data from relational databases to HDFS and Hive.
- Export data from HDFS to a relational database.
28 horas
Visão geral
This instructor-led, live training in Brasil (online or onsite) is aimed at technical persons who wish to deploy Talend Open Studio for Big Data to simplifying the process of reading and crunching through Big Data.
By the end of this training, participants will be able to:
- Install and configure Talend Open Studio for Big Data.
- Connect with Big Data systems such as Cloudera, HortonWorks, MapR, Amazon EMR and Apache.
- Understand and set up Open Studio's big data components and connectors.
- Configure parameters to automatically generate MapReduce code.
- Use Open Studio's drag-and-drop interface to run Hadoop jobs.
- Prototype big data pipelines.
- Automate big data integration projects.
By the end of this training, participants will be able to:
- Install and configure Talend Open Studio for Big Data.
- Connect with Big Data systems such as Cloudera, HortonWorks, MapR, Amazon EMR and Apache.
- Understand and set up Open Studio's big data components and connectors.
- Configure parameters to automatically generate MapReduce code.
- Use Open Studio's drag-and-drop interface to run Hadoop jobs.
- Prototype big data pipelines.
- Automate big data integration projects.
21 horas
Visão geral
Este curso é dedicado à especialistas TI que buscam soluções para guardas e processar grandes sets de dados num ambiente de sistema distribuido.
Objetivo de cursio: Conseguir conhecimento sobre Hadoop cluster administration.
Objetivo de cursio: Conseguir conhecimento sobre Hadoop cluster administration.
35 horas
Visão geral
Público:
O curso destina-se a especialistas em TI que procuram uma solução para armazenar e processar grandes conjuntos de dados em um ambiente de sistema distribuído
Go ai:
Profundo conhecimento em administração de cluster do Hadoop .
O curso destina-se a especialistas em TI que procuram uma solução para armazenar e processar grandes conjuntos de dados em um ambiente de sistema distribuído
Go ai:
Profundo conhecimento em administração de cluster do Hadoop .
28 horas
Visão geral
Audience:
This course is intended to demystify big data/hadoop technology and to show it is not difficult to understand.
This course is intended to demystify big data/hadoop technology and to show it is not difficult to understand.
28 horas
Visão geral
Apache Hadoop is the most popular framework for processing Big Data on clusters of servers. This course will introduce a developer to various components (HDFS, MapReduce, Pig, Hive and HBase) Hadoop ecosystem.
21 horas
Visão geral
Apache Hadoop is one of the most popular frameworks for processing Big Data on clusters of servers. This course delves into data management in HDFS, advanced Pig, Hive, and HBase. These advanced programming techniques will be beneficial to experienced Hadoop developers.
Audience: developers
Duration: three days
Format: lectures (50%) and hands-on labs (50%).
Audience: developers
Duration: three days
Format: lectures (50%) and hands-on labs (50%).
21 horas
Visão geral
Este curso introduz HBase, e é dirigido a todos aqueles desenvolvedores que utilizarão o HBase para desenvolver aplicações, e administradores que vao manejar clusters HBase.
Vamos a guiar um desenvolvedor através da arquitetura HBase e modelagem de dados e desenvolvimento de aplicações em HBase. Também vamos discutir utilizando MapReduce com HBase, e alguns tópicos administrativos.
Vamos a guiar um desenvolvedor através da arquitetura HBase e modelagem de dados e desenvolvimento de aplicações em HBase. Também vamos discutir utilizando MapReduce com HBase, e alguns tópicos administrativos.
21 horas
Visão geral
Apache Hadoop is the most popular framework for processing Big Data on clusters of servers. In this three (optionally, four) days course, attendees will learn about the business benefits and use cases for Hadoop and its ecosystem, how to plan cluster deployment and growth, how to install, maintain, monitor, troubleshoot and optimize Hadoop. They will also practice cluster bulk data load, get familiar with various Hadoop distributions, and practice installing and managing Hadoop ecosystem tools. The course finishes off with discussion of securing cluster with Kerberos.
“…The materials were very well prepared and covered thoroughly. The Lab was very helpful and well organized”
— Andrew Nguyen, Principal Integration DW Engineer, Microsoft Online Advertising
Audience
Hadoop administrators
Format
Lectures and hands-on labs, approximate balance 60% lectures, 40% labs.
“…The materials were very well prepared and covered thoroughly. The Lab was very helpful and well organized”
— Andrew Nguyen, Principal Integration DW Engineer, Microsoft Online Advertising
Audience
Hadoop administrators
Format
Lectures and hands-on labs, approximate balance 60% lectures, 40% labs.
21 horas
Visão geral
Apache Hadoop is the most popular framework for processing Big Data. Hadoop provides rich and deep analytics capability, and it is making in-roads in to tradional BI analytics world. This course will introduce an analyst to the core components of Hadoop eco system and its analytics
Audience
Business Analysts
Duration
three days
Format
Lectures and hands on labs.
Audience
Business Analysts
Duration
three days
Format
Lectures and hands on labs.
21 horas
Visão geral
Hadoop is the most popular Big Data processing framework.
14 horas
Visão geral
Audience
- Developers
Format of the Course
- Lectures, hands-on practice, small tests along the way to gauge understanding
- Developers
Format of the Course
- Lectures, hands-on practice, small tests along the way to gauge understanding
21 horas
Visão geral
This course is intended for developers, architects, data scientists or any profile that requires access to data either intensively or on a regular basis.
The major focus of the course is data manipulation and transformation.
Among the tools in the Hadoop ecosystem this course includes the use of Pig and Hive both of which are heavily used for data transformation and manipulation.
This training also addresses performance metrics and performance optimisation.
The course is entirely hands on and is punctuated by presentations of the theoretical aspects.
The major focus of the course is data manipulation and transformation.
Among the tools in the Hadoop ecosystem this course includes the use of Pig and Hive both of which are heavily used for data transformation and manipulation.
This training also addresses performance metrics and performance optimisation.
The course is entirely hands on and is punctuated by presentations of the theoretical aspects.
14 horas
Visão geral
In this instructor-led training in Brasil, participants will learn the core components of the Hadoop ecosystem and how these technologies can be used to solve large-scale problems. By learning these foundations, participants will improve their ability to communicate with the developers and implementers of these systems as well as the data scientists and analysts that many IT projects involve.
Audience
- Project Managers wishing to implement Hadoop into their existing development or IT infrastructure
- Project Managers needing to communicate with cross-functional teams that include big data engineers, data scientists and business analysts
Audience
- Project Managers wishing to implement Hadoop into their existing development or IT infrastructure
- Project Managers needing to communicate with cross-functional teams that include big data engineers, data scientists and business analysts
14 horas
Visão geral
Apache Samza is an open-source near-realtime, asynchronous computational framework for stream processing. It uses Apache Kafka for messaging, and Apache Hadoop YARN for fault tolerance, processor isolation, security, and resource management.
This instructor-led, live training introduces the principles behind messaging systems and distributed stream processing, while walking participants through the creation of a sample Samza-based project and job execution.
By the end of this training, participants will be able to:
- Use Samza to simplify the code needed to produce and consume messages.
- Decouple the handling of messages from an application.
- Use Samza to implement near-realtime asynchronous computation.
- Use stream processing to provide a higher level of abstraction over messaging systems.
Audience
- Developers
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
This instructor-led, live training introduces the principles behind messaging systems and distributed stream processing, while walking participants through the creation of a sample Samza-based project and job execution.
By the end of this training, participants will be able to:
- Use Samza to simplify the code needed to produce and consume messages.
- Decouple the handling of messages from an application.
- Use Samza to implement near-realtime asynchronous computation.
- Use stream processing to provide a higher level of abstraction over messaging systems.
Audience
- Developers
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
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