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
.png "iberoamericana de hidrocarburos (ihsa)")
.jpg "european southern observatory (eso)")
_ireland.gif "environmental protection agency (epa) ireland")
Programas do curso Big Data
Nome do Curso
Duração
Visão geral
Nome do Curso
Duração
Visão geral
21 horas
In this instructor-led, live training in Brasil, participants will learn how to use Python and Spark together to analyze big data as they work on hands-on exercises.
By the end of this training, participants will be able to:
- Learn how to use Spark with Python to analyze Big Data.
- Work on exercises that mimic real world cases.
- Use different tools and techniques for big data analysis using PySpark.
By the end of this training, participants will be able to:
- Learn how to use Spark with Python to analyze Big Data.
- Work on exercises that mimic real world cases.
- Use different tools and techniques for big data analysis using PySpark.
14 horas
This instructor-led, live training in Brasil (online or onsite) is aimed at data scientists who wish to use Excel for data mining.
- By the end of this training, participants will be able to:
- Explore data with Excel to perform data mining and analysis.
- Use Microsoft algorithms for data mining.
- Understand concepts in Excel data mining.
- By the end of this training, participants will be able to:
- Explore data with Excel to perform data mining and analysis.
- Use Microsoft algorithms for data mining.
- Understand concepts in Excel data mining.
35 horas
Advances in technologies and the increasing amount of information are transforming how business is conducted in many industries, including government. Government data generation and digital archiving rates are on the rise due to the rapid growth of mobile devices and applications, smart sensors and devices, cloud computing solutions, and citizen-facing portals. As digital information expands and becomes more complex, information management, processing, storage, security, and disposition become more complex as well. New capture, search, discovery, and analysis tools are helping organizations gain insights from their unstructured data. The government market is at a tipping point, realizing that information is a strategic asset, and government needs to protect, leverage, and analyze both structured and unstructured information to better serve and meet mission requirements. As government leaders strive to evolve data-driven organizations to successfully accomplish mission, they are laying the groundwork to correlate dependencies across events, people, processes, and information.
High-value government solutions will be created from a mashup of the most disruptive technologies:
- Mobile devices and applications
- Cloud services
- Social business technologies and networking
- Big Data and analytics
IDC predicts that by 2020, the IT industry will reach $5 trillion, approximately $1.7 trillion larger than today, and that 80% of the industry's growth will be driven by these 3rd Platform technologies. In the long term, these technologies will be key tools for dealing with the complexity of increased digital information. Big Data is one of the intelligent industry solutions and allows government to make better decisions by taking action based on patterns revealed by analyzing large volumes of data — related and unrelated, structured and unstructured.
But accomplishing these feats takes far more than simply accumulating massive quantities of data.“Making sense of thesevolumes of Big Datarequires cutting-edge tools and technologies that can analyze and extract useful knowledge from vast and diverse streams of information,” Tom Kalil and Fen Zhao of the White House Office of Science and Technology Policy wrote in a post on the OSTP Blog.
The White House took a step toward helping agencies find these technologies when it established the National Big Data Research and Development Initiative in 2012. The initiative included more than $200 million to make the most of the explosion of Big Data and the tools needed to analyze it.
The challenges that Big Data poses are nearly as daunting as its promise is encouraging. Storing data efficiently is one of these challenges. As always, budgets are tight, so agencies must minimize the per-megabyte price of storage and keep the data within easy access so that users can get it when they want it and how they need it. Backing up massive quantities of data heightens the challenge.
Analyzing the data effectively is another major challenge. Many agencies employ commercial tools that enable them to sift through the mountains of data, spotting trends that can help them operate more efficiently. (A recent study by MeriTalk found that federal IT executives think Big Data could help agencies save more than $500 billion while also fulfilling mission objectives.).
Custom-developed Big Data tools also are allowing agencies to address the need to analyze their data. For example, the Oak Ridge National Laboratory’s Computational Data Analytics Group has made its Piranha data analytics system available to other agencies. The system has helped medical researchers find a link that can alert doctors to aortic aneurysms before they strike. It’s also used for more mundane tasks, such as sifting through résumés to connect job candidates with hiring managers.
High-value government solutions will be created from a mashup of the most disruptive technologies:
- Mobile devices and applications
- Cloud services
- Social business technologies and networking
- Big Data and analytics
IDC predicts that by 2020, the IT industry will reach $5 trillion, approximately $1.7 trillion larger than today, and that 80% of the industry's growth will be driven by these 3rd Platform technologies. In the long term, these technologies will be key tools for dealing with the complexity of increased digital information. Big Data is one of the intelligent industry solutions and allows government to make better decisions by taking action based on patterns revealed by analyzing large volumes of data — related and unrelated, structured and unstructured.
But accomplishing these feats takes far more than simply accumulating massive quantities of data.“Making sense of thesevolumes of Big Datarequires cutting-edge tools and technologies that can analyze and extract useful knowledge from vast and diverse streams of information,” Tom Kalil and Fen Zhao of the White House Office of Science and Technology Policy wrote in a post on the OSTP Blog.
The White House took a step toward helping agencies find these technologies when it established the National Big Data Research and Development Initiative in 2012. The initiative included more than $200 million to make the most of the explosion of Big Data and the tools needed to analyze it.
The challenges that Big Data poses are nearly as daunting as its promise is encouraging. Storing data efficiently is one of these challenges. As always, budgets are tight, so agencies must minimize the per-megabyte price of storage and keep the data within easy access so that users can get it when they want it and how they need it. Backing up massive quantities of data heightens the challenge.
Analyzing the data effectively is another major challenge. Many agencies employ commercial tools that enable them to sift through the mountains of data, spotting trends that can help them operate more efficiently. (A recent study by MeriTalk found that federal IT executives think Big Data could help agencies save more than $500 billion while also fulfilling mission objectives.).
Custom-developed Big Data tools also are allowing agencies to address the need to analyze their data. For example, the Oak Ridge National Laboratory’s Computational Data Analytics Group has made its Piranha data analytics system available to other agencies. The system has helped medical researchers find a link that can alert doctors to aortic aneurysms before they strike. It’s also used for more mundane tasks, such as sifting through résumés to connect job candidates with hiring managers.
21 horas
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
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
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
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
The course is part of the Data Scientist skill set (Domain: Data and Technology).
35 horas
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
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
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
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
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
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
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
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
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
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
The course is dedicated to IT specialists that are looking for a solution to store and process large data sets in distributed system environment
Course goal:
Getting knowledge regarding Hadoop cluster administration
Course goal:
Getting knowledge regarding Hadoop cluster administration
35 horas
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
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
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
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
This course introduces HBase – a NoSQL store on top of Hadoop. The course is intended for developers who will be using HBase to develop applications, and administrators who will manage HBase clusters.
We will walk a developer through HBase architecture and data modelling and application development on HBase. It will also discuss using MapReduce with HBase, and some administration topics, related to performance optimization. The course is very hands-on with lots of lab exercises.
Duration : 3 days
Audience : Developers & Administrators
We will walk a developer through HBase architecture and data modelling and application development on HBase. It will also discuss using MapReduce with HBase, and some administration topics, related to performance optimization. The course is very hands-on with lots of lab exercises.
Duration : 3 days
Audience : Developers & Administrators
21 horas
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
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
Hadoop is the most popular Big Data processing framework.
14 horas
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
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
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
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