現地のインストラクターによるライブビッグデータトレーニングコースは、ビッグデータの基本概念の紹介から始め、データ分析を実行するために使用されるプログラミング言語と方法論へと進んでいきます。ビッグデータストレージ、分散処理、およびスケーラビリティを実現するためのツールとインフラストラクチャについては、デモ演習セッションで説明し、比較し、実装します。ビッグデータトレーニングは、「オンサイトライブトレーニング」または「リモートライブトレーニング」として利用できます。現地でのトレーニングは、 NobleProg提携の企業研修センターで受講可能です。リモートライブトレーニングは、インタラクティブなリモートデスクトップを介して実行されます。
お客様の声
トレーナーが外部ディスクに提供した、すでに準備されたVMですべてのデータとソフトウェアを使用できる状態になったこと。
vyzVoice
コース: Hadoop for Developers and Administrators
Machine Translated
材料の範囲
Maciej Jonczyk
コース: From Data to Decision with Big Data and Predictive Analytics
Machine Translated
MLの分野における知識の体系化
Orange Polska
コース: From Data to Decision with Big Data and Predictive Analytics
Machine Translated
研修後に検討することができる多くの問題
Klaudia Kłębek
コース: Data Mining z wykorzystaniem R
Machine Translated
トレーナーは非常に知識が豊富で、私が興味を持った分野が含まれていました。
Mohamed Salama
コース: Data Mining & Machine Learning with R
Machine Translated
非常にニーズに合わせて調整。
Yashan Wang
コース: Data Mining with R
Machine Translated
リチャードは、非常に落ち着いて組織的で、分析的な洞察力を持っています。まさにこの種のことを提示するのに必要な資質です。
Kieran Mac Kenna
コース: Spark for Developers
Machine Translated
私は行なわれた練習が好きです。
Nour Assaf
コース: Data Mining and Analysis
Machine Translated
実践的なエクササイズとトレーナーは、複雑なトピックを簡単に説明することができます。
youssef chamoun
コース: Data Mining and Analysis
Machine Translated
提供された情報は面白かったし、デュレックスのデータが提供され、私たちがよく知っているデータを処理し、結果を得るための操作を実行したときに、最も重要な部分が終わりに向かっていた。
Jessica Chaar
コース: Data Mining and Analysis
Machine Translated
私は主にトレーナーが実際に生き生きとした例を与えるのを好んだ
Simon Hahn
コース: Administrator Training for Apache Hadoop
Machine Translated
私は真剣にトレーナーの大きな能力を享受しました。
Grzegorz Gorski
コース: Administrator Training for Apache Hadoop
Machine Translated
私は本当に多くの実践的なセッションを楽しんだ。
Jacek Pieczątka
コース: Administrator Training for Apache Hadoop
Machine Translated
私はその情報が面白かったと思った。
Allison May
コース: Data Visualization
Machine Translated
Jeffは教育データに適用できるデータと例を利用してくれて本当に感謝しています。彼はそれを面白くインタラクティブにしました。
Carol Wells Bazzichi
コース: Data Visualization
Machine Translated
すべてのグラフの種類とそのグラフの種類について学習します。混乱の価値を学ぶ。時間データを表示する方法について学習する。
Susan Williams
コース: Data Visualization
Machine Translated
トレーナーは熱狂的でした。
Diane Lucas
コース: Data Visualization
Machine Translated
私はコンテンツ/インストラクターが本当に好きだった。
Craig Roberson
コース: Data Visualization
Machine Translated
私は実践的な学習者であり、これは彼がたくさん行ったことでした。
Lisa Comfort
コース: Data Visualization
Machine Translated
私は例を気に入った。
Peter Coleman
コース: Data Visualization
Machine Translated
私は一般的に例の恩恵を受けていました。
Peter Coleman
コース: Data Visualization
Machine Translated
私は良い現実世界の例、既存のレポートのレビューを楽しんだ。
Ronald Parrish
コース: Data Visualization
Machine Translated
考えられるシナリオとケース
zhaopeng liu - Fmr
コース: Spark for Developers
Machine Translated
ケーススタディ
国栋 张
コース: Spark for Developers
Machine Translated
セッションのすべての部分
Eric Han - Fmr
コース: Spark for Developers
Machine Translated
私は本当にトレーナーのより多くを分かち合う意欲から恩恵を受けました。
Balaram Chandra Paul
コース: A practical introduction to Data Analysis and Big Data
Machine Translated
私たちは、環境全体についてもっと知っています。
John Kidd
コース: Spark for Developers
Machine Translated
トレーナーはクラスを面白くて面白くし、終日のトレーニングでかなり役立ちました。
Ryan Speelman
コース: Spark for Developers
Machine Translated
トレーナーは、ユーモアと現実のストーリーを組み合わせて優れたスタイルを持っていて、手元にある人に近づけることができたと思います。私はこの教授を将来的に強く勧めます。
コース: Spark for Developers
Machine Translated
インタラクティブな学習方法を非常に気に入っていました。
Luigi Loiacono
コース: Data Analysis with Hive/HiveQL
Machine Translated
非常に実践的なトレーニングであり、私は実践的な練習が好きでした。
Proximus
コース: Data Analysis with Hive/HiveQL
Machine Translated
私は良い概観、理論と演習の間の良いバランスから恩恵を受けました。
Proximus
コース: Data Analysis with Hive/HiveQL
Machine Translated
バーチャルマシンのおかげで、ダイナミックなやりとりを楽しんで、テーマを「実践」しました。とても刺激的でした！
Philippe Job
コース: Data Analysis with Hive/HiveQL
Machine Translated
Ernestoは、Sparkとそのさまざまなモジュールを使用するという高水準の概念について説明してくれました。
Michael Nemerouf
コース: Spark for Developers
Machine Translated
私はトレーナーの能力と知識の恩恵を受けました。
Jonathan Puvilland
コース: Data Analysis with Hive/HiveQL
Machine Translated
私は一般的に技術の恩恵を受けていました。
Continental AG / Abteilung: CF IT Finance
コース: A practical introduction to Data Analysis and Big Data
Machine Translated
全体的にコンテンツは良好でした。
Sameer Rohadia
コース: A practical introduction to Data Analysis and Big Data
Machine Translated
演習例;実務経験の共有
澳新银行
コース: Data Vault: Building a Scalable Data Warehouse
Machine Translated
データウェアハウスに関する教師の知識は非常に包括的であり、賞賛です。
澳新银行
コース: Data Vault: Building a Scalable Data Warehouse
Machine Translated
先生は詳細を説明し、雰囲気について話し合った。
澳新银行
コース: Data Vault: Building a Scalable Data Warehouse
Machine Translated
Michael氏のトレーナーは、Big DataとRの主題について非常に精通しており、熟練しています。彼は非常に柔軟で、トレーニング会議の顧客のニーズを迅速にカスタマイズできます。彼はまた、外出先で技術的および主題の問題を解決する能力も非常に高いです。ファンタスティックでプロフェッショナルなトレーニング！
Xiaoyuan Geng - Ottawa Research and Development Center, Science Technology Branch, Agriculture and Agri-Food Canada
コース: Programming with Big Data in R
Machine Translated
私は本当に新しいパッケージの導入を楽しんだ。
Ottawa Research and Development Center, Science Technology Branch, Agriculture and Agri-Food Canada
コース: Programming with Big Data in R
Machine Translated
チューターのマイケル・ヤン氏は聴衆と非常によく交流し、指示は明確でした。また、チューターは、トレーニング中の生徒からのリクエストに基づいて、より多くの情報を追加します。
Ottawa Research and Development Center, Science Technology Branch, Agriculture and Agri-Food Canada
コース: Programming with Big Data in R
Machine Translated
主題とペースは完璧でした。
Tim - Ottawa Research and Development Center, Science Technology Branch, Agriculture and Agri-Food Canada
コース: Programming with Big Data in R
Machine Translated
例とトレーニングの資料は十分で、あなたがしていることを理解しやすくなりました。
Teboho Makenete
コース: Data Science for Big Data Analytics
Machine Translated
これは、私がこれまでに受けたエクササイズプログラミングコースの最も実践的なものの1つです。
Laura Kahn
コース: Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP
Machine Translated
これは私が13年のキャリアで務めた最高品質のオンライントレーニングの1つです。これからもいい結果を出し続けてください！。
コース: Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP
Machine Translated
とても実践的でしたが、Clouded / Hardtopで実際にやっている時間の半分を過ごし、異なるコマンドを実行したり、システムをチェックしたりしました。余分な資料（書籍、ウェブサイトなど）は本当に感謝しており、私たちは引き続き学習しなければなりません。インストールは非常に面白かったし、とても便利でした。最初からクラスタをセットアップするのは本当に良かったです。
Ericsson
コース: Administrator Training for Apache Hadoop
Machine Translated
リチャードのトレーニングスタイルは興味深いものでしたが、実際の使用例はこのコンセプトを家庭に持ち込むのを助けました。
Jamie Martin-Royle - NBrown Group
コース: From Data to Decision with Big Data and Predictive Analytics
Machine Translated
内容は、とても面白かったし、大学の最後の1年で私が助けてくれると思ったので。
Krishan Mistry - NBrown Group
コース: From Data to Decision with Big Data and Predictive Analytics
Machine Translated
トレーナーは、ユーモアと現実のストーリーを組み合わせて優れたスタイルを持っていて、手元にある人に近づけることができたと思います。私はこの教授を将来的に強く勧めます。
コース: Spark for Developers
Machine Translated
これは私が13年のキャリアで務めた最高品質のオンライントレーニングの1つです。これからもいい結果を出し続けてください！。
コース: Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP
Machine Translated
ビッグデータコース概要
コース名
期間
概要
コース名
期間
概要
21 時間
In this instructor-led, live training in 日本, 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 時間
This instructor-led, live training in 日本 (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 時間
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 時間
Audience
If you try to make sense out of the data you have access to or want to analyse unstructured data available on the net (like Twitter, Linked in, etc...) this course is for you.
It is mostly aimed at decision makers and people who need to choose what data is worth collecting and what is worth analyzing.
It is not aimed at people configuring the solution, those people will benefit from the big picture though.
Delivery Mode
During the course delegates will be presented with working examples of mostly open source technologies.
Short lectures will be followed by presentation and simple exercises by the participants
Content and Software used
All software used is updated each time the course is run, so we check the newest versions possible.
It covers the process from obtaining, formatting, processing and analysing the data, to explain how to automate decision making process with machine learning.
If you try to make sense out of the data you have access to or want to analyse unstructured data available on the net (like Twitter, Linked in, etc...) this course is for you.
It is mostly aimed at decision makers and people who need to choose what data is worth collecting and what is worth analyzing.
It is not aimed at people configuring the solution, those people will benefit from the big picture though.
Delivery Mode
During the course delegates will be presented with working examples of mostly open source technologies.
Short lectures will be followed by presentation and simple exercises by the participants
Content and Software used
All software used is updated each time the course is run, so we check the newest versions possible.
It covers the process from obtaining, formatting, processing and analysing the data, to explain how to automate decision making process with machine learning.
35 時間
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 時間
Big Data is a term that refers to solutions destined for storing and processing large data sets. Developed by Google initially, these Big Data solutions have evolved and inspired other similar projects, many of which are available as open-source. R is a popular programming language in the financial industry.
14 時間
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 時間
The course is part of the Data Scientist skill set (Domain: Data and Technology).
35 時間
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 時間
Participants who complete this instructor-led, live training in 日本 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 時間
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 時間
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 時間
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 時間
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 時間
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 時間
This instructor-led, live training in 日本 (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 時間
This instructor-led, live training in 日本 (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 時間
This instructor-led, live training in 日本 (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 時間
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 時間
Audience:
The course is intended for IT specialists looking for a solution to store and process large data sets in a distributed system environment
Goal:
Deep knowledge on Hadoop cluster administration.
The course is intended for IT specialists looking for a solution to store and process large data sets in a distributed system environment
Goal:
Deep knowledge on Hadoop cluster administration.
28 時間
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 時間
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 時間
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 時間
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 時間
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 時間
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 時間
Hadoop is the most popular Big Data processing framework.
14 時間
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 時間
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 時間
In this instructor-led training in 日本, 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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