Lokale, door een instructeur geleide, live Big Data-trainingen beginnen met een inleiding tot elementaire concepten van Big Data en vervolgens de voortgang in de programmeertalen en -methodieken die worden gebruikt om gegevensanalyse uit te voeren. Tools en infrastructuur voor het inschakelen van Big Data-opslag, gedistribueerde verwerking en schaalbaarheid worden besproken, vergeleken en geïmplementeerd in demo-oefensessies. Big Data-training is beschikbaar als 'live training op locatie' of 'live training op afstand'. Onsite live training kan lokaal worden uitgevoerd op het terrein van de klant in Nederland of in NobleProg bedrijfsopleidingscentra in Nederland . Live-training op afstand wordt uitgevoerd via een interactieve, externe desktop. NobleProg - uw lokale trainingsaanbieder
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Getuigenissen
Ik heb genoten van de goede balans tussen theorie en praktische labs ..
N. V. Nederlandse Spoorwegen
Cursus: Apache Ignite: Improve Speed, Scale and Availability with In-Memory Computing
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Ik had over het algemeen baat bij het beter begrijpen van Ignite ..
N. V. Nederlandse Spoorwegen
Cursus: Apache Ignite: Improve Speed, Scale and Availability with In-Memory Computing
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Ik vond vooral de goede lezingen leuk.
N. V. Nederlandse Spoorwegen
Cursus: Apache Ignite: Improve Speed, Scale and Availability with In-Memory Computing
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Het feit dat alle gegevens en software klaar waren om te gebruiken op een reeds voorbereide VM, geleverd door de trainer op externe schijven.
vyzVoice
Cursus: Hadoop for Developers and Administrators
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bereik van materiaal
Maciej Jonczyk
Cursus: From Data to Decision with Big Data and Predictive Analytics
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systematisering van kennis op het gebied van ML
Orange Polska
Cursus: From Data to Decision with Big Data and Predictive Analytics
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Veel problemen die na de training kunnen worden onderzocht
Klaudia Kłębek
Cursus: Data Mining z wykorzystaniem R
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De trainer was zo deskundig en inclusief gebieden waarin ik geïnteresseerd was.
Mohamed Salama
Cursus: Data Mining & Machine Learning with R
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Zeer afgestemd op de behoeften.
Yashan Wang
Cursus: Data Mining with R
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Richard is erg kalm en methodisch, met een analytisch inzicht - precies de kwaliteiten die nodig zijn om dit soort cursus te presenteren.
Kieran Mac Kenna
Cursus: Spark for Developers
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Ik hou van de uitgevoerde oefeningen.
Nour Assaf
Cursus: Data Mining and Analysis
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De hands-on oefening en de trainercapaciteit om complexe onderwerpen in eenvoudige bewoordingen uit te leggen.
youssef chamoun
Cursus: Data Mining and Analysis
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De gegeven informatie was interessant en het beste deel was tegen het einde toen we werden voorzien van gegevens van Durex en werkten aan gegevens die we kennen en uitvoeren om resultaten te krijgen.
Jessica Chaar
Cursus: Data Mining and Analysis
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Ik vond vooral de trainer die echte voorbeelden gaf.
Simon Hahn
Cursus: Administrator Training for Apache Hadoop
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Ik genoot oprecht van de grote competenties van Trainer.
Grzegorz Gorski
Cursus: Administrator Training for Apache Hadoop
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Ik genoot enorm van de vele hands-on sessies.
Jacek Pieczątka
Cursus: Administrator Training for Apache Hadoop
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deel conceptdiagram en ook monster voor handen vuil
Mark Yang - FMR
Cursus: Spark for Developers
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Ik dacht dat de informatie interessant was.
Allison May
Cursus: Data Visualization
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Ik waardeerde echt dat Jeff gegevens en voorbeelden gebruikte die van toepassing waren op onderwijsgegevens. Hij maakte het interessant en interactief.
Carol Wells Bazzichi
Cursus: Data Visualization
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Leren over alle grafiektypen en waarvoor ze worden gebruikt. De waarde van rommel leren kennen. Leren over de methoden om tijdgegevens weer te geven.
Susan Williams
Cursus: Data Visualization
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Trainer was enthousiast.
Diane Lucas
Cursus: Data Visualization
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Ik vond de inhoud / instructeur erg goed.
Craig Roberson
Cursus: Data Visualization
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Ik ben een hands-on leerling en dit was iets dat hij veel deed.
Lisa Comfort
Cursus: Data Visualization
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Ik vond de voorbeelden leuk.
Peter Coleman
Cursus: Data Visualization
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Over het algemeen was ik gebaat bij de voorbeelden.
Peter Coleman
Cursus: Data Visualization
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Ik heb genoten van de goede voorbeelden uit de echte wereld, beoordelingen van bestaande rapporten.
Ronald Parrish
Cursus: Data Visualization
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Toepasselijke scenario's en cases
zhaopeng liu - Fmr
Cursus: Spark for Developers
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Case analyse
国栋 张
Cursus: Spark for Developers
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alle delen van deze sessie
Eric Han - Fmr
Cursus: Spark for Developers
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Ik had echt baat bij de bereidheid van de trainer om meer te delen.
Balaram Chandra Paul
Cursus: A practical introduction to Data Analysis and Big Data
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We weten veel meer over de hele omgeving.
John Kidd
Cursus: Spark for Developers
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De trainer heeft de klas interessant en onderhoudend gemaakt en dat helpt een hele dag met training.
Ryan Speelman
Cursus: Spark for Developers
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Ik denk dat de trainer een uitstekende manier had om humor en verhalen uit het echte leven te combineren om de onderwerpen die voor de hand liggen zeer toegankelijk te maken. Ik zou deze professor in de toekomst zeer aanbevelen.
Cursus: Spark for Developers
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Ik vond de interactieve manier van leren erg leuk.
Luigi Loiacono
Cursus: Data Analysis with Hive/HiveQL
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Het was een heel praktische training, ik vond de praktische oefeningen leuk.
Proximus
Cursus: Data Analysis with Hive/HiveQL
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Ik had baat bij het goede overzicht, de goede balans tussen theorie en oefeningen.
Proximus
Cursus: Data Analysis with Hive/HiveQL
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Ik heb genoten van de dynamische interactie en het onderwerp "hands-on", dankzij de virtuele machine, zeer stimulerend !.
Philippe Job
Cursus: Data Analysis with Hive/HiveQL
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Ernesto heeft uitstekend werk verricht door de concepten op hoog niveau van het gebruik van Spark en de verschillende modules ervan uit te leggen.
Michael Nemerouf
Cursus: Spark for Developers
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Ik had baat bij de competentie en kennis van de trainer.
Jonathan Puvilland
Cursus: Data Analysis with Hive/HiveQL
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Over het algemeen had ik baat bij de presentatie van technologieën.
Continental AG / Abteilung: CF IT Finance
Cursus: A practical introduction to Data Analysis and Big Data
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Over het algemeen was de inhoud goed.
Sameer Rohadia
Cursus: A practical introduction to Data Analysis and Big Data
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Voorbeeldoefeningen; Praktische werkervaring delen
澳新银行
Cursus: Data Vault: Building a Scalable Data Warehouse
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Cube en DV
Alan Xie
Cursus: Data Vault: Building a Scalable Data Warehouse
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De kennis van de leraar van het datawarehouse is uitgebreid en hij prijst het!
澳新银行
Cursus: Data Vault: Building a Scalable Data Warehouse
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De leraar legde in detail uit en besprak de sfeer
澳新银行
Cursus: Data Vault: Building a Scalable Data Warehouse
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Michael de trainer is zeer deskundig en bekwaam met betrekking tot het onderwerp Big Data en R. Hij is zeer flexibel en past de training aan op de behoefte van de klant. Hij is ook zeer capabel om technische en probleemproblemen onderweg op te lossen. Fantastische en professionele training !.
Xiaoyuan Geng - Ottawa Research and Development Center, Science Technology Branch, Agriculture and Agri-Food Canada
Cursus: Programming with Big Data in R
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Ik heb echt genoten van de introductie van nieuwe pakketten.
Ottawa Research and Development Center, Science Technology Branch, Agriculture and Agri-Food Canada
Cursus: Programming with Big Data in R
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Ik denk dat de trainer een uitstekende manier had om humor en verhalen uit het echte leven te combineren om de onderwerpen die voor de hand liggen zeer toegankelijk te maken. Ik zou deze professor in de toekomst zeer aanbevelen.
Cursus: Spark for Developers
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Big Data Cursusoverzicht
Course Name
Duration
Overview
Course Name
Duration
Overview
35 hours
Overview
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.
35 hours
Overview
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 hours
Overview
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 hours
Overview
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 hours
Overview
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 hours
Overview
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 hours
Overview
The course is part of the Data Scientist skill set (Domain: Data and Technology).
35 hours
Overview
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 hours
Overview
Participants who complete this instructor-led, live training in Nederland 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 hours
Overview
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 hours
Overview
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 hours
Overview
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 hours
Overview
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 hours
Overview
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.
14 hours
Overview
This instructor-led, live training (online or onsite) is aimed at software developers, managers, and business analyst who wish to use big data systems to store and retrieve large amounts of data.
By the end of this training, participants will be able to:
- Query large amounts of data efficiently.
- Understand how Big Data system store and retrieve data
- Use the latest big data systems available
- Wrangle data from data systems into reporting systems
- Learn to write SQL queries in:
- MySQL
- Postgres
- Hive Query Language (HiveQL/HQL)
- Redshift
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
7 hours
Overview
This instructor-led, live training in Nederland (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 hours
Overview
This instructor-led, live training in Nederland (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 hours
Overview
This instructor-led, live training in Nederland (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 hours
Overview
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 hours
Overview
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 hours
Overview
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 hours
Overview
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 hours
Overview
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 hours
Overview
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 hours
Overview
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 hours
Overview
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 hours
Overview
Hadoop is the most popular Big Data processing framework.
14 hours
Overview
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 hours
Overview
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 hours
Overview
As more and more software and IT projects migrate from local processing and data management to distributed processing and big data storage, Project Managers are finding the need to upgrade their knowledge and skills to grasp the concepts and practices relevant to Big Data projects and opportunities.
This course introduces Project Managers to the most popular Big Data processing framework: Hadoop.
In this instructor-led training, participants will learn the core components of the Hadoop ecosystem and how these technologies can be used to solve large-scale problems. In learning these foundations, participants will also 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
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
This course introduces Project Managers to the most popular Big Data processing framework: Hadoop.
In this instructor-led training, participants will learn the core components of the Hadoop ecosystem and how these technologies can be used to solve large-scale problems. In learning these foundations, participants will also 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
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
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