Lokální instruktorem vedené Big Data školení České republice.
Reference
The trainer was passionate and well-known what he said I appreciate his help and answers all our questions and suggested cases.
Kurz: A Practical Introduction to Stream Processing
the good energy of the trainer, he explained everything clearly and precisely
MicroStrategy Poland Sp. z o.o.
Kurz: Teradata Fundamentals
Materials (code, presentation) were good
MicroStrategy Poland Sp. z o.o.
Kurz: Teradata Fundamentals
Nice training, full of interesting topics. After each topic helpful examples were provided.
Pawel Wojcikowski - MicroStrategy Poland Sp. z o.o.
Kurz: Teradata Fundamentals
Líbí se mi komplexní přístup k vyučovanému předmětu. Kurz byl strukturován, témata byla připravena a prezentována v dobrém pořádku, což nám pomohlo pochopit postupnou logiku toho, jak jednotlivé aspekty Teradata fungují. Obzvláště se mi líbí, že na téma indexů byl kladen velký dopad. Pablo je velmi sympatický člověk a dobrý učitel. Čas strávený na tomto školení byl rozhodně dobře strávený.
MicroStrategy Poland Sp. z o.o.
Kurz: Teradata Fundamentals
Machine Translated
interaction
Maastricht University | UMIO
Kurz: Teradata Fundamentals
Discussing many examples.
Emily Chesworth - Maastricht University | UMIO
Kurz: Teradata Fundamentals
exercises & subquery
Maastricht University | UMIO
Kurz: Teradata Fundamentals
I liked the depth of the training. I was able to understand most of it and definitely learned from this training
Maastricht University | UMIO
Kurz: Teradata Fundamentals
passionate trainer
Sylvia Baniak - Maastricht University | UMIO
Kurz: Teradata Fundamentals
x
Maastricht University | UMIO
Kurz: Teradata Fundamentals
New learned skills
Maastricht University | UMIO
Kurz: Teradata Fundamentals
The enthusiasm of the trainer
Maastricht University | UMIO
Kurz: Teradata Fundamentals
Vysvětlete krok, který je třeba učinit při pokusu o zodpovězení otázky pomocí SQL
Maastricht University | UMIO
Kurz: Teradata Fundamentals
Machine Translated
The Topic
Accenture Inc.
Kurz: Data Vault: Building a Scalable Data Warehouse
Content was good
Northrop Grumman
Kurz: Apache Accumulo Fundamentals
Instructor very knowledgeable and very happy to stop and explain stuff to the group or to an individual.
Paul Anstee - Northrop Grumman
Kurz: Apache Accumulo Fundamentals
the follow along style instead of just sitting and listening.
Jack Gallacher - Northrop Grumman
Kurz: Apache Accumulo Fundamentals
The ability of the trainer to break down complex topics to simple topics.
Northrop Grumman
Kurz: Apache Accumulo Fundamentals
Fulvio took time and attention from the outset to check what delegates wanted most out of their training days and in particular I liked that he focused quite a lot of attention to working with the Shell e.g. scan, grep, egrep commands – particularly filtering over a system time period e.g. last hour. Fulvio's style and pace can be quite quick at times which mostly suited me as there were a lot of topics covered especially in the last 2 days. I realise that the class was a bit of a mixed audience with varying technical skills, so it was good to see that he was able to allow others to catchup and to help them fix their problems whenever encountered. Nothing seemed to be too much trouble for Fulvio and enjoyed this course a lot.
Northrop Grumman
Kurz: Apache Accumulo Fundamentals
Velmi dobře přenesené znalosti učitele, žádné nezodpovězené otázky.
Karolin Papaj - Mowi Poland SA
Kurz: Data Mining and Analysis
Machine Translated
Covered a lot in a short space of time. Gained a good overall knowledge of Accumulo and knowledge gained will be useful for other NoSQL databases.
Lauren Rees - Northrop Grumman
Kurz: Apache Accumulo Fundamentals
Opravdu jsem měl rád pracovní cvičení s klastrem, abych viděl výkon uzlů v klastru a rozšířenou funkčnost.
CACI Ltd
Kurz: Apache NiFi for Developers
Machine Translated
The trainers in depth knowledge of the subject
CACI Ltd
Kurz: Apache NiFi for Administrators
Ajay byl velmi zkušený konzultant a byl schopen odpovědět na všechny naše otázky a dokonce navrhl osvědčené postupy pro projekt, na kterém se momentálně zabýváme.
CACI Ltd
Kurz: Apache NiFi for Administrators
Machine Translated
That I had it in the first place.
Peter Scales - CACI Ltd
Kurz: Apache NiFi for Developers
content & detail to the point
Sirat Khwaja - Northrop Grumman
Kurz: Apache Accumulo Fundamentals
Příklady kódu navazující na praktická cvičení.
Northrop Grumman
Kurz: Apache Accumulo Fundamentals
Machine Translated
Učitel přizpůsobil vzdělávací program našim současným potřebám.
EduBroker Sp. z o.o.
Kurz: Python and Spark for Big Data (PySpark)
Machine Translated
The NIFI workflow excercises
Politiets Sikkerhetstjeneste
Kurz: Apache NiFi for Administrators
New knowledge about SOAP and Rest API.
Total Access Communication Public Company Limited
Kurz: Talend Open Studio for Data Integration
It Good for many exercise.
Gun Thepinjai - Total Access Communication Public Company Limited
Kurz: Talend Open Studio for Data Integration
answers to our specific questions
MOD BELGIUM
Kurz: Apache NiFi for Administrators
every question we had, he explained it with great details. i also liked the facts that Fulvio gets excited when there is an interaction.
Solvinity B.V.
Kurz: SolrCloud
široký aspekt Teradata knolwedge - zahájení základního monitorování architektury dokončení
MicroStrategy Poland Sp. z o.o.
Kurz: Teradata Fundamentals
Machine Translated
Provádění podobných cvičení různými způsoby skutečně pomáhá pochopit, co každá složka ( Hadoop / Spark, samostatný / klastr) může udělat samostatně a společně. Dalo mi nápady, jak bych měl vyzkoušet svou aplikaci na svém místním počítači, když se vyvíjím, vs, když je nasazen v clusteru.
Thomas Carcaud - IT Frankfurt GmbH
Kurz: Spark for Developers
Machine Translated
The lessons were taught in a Jupyter notebook. The topics were structured with a logical sequence and naturally helped develop the session from the easier parts to the more complex. I'm already an advanced user of Python with background in Machine Learning, so found the course easier to follow than, possibly, some of my classmates that took the training course. I appreciate that some of the most elementary concepts were skipped and that he focused on the most substantial matters.
Angela DeLaMora - ADT, LLC
Kurz: Python and Spark for Big Data (PySpark)
Intensity, Training materials and expertise, Clarity, Excellent communication with Alessandra
Marija Hornis Dmitrovic - Marija Hornis
Kurz: Data Science for Big Data Analytics
NA
DBS
Kurz: Python and Spark for Big Data (PySpark)
R programming
Osden Jokonya - University of the Western Cape
Kurz: A Practical Introduction to Data Analysis and Big Data
Practical exercises
JOEL CHIGADA - University of the Western Cape
Kurz: A Practical Introduction to Data Analysis and Big Data
Znalost instruktora předmětu
Juan Francisco Trejo Escoto
Kurz: Advanced Teradata
Machine Translated
Exercises.
David Lehotak - NVision Czech Republic ICT a.s.
Kurz: Apache Ignite for Developers
Very useful in because it helps me understand what we can do with the data in our context. It will also help me
Nicolas NEMORIN - Adecco Groupe France
Kurz: KNIME Analytics Platform for BI
He had a very good manner,
Premier Partnership
Kurz: IoT Fundamentals and Frontiers : For Managers, CXO, VP, Investors and Entrepreneurs
The trainer was passionate and well-known what he said I appreciate his help and answers all our questions and suggested cases.
Kurz: A Practical Introduction to Stream Processing
Big Data Návrh Školení
Název školení
Doba trvání
Přehled
Název školení
Doba trvání
Přehled
35 hodin
Přehled
Toto školení vedené instruktory (na místě nebo na dálku) je zaměřeno na profesionály, kteří chtějí zahájit kariéru v oboru Data Science .
Na konci tohoto školení budou účastníci schopni:
- Nainstalujte a nakonfigurujte Python a MySql.
- Pochopte, co je to Data Science a jak může přinést hodnotu prakticky jakémukoli podnikání.
- Naučte se základy kódování v Python
- Naučte se techniky Machine Learning pod dohledem a bez dozoru a jak je implementovat a interpretovat výsledky.
Formát kurzu
- Interaktivní přednáška a diskuse.
- Spousta cvičení a cvičení.
- Praktická implementace v prostředí živé laboratoře.
Možnosti přizpůsobení kurzu
- Chcete-li požádat o přizpůsobené školení pro tento kurz, kontaktujte nás a domluvte se.
Na konci tohoto školení budou účastníci schopni:
- Nainstalujte a nakonfigurujte Python a MySql.
- Pochopte, co je to Data Science a jak může přinést hodnotu prakticky jakémukoli podnikání.
- Naučte se základy kódování v Python
- Naučte se techniky Machine Learning pod dohledem a bez dozoru a jak je implementovat a interpretovat výsledky.
Formát kurzu
- Interaktivní přednáška a diskuse.
- Spousta cvičení a cvičení.
- Praktická implementace v prostředí živé laboratoře.
Možnosti přizpůsobení kurzu
- Chcete-li požádat o přizpůsobené školení pro tento kurz, kontaktujte nás a domluvte se.
21 hodin
Přehled
Apache Accumulo is a sorted, distributed key/value store that provides robust, scalable data storage and retrieval. It is based on the design of Google's BigTable and is powered by Apache Hadoop, Apache Zookeeper, and Apache Thrift.
This instructor-led, live courses covers the working principles behind Accumulo and walks participants through the development of a sample application on Apache Accumulo.
Format of the Course
- Part lecture, part discussion, hands-on development and implementation, occasional tests to gauge understanding
This instructor-led, live courses covers the working principles behind Accumulo and walks participants through the development of a sample application on Apache Accumulo.
Format of the Course
- Part lecture, part discussion, hands-on development and implementation, occasional tests to gauge understanding
21 hodin
Přehled
Knowledge discovery in databases (KDD) is the process of discovering useful knowledge from a collection of data. Real-life applications for this data mining technique include marketing, fraud detection, telecommunication and manufacturing.
In this instructor-led, live course, we introduce the processes involved in KDD and carry out a series of exercises to practice the implementation of those processes.
Audience
- Data analysts or anyone interested in learning how to interpret data to solve problems
Format of the Course
- After a theoretical discussion of KDD, the instructor will present real-life cases which call for the application of KDD to solve a problem. Participants will prepare, select and cleanse sample data sets and use their prior knowledge about the data to propose solutions based on the results of their observations.
In this instructor-led, live course, we introduce the processes involved in KDD and carry out a series of exercises to practice the implementation of those processes.
Audience
- Data analysts or anyone interested in learning how to interpret data to solve problems
Format of the Course
- After a theoretical discussion of KDD, the instructor will present real-life cases which call for the application of KDD to solve a problem. Participants will prepare, select and cleanse sample data sets and use their prior knowledge about the data to propose solutions based on the results of their observations.
28 hodin
Přehled
MemSQL is an in-memory, distributed, SQL database management system for cloud and on-premises. It's a real-time data warehouse that immediately delivers insights from live and historical data.
In this instructor-led, live training, participants will learn the essentials of MemSQL for development and administration.
By the end of this training, participants will be able to:
- Understand the key concepts and characteristics of MemSQL
- Install, design, maintain, and operate MemSQL
- Optimize schemas in MemSQL
- Improve queries in MemSQL
- Benchmark performance in MemSQL
- Build real-time data applications using MemSQL
Audience
- Developers
- Administrators
- Operation Engineers
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
In this instructor-led, live training, participants will learn the essentials of MemSQL for development and administration.
By the end of this training, participants will be able to:
- Understand the key concepts and characteristics of MemSQL
- Install, design, maintain, and operate MemSQL
- Optimize schemas in MemSQL
- Improve queries in MemSQL
- Benchmark performance in MemSQL
- Build real-time data applications using MemSQL
Audience
- Developers
- Administrators
- Operation Engineers
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
21 hodin
Přehled
Predictive analytics is the process of using data analytics to make predictions about the future. This process uses data along with data mining, statistics, and machine learning techniques to create a predictive model for forecasting future events.
In this instructor-led, live training, participants will learn how to use Matlab to build predictive models and apply them to large sample data sets to predict future events based on the data.
By the end of this training, participants will be able to:
- Create predictive models to analyze patterns in historical and transactional data
- Use predictive modeling to identify risks and opportunities
- Build mathematical models that capture important trends
- Use data from devices and business systems to reduce waste, save time, or cut costs
Audience
- Developers
- Engineers
- Domain experts
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
In this instructor-led, live training, participants will learn how to use Matlab to build predictive models and apply them to large sample data sets to predict future events based on the data.
By the end of this training, participants will be able to:
- Create predictive models to analyze patterns in historical and transactional data
- Use predictive modeling to identify risks and opportunities
- Build mathematical models that capture important trends
- Use data from devices and business systems to reduce waste, save time, or cut costs
Audience
- Developers
- Engineers
- Domain experts
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
21 hodin
Přehled
MATLAB is a numerical computing environment and programming language developed by MathWorks.
7 hodin
Přehled
In this instructor-led, live training, participants will learn the core concepts behind MapR Stream Architecture as they develop a real-time streaming application.
By the end of this training, participants will be able to build producer and consumer applications for real-time stream data procesing.
Audience
- Developers
- Administrators
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
Note
- To request a customized training for this course, please contact us to arrange.
By the end of this training, participants will be able to build producer and consumer applications for real-time stream data procesing.
Audience
- Developers
- Administrators
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
Note
- To request a customized training for this course, please contact us to arrange.
14 hodin
Přehled
Magellan is an open-source distributed execution engine for geospatial analytics on big data. Implemented on top of Apache Spark, it extends Spark SQL and provides a relational abstraction for geospatial analytics.
This instructor-led, live training introduces the concepts and approaches for implementing geospacial analytics and walks participants through the creation of a predictive analysis application using Magellan on Spark.
By the end of this training, participants will be able to:
- Efficiently query, parse and join geospatial datasets at scale
- Implement geospatial data in business intelligence and predictive analytics applications
- Use spatial context to extend the capabilities of mobile devices, sensors, logs, and wearables
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.
This instructor-led, live training introduces the concepts and approaches for implementing geospacial analytics and walks participants through the creation of a predictive analysis application using Magellan on Spark.
By the end of this training, participants will be able to:
- Efficiently query, parse and join geospatial datasets at scale
- Implement geospatial data in business intelligence and predictive analytics applications
- Use spatial context to extend the capabilities of mobile devices, sensors, logs, and wearables
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.
14 hodin
Přehled
Apache Kylin is an extreme, distributed analytics engine for big data.
In this instructor-led live training, participants will learn how to use Apache Kylin to set up a real-time data warehouse.
By the end of this training, participants will be able to:
- Consume real-time streaming data using Kylin
- Utilize Apache Kylin's powerful features, rich SQL interface, spark cubing and subsecond query latency
Note
- We use the latest version of Kylin (as of this writing, Apache Kylin v2.0)
Audience
- Big data engineers
- Big Data analysts
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
In this instructor-led live training, participants will learn how to use Apache Kylin to set up a real-time data warehouse.
By the end of this training, participants will be able to:
- Consume real-time streaming data using Kylin
- Utilize Apache Kylin's powerful features, rich SQL interface, spark cubing and subsecond query latency
Note
- We use the latest version of Kylin (as of this writing, Apache Kylin v2.0)
Audience
- Big data engineers
- Big Data analysts
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
7 hodin
Přehled
Confluent KSQL is a stream processing framework built on top of Apache Kafka. It enables real-time data processing using SQL operations.
This instructor-led, live training (online or onsite) is aimed at developers who wish to implement Apache Kafka stream processing without writing code.
By the end of this training, participants will be able to:
- Install and configure Confluent KSQL.
- Set up a stream processing pipeline using only SQL commands (no Java or Python coding).
- Carry out data filtering, transformations, aggregations, joins, windowing, and sessionization entirely in SQL.
- Design and deploy interactive, continuous queries for streaming ETL and real-time analytics.
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.
This instructor-led, live training (online or onsite) is aimed at developers who wish to implement Apache Kafka stream processing without writing code.
By the end of this training, participants will be able to:
- Install and configure Confluent KSQL.
- Set up a stream processing pipeline using only SQL commands (no Java or Python coding).
- Carry out data filtering, transformations, aggregations, joins, windowing, and sessionization entirely in SQL.
- Design and deploy interactive, continuous queries for streaming ETL and real-time analytics.
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.
35 hodin
Přehled
KNIME is a free and open-source data analytics, reporting and integration platform. KNIME integrates various components for machine learning and data mining through its modular data pipelining concept. A graphical user interface and use of JDBC allows assembly of nodes blending different data sources, including preprocessing (ETL: Extraction, Transformation, Loading), for modeling, data analysis and visualization without, or with only minimal, programming. To some extent as advanced analytics tool KNIME can be considered as a SAS alternative.
Since 2006, KNIME has been used in pharmaceutical research, it also used in other areas like CRM customer data analysis, business intelligence and financial data analysis.
Since 2006, KNIME has been used in pharmaceutical research, it also used in other areas like CRM customer data analysis, business intelligence and financial data analysis.
21 hodin
Přehled
KNIME Analytics Platform is a leading open source option for data-driven innovation, helping you discover the potential hidden in your data, mine for fresh insights, or predict new futures. With more than 1000 modules, hundreds of ready-to-run examples, a comprehensive range of integrated tools, and the widest choice of advanced algorithms available, KNIME Analytics Platform is the perfect toolbox for any data scientist and business analyst.
This course for KNIME Analytics Platform is an ideal opportunity for beginners, advanced users and KNIME experts to be introduced to KNIME, to learn how to use it more effectively, and how to create clear, comprehensive reports based on KNIME workflows
This course for KNIME Analytics Platform is an ideal opportunity for beginners, advanced users and KNIME experts to be introduced to KNIME, to learn how to use it more effectively, and how to create clear, comprehensive reports based on KNIME workflows
21 hodin
Přehled
Unlike other technologies, IoT is far more complex encompassing almost every branch of core Engineering-Mechanical, Electronics, Firmware, Middleware, Cloud, Analytics and Mobile. For each of its engineering layers, there are aspects of economics, standards, regulations and evolving state of the art. This is for the firs time, a modest course is offered to cover all of these critical aspects of IoT Engineering.
Summary
-
An advanced training program covering the current state of the art in Internet of Things
-
Cuts across multiple technology domains to develop awareness of an IoT system and its components and how it can help businesses and organizations.
-
Live demo of model IoT applications to showcase practical IoT deployments across different industry domains, such as Industrial IoT, Smart Cities, Retail, Travel & Transportation and use cases around connected devices & things
Target Audience
-
Managers responsible for business and operational processes within their respective organizations and want to know how to harness IoT to make their systems and processes more efficient.
-
Entrepreneurs and Investors who are looking to build new ventures and want to develop a better understanding of the IoT technology landscape to see how they can leverage it in an effective manner.
Estimates for Internet of Things or IoT market value are massive, since by definition the IoT is an integrated and diffused layer of devices, sensors, and computing power that overlays entire consumer, business-to-business, and government industries. The IoT will account for an increasingly huge number of connections: 1.9 billion devices today, and 9 billion by 2018. That year, it will be roughly equal to the number of smartphones, smart TVs, tablets, wearable computers, and PCs combined.
In the consumer space, many products and services have already crossed over into the IoT, including kitchen and home appliances, parking, RFID, lighting and heating products, and a number of applications in Industrial Internet.
However, the underlying technologies of IoT are nothing new as M2M communication existed since the birth of Internet. However what changed in last couple of years is the emergence of number of inexpensive wireless technologies added by overwhelming adaptation of smart phones and Tablet in every home. Explosive growth of mobile devices led to present demand of IoT.
Due to unbounded opportunities in IoT business, a large number of small and medium sized entrepreneurs jumped on a bandwagon of IoT gold rush. Also due to emergence of open source electronics and IoT platform, cost of development of IoT system and further managing its sizable production is increasingly affordable. Existing electronic product owners are experiencing pressure to integrate their device with Internet or Mobile app.
This training is intended for a technology and business review of an emerging industry so that IoT enthusiasts/entrepreneurs can grasp the basics of IoT technology and business.
Course Objective
Main objective of the course is to introduce emerging technological options, platforms and case studies of IoT implementation in home & city automation (smart homes and cities), Industrial Internet, healthcare, Govt., Mobile Cellular and other areas.
-
Basic introduction of all the elements of IoT-Mechanical, Electronics/sensor platform, Wireless and wireline protocols, Mobile to Electronics integration, Mobile to enterprise integration, Data-analytics and Total control plane
-
M2M Wireless protocols for IoT- WiFi, Zigbee/Zwave, Bluetooth, ANT+ : When and where to use which one?
-
Mobile/Desktop/Web app- for registration, data acquisition and control –Available M2M data acquisition platform for IoT-–Xively, Omega and NovoTech, etc.
-
Security issues and security solutions for IoT
-
Open source/commercial electronics platform for IoT-Raspberry Pi, Arduino , ArmMbedLPC etc
-
Open source /commercial enterprise cloud platform for AWS-IoT apps, Azure -IOT, Watson-IOT cloud in addition to other minor IoT clouds
-
Studies of business and technology of some of the common IoT devices like Home automation, Smoke alarm, vehicles, military, home health etc.
Summary
-
An advanced training program covering the current state of the art in Internet of Things
-
Cuts across multiple technology domains to develop awareness of an IoT system and its components and how it can help businesses and organizations.
-
Live demo of model IoT applications to showcase practical IoT deployments across different industry domains, such as Industrial IoT, Smart Cities, Retail, Travel & Transportation and use cases around connected devices & things
Target Audience
-
Managers responsible for business and operational processes within their respective organizations and want to know how to harness IoT to make their systems and processes more efficient.
-
Entrepreneurs and Investors who are looking to build new ventures and want to develop a better understanding of the IoT technology landscape to see how they can leverage it in an effective manner.
Estimates for Internet of Things or IoT market value are massive, since by definition the IoT is an integrated and diffused layer of devices, sensors, and computing power that overlays entire consumer, business-to-business, and government industries. The IoT will account for an increasingly huge number of connections: 1.9 billion devices today, and 9 billion by 2018. That year, it will be roughly equal to the number of smartphones, smart TVs, tablets, wearable computers, and PCs combined.
In the consumer space, many products and services have already crossed over into the IoT, including kitchen and home appliances, parking, RFID, lighting and heating products, and a number of applications in Industrial Internet.
However, the underlying technologies of IoT are nothing new as M2M communication existed since the birth of Internet. However what changed in last couple of years is the emergence of number of inexpensive wireless technologies added by overwhelming adaptation of smart phones and Tablet in every home. Explosive growth of mobile devices led to present demand of IoT.
Due to unbounded opportunities in IoT business, a large number of small and medium sized entrepreneurs jumped on a bandwagon of IoT gold rush. Also due to emergence of open source electronics and IoT platform, cost of development of IoT system and further managing its sizable production is increasingly affordable. Existing electronic product owners are experiencing pressure to integrate their device with Internet or Mobile app.
This training is intended for a technology and business review of an emerging industry so that IoT enthusiasts/entrepreneurs can grasp the basics of IoT technology and business.
Course Objective
Main objective of the course is to introduce emerging technological options, platforms and case studies of IoT implementation in home & city automation (smart homes and cities), Industrial Internet, healthcare, Govt., Mobile Cellular and other areas.
-
Basic introduction of all the elements of IoT-Mechanical, Electronics/sensor platform, Wireless and wireline protocols, Mobile to Electronics integration, Mobile to enterprise integration, Data-analytics and Total control plane
-
M2M Wireless protocols for IoT- WiFi, Zigbee/Zwave, Bluetooth, ANT+ : When and where to use which one?
-
Mobile/Desktop/Web app- for registration, data acquisition and control –Available M2M data acquisition platform for IoT-–Xively, Omega and NovoTech, etc.
-
Security issues and security solutions for IoT
-
Open source/commercial electronics platform for IoT-Raspberry Pi, Arduino , ArmMbedLPC etc
-
Open source /commercial enterprise cloud platform for AWS-IoT apps, Azure -IOT, Watson-IOT cloud in addition to other minor IoT clouds
-
Studies of business and technology of some of the common IoT devices like Home automation, Smoke alarm, vehicles, military, home health etc.
7 hodin
Přehled
Kafka Streams is a client-side library for building applications and microservices whose data is passed to and from a Kafka messaging system. Traditionally, Apache Kafka has relied on Apache Spark or Apache Storm to process data between message producers and consumers. By calling the Kafka Streams API from within an application, data can be processed directly within Kafka, bypassing the need for sending the data to a separate cluster for processing.
In this instructor-led, live training, participants will learn how to integrate Kafka Streams into a set of sample Java applications that pass data to and from Apache Kafka for stream processing.
By the end of this training, participants will be able to:
- Understand Kafka Streams features and advantages over other stream processing frameworks
- Process stream data directly within a Kafka cluster
- Write a Java or Scala application or microservice that integrates with Kafka and Kafka Streams
- Write concise code that transforms input Kafka topics into output Kafka topics
- Build, package and deploy the application
Audience
- Developers
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
Notes
- To request a customized training for this course, please contact us to arrange
In this instructor-led, live training, participants will learn how to integrate Kafka Streams into a set of sample Java applications that pass data to and from Apache Kafka for stream processing.
By the end of this training, participants will be able to:
- Understand Kafka Streams features and advantages over other stream processing frameworks
- Process stream data directly within a Kafka cluster
- Write a Java or Scala application or microservice that integrates with Kafka and Kafka Streams
- Write concise code that transforms input Kafka topics into output Kafka topics
- Build, package and deploy the application
Audience
- Developers
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
Notes
- To request a customized training for this course, please contact us to arrange
28 hodin
Přehled
MonetDB is an open-source database that pioneered the column-store technology approach.
In this instructor-led, live training, participants will learn how to use MonetDB and how to get the most value out of it.
By the end of this training, participants will be able to:
- Understand MonetDB and its features
- Install and get started with MonetDB
- Explore and perform different functions and tasks in MonetDB
- Accelerate the delivery of their project by maximizing MonetDB capabilities
Audience
- Developers
- Technical experts
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
In this instructor-led, live training, participants will learn how to use MonetDB and how to get the most value out of it.
By the end of this training, participants will be able to:
- Understand MonetDB and its features
- Install and get started with MonetDB
- Explore and perform different functions and tasks in MonetDB
- Accelerate the delivery of their project by maximizing MonetDB capabilities
Audience
- Developers
- Technical experts
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
21 hodin
Přehled
Stream Processing refers to the real-time processing of "data in motion", that is, performing computations on data as it is being received. Such data is read as continuous streams from data sources such as sensor events, website user activity, financial trades, credit card swipes, click streams, etc. Stream Processing frameworks are able to read large volumes of incoming data and provide valuable insights almost instantaneously.
In this instructor-led, live training (onsite or remote), participants will learn how to set up and integrate different Stream Processing frameworks with existing big data storage systems and related software applications and microservices.
By the end of this training, participants will be able to:
- Install and configure different Stream Processing frameworks, such as Spark Streaming and Kafka Streaming.
- Understand and select the most appropriate framework for the job.
- Process of data continuously, concurrently, and in a record-by-record fashion.
- Integrate Stream Processing solutions with existing databases, data warehouses, data lakes, etc.
- Integrate the most appropriate stream processing library with enterprise applications and microservices.
Audience
- Developers
- Software architects
Format of the Course
- Part lecture, part discussion, exercises and heavy hands-on practice
Notes
- To request a customized training for this course, please contact us to arrange.
In this instructor-led, live training (onsite or remote), participants will learn how to set up and integrate different Stream Processing frameworks with existing big data storage systems and related software applications and microservices.
By the end of this training, participants will be able to:
- Install and configure different Stream Processing frameworks, such as Spark Streaming and Kafka Streaming.
- Understand and select the most appropriate framework for the job.
- Process of data continuously, concurrently, and in a record-by-record fashion.
- Integrate Stream Processing solutions with existing databases, data warehouses, data lakes, etc.
- Integrate the most appropriate stream processing library with enterprise applications and microservices.
Audience
- Developers
- Software architects
Format of the Course
- Part lecture, part discussion, exercises and heavy hands-on practice
Notes
- To request a customized training for this course, please contact us to arrange.
14 hodin
Přehled
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 hodin
Přehled
Cloudera Impala is an open source massively parallel processing (MPP) SQL query engine for Apache Hadoop clusters.
Impala enables users to issue low-latency SQL queries to data stored in Hadoop Distributed File System and Apache Hbase without requiring data movement or transformation.
Audience
This course is aimed at analysts and data scientists performing analysis on data stored in Hadoop via Business Intelligence or SQL tools.
After this course delegates will be able to
- Extract meaningful information from Hadoop clusters with Impala.
- Write specific programs to facilitate Business Intelligence in Impala SQL Dialect.
- Troubleshoot Impala.
Impala enables users to issue low-latency SQL queries to data stored in Hadoop Distributed File System and Apache Hbase without requiring data movement or transformation.
Audience
This course is aimed at analysts and data scientists performing analysis on data stored in Hadoop via Business Intelligence or SQL tools.
After this course delegates will be able to
- Extract meaningful information from Hadoop clusters with Impala.
- Write specific programs to facilitate Business Intelligence in Impala SQL Dialect.
- Troubleshoot Impala.
7 hodin
Přehled
This course covers how to use Hive SQL language (AKA: Hive HQL, SQL on Hive, HiveQL) for people who extract data from Hive
21 hodin
Přehled
Hortonworks Data Platform (HDP) is an open-source Apache Hadoop support platform that provides a stable foundation for developing big data solutions on the Apache Hadoop ecosystem.
This instructor-led, live training (online or onsite) introduces Hortonworks Data Platform (HDP) and walks participants through the deployment of Spark + Hadoop solution.
By the end of this training, participants will be able to:
- Use Hortonworks to reliably run Hadoop at a large scale.
- Unify Hadoop's security, governance, and operations capabilities with Spark's agile analytic workflows.
- Use Hortonworks to investigate, validate, certify and support each of the components in a Spark project.
- Process different types of data, including structured, unstructured, in-motion, and at-rest.
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.
This instructor-led, live training (online or onsite) introduces Hortonworks Data Platform (HDP) and walks participants through the deployment of Spark + Hadoop solution.
By the end of this training, participants will be able to:
- Use Hortonworks to reliably run Hadoop at a large scale.
- Unify Hadoop's security, governance, and operations capabilities with Spark's agile analytic workflows.
- Use Hortonworks to investigate, validate, certify and support each of the components in a Spark project.
- Process different types of data, including structured, unstructured, in-motion, and at-rest.
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.
21 hodin
Přehled
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
28 hodin
Přehled
Hadoop is a popular Big Data processing framework. Python is a high-level programming language famous for its clear syntax and code readibility.
In this instructor-led, live training, participants will learn how to work with Hadoop, MapReduce, Pig, and Spark using Python as they step through multiple examples and use cases.
By the end of this training, participants will be able to:
- Understand the basic concepts behind Hadoop, MapReduce, Pig, and Spark
- Use Python with Hadoop Distributed File System (HDFS), MapReduce, Pig, and Spark
- Use Snakebite to programmatically access HDFS within Python
- Use mrjob to write MapReduce jobs in Python
- Write Spark programs with Python
- Extend the functionality of pig using Python UDFs
- Manage MapReduce jobs and Pig scripts using Luigi
Audience
- Developers
- IT Professionals
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
In this instructor-led, live training, participants will learn how to work with Hadoop, MapReduce, Pig, and Spark using Python as they step through multiple examples and use cases.
By the end of this training, participants will be able to:
- Understand the basic concepts behind Hadoop, MapReduce, Pig, and Spark
- Use Python with Hadoop Distributed File System (HDFS), MapReduce, Pig, and Spark
- Use Snakebite to programmatically access HDFS within Python
- Use mrjob to write MapReduce jobs in Python
- Write Spark programs with Python
- Extend the functionality of pig using Python UDFs
- Manage MapReduce jobs and Pig scripts using Luigi
Audience
- Developers
- IT Professionals
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
28 hodin
Přehled
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.
14 hodin
Přehled
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
7 hodin
Přehled
This instructor-led, live training (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.
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.
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.
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.
21 hodin
Přehled
Apache NiFi (Hortonworks DataFlow) is a real-time integrated data logistics and simple event processing platform that enables the moving, tracking and automation of data between systems. It is written using flow-based programming and provides a web-based user interface to manage dataflows in real time.
In this instructor-led, live training (onsite or remote), participants will learn how to deploy and manage Apache NiFi in a live lab environment.
By the end of this training, participants will be able to:
- Install and configure Apachi NiFi.
- Source, transform and manage data from disparate, distributed data sources, including databases and big data lakes.
- Automate dataflows.
- Enable streaming analytics.
- Apply various approaches for data ingestion.
- Transform Big Data and into business insights.
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.
In this instructor-led, live training (onsite or remote), participants will learn how to deploy and manage Apache NiFi in a live lab environment.
By the end of this training, participants will be able to:
- Install and configure Apachi NiFi.
- Source, transform and manage data from disparate, distributed data sources, including databases and big data lakes.
- Automate dataflows.
- Enable streaming analytics.
- Apply various approaches for data ingestion.
- Transform Big Data and into business insights.
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.
21 hodin
Přehled
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%).
7 hodin
Přehled
Spark SQL is Apache Spark's module for working with structured and unstructured data. Spark SQL provides information about the structure of the data as well as the computation being performed. This information can be used to perform optimizations. Two common uses for Spark SQL are:
- to execute SQL queries.
- to read data from an existing Hive installation.
In this instructor-led, live training (onsite or remote), participants will learn how to analyze various types of data sets using Spark SQL.
By the end of this training, participants will be able to:
- Install and configure Spark SQL.
- Perform data analysis using Spark SQL.
- Query data sets in different formats.
- Visualize data and query results.
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.
- to execute SQL queries.
- to read data from an existing Hive installation.
In this instructor-led, live training (onsite or remote), participants will learn how to analyze various types of data sets using Spark SQL.
By the end of this training, participants will be able to:
- Install and configure Spark SQL.
- Perform data analysis using Spark SQL.
- Query data sets in different formats.
- Visualize data and query results.
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.
14 hodin
Přehled
Apache Zeppelin is a web-based notebook for capturing, exploring, visualizing and sharing Hadoop and Spark based data.
This instructor-led, live training introduces the concepts behind interactive data analytics and walks participants through the deployment and usage of Zeppelin in a single-user or multi-user environment.
By the end of this training, participants will be able to:
- Install and configure Zeppelin
- Develop, organize, execute and share data in a browser-based interface
- Visualize results without referring to the command line or cluster details
- Execute and collaborate on long workflows
- Work with any of a number of plug-in language/data-processing-backends, such as Scala (with Apache Spark), Python (with Apache Spark), Spark SQL, JDBC, Markdown and Shell.
- Integrate Zeppelin with Spark, Flink and Map Reduce
- Secure multi-user instances of Zeppelin with Apache Shiro
Audience
- Data engineers
- Data analysts
- Data scientists
- Software developers
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
This instructor-led, live training introduces the concepts behind interactive data analytics and walks participants through the deployment and usage of Zeppelin in a single-user or multi-user environment.
By the end of this training, participants will be able to:
- Install and configure Zeppelin
- Develop, organize, execute and share data in a browser-based interface
- Visualize results without referring to the command line or cluster details
- Execute and collaborate on long workflows
- Work with any of a number of plug-in language/data-processing-backends, such as Scala (with Apache Spark), Python (with Apache Spark), Spark SQL, JDBC, Markdown and Shell.
- Integrate Zeppelin with Spark, Flink and Map Reduce
- Secure multi-user instances of Zeppelin with Apache Shiro
Audience
- Data engineers
- Data analysts
- Data scientists
- Software developers
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
14 hodin
Přehled
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
Nadcházející Big Data Školení
2020-08-31 09:30:00
Praha, Pražský hrad
21 hodiny
2020-08-31 09:30:00
Praha, Pražský hrad
21 hodiny
2020-10-21 09:30:00
Praha, Pražský hrad
21 hodiny
2020-10-27 09:30:00
Praha, Pražský hrad
21 hodiny
2020-12-14 09:30:00
Praha, Pražský hrad
21 hodiny
Ostatní země
Konzultace
Víkendové Big Data kurzy, Večerní Big Data školení, Big Data přijímač, Big Data vedené školitelem, Víkendové Big Data školení, Večerní Big Data kurzy, Big Data koučování, Big Data lektor, Big Data školitel, Big Data počítačová školení, Big Data počítačové kurzy , Big Data kurzy, Big Data školení, Big Data on-site, Big Data uzavřená školení, Big Data individuální školení
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