Local, instructor-led live Stream Processing training courses demonstrate through interactive discussion and hands-on practice the fundamentals and advanced topics of Stream Processing. Stream Processing training is available as "onsite live training" or "remote live training". Onsite live training can be carried out locally on customer premises in Singapore or in NobleProg corporate training centers in Singapore. Remote live training is carried out by way of an interactive, remote desktop. NobleProg -- Your Local Training Provider
What did you like the most about the training?: Detailed on each tool.
Amber Mehrotra - NIIT Limited
Course:Hadoop Administration
The fact that all the data and software was ready to use on an already prepared VM, provided by the trainer in external disks.
vyzVoice
Course:Hadoop for Developers and Administrators
The trainer was so knowledgeable and included areas I was interested in.
Mohamed Salama
Course:Data Mining & Machine Learning with R
Very tailored to needs.
Yashan Wang
Course:Data Mining with R
Richard is very calm and methodical, with an analytic insight - exactly the qualities needed to present this sort of course.
Kieran Mac Kenna
Course:Spark for Developers
I like the exercises done.
Nour Assaf
Course:Data Mining and Analysis
The hands-on exercise and the trainer capacity to explain complex topics in simple terms.
youssef chamoun
Course:Data Mining and Analysis
The information given was interesting and the best part was towards the end when we were provided with Data from Durex and worked on Data we are familiar with and perform operations to get results.
Jessica Chaar
Course:Data Mining and Analysis
I mostly liked the trainer giving real live Examples.
Simon Hahn
Course:Administrator Training for Apache Hadoop
I genuinely enjoyed the big competences of Trainer.
Grzegorz Gorski
Course:Administrator Training for Apache Hadoop
I genuinely enjoyed the many hands-on sessions.
Jacek Pieczątka
Course:Administrator Training for Apache Hadoop
I thought that the information was interesting.
Allison May
Course:Data Visualization
I really appreciated that Jeff utilized data and examples that were applicable to education data. He made it interesting and interactive.
Carol Wells Bazzichi
Course:Data Visualization
Learning about all the chart types and what they are used for. Learning the value of cluttering. Learning about the methods to show time data.
Susan Williams
Course:Data Visualization
Trainer was enthusiastic.
Diane Lucas
Course:Data Visualization
I really liked the content / Instructor.
Craig Roberson
Course:Data Visualization
I am a hands-on learner and this was something that he did a lot of.
Lisa Comfort
Course:Data Visualization
I liked the examples.
Peter Coleman
Course:Data Visualization
I generally was benefit from the examples.
peter coleman
Course:Data Visualization
I enjoyed the good real world examples, reviews of existing reports.
Ronald Parrish
Course:Data Visualization
I enjoyed the interaction and facts gained / learn.
Monna Liza Mengullo
Course:Data Protection
I was benefit from the discussion and exchange of ideas.
RAYMOND JACKSON PAJARILLO
Course:Data Protection
I generally liked all.
Marjorie Pepito
Course:Data Protection
I really was benefit from the willingness of the trainer to share more.
Balaram Chandra Paul
Course:A practical introduction to Data Analysis and Big Data
We know a lot more about the whole environment.
John Kidd
Course:Spark for Developers
The trainer made the class interesting and entertaining which helps quite a bit with all day training.
Ryan Speelman
Course:Spark for Developers
I think the trainer had an excellent style of combining humor and real life stories to make the subjects at hand very approachable. I would highly recommend this professor in the future.
Course:Spark for Developers
Ernesto did a great job explaining the high level concepts of using Spark and its various modules.
Michael Nemerouf
Course:Spark for Developers
He was very personable and worked hard to provide a relevant experience. It was the course content that was the issue.
TECTERRA
Course:IoT (Internet of Things) for Entrepreneurs, Managers and Investors
I was benefit from some new and interesting ideas. Meeting and interacting with other attendees.
TECTERRA
Course:IoT (Internet of Things) for Entrepreneurs, Managers and Investors
It covered a broad range of information.
Continental AG / Abteilung: CF IT Finance
Course:A practical introduction to Data Analysis and Big Data
I generally was benefit from the presentation of technologies.
Continental AG / Abteilung: CF IT Finance
Course:A practical introduction to Data Analysis and Big Data
Overall the Content was good.
Sameer Rohadia
Course:A practical introduction to Data Analysis and Big Data
I generally enjoyed the Hardtop Eco system.
Adnan Rafiq
Course:Big Data Business Intelligence for Govt. Agencies
Michael the trainer is very knowledgeable and skillful about the subject of Big Data and R. He is very flexible and quickly customize the training meeting clients' need. He is also very capable to solve technical and subject matter problems on the go. Fantastic and professional training!.
Xiaoyuan Geng - Ottawa Research and Development Center, Science Technology Branch, Agriculture and Agri-Food Canada
Course:Programming with Big Data in R
I really enjoyed the introduction of new packages.
Ottawa Research and Development Center, Science Technology Branch, Agriculture and Agri-Food Canada
Course:Programming with Big Data in R
The tutor, Mr. Michael An, interacted with the audience very well, the instruction was clear. The tutor also go extent to add more information based on the requests from the students during the training.
Ottawa Research and Development Center, Science Technology Branch, Agriculture and Agri-Food Canada
Course:Programming with Big Data in R
The subject matter and the pace were perfect.
Tim - Ottawa Research and Development Center, Science Technology Branch, Agriculture and Agri-Food Canada
Course:Programming with Big Data in R
The example and training material were sufficient and made it easy to understand what you are doing.
Teboho Makenete
Course:Data Science for Big Data Analytics
It was very hands-on, we spent half the time actually doing things in Clouded/Hardtop, running different commands, checking the system, and so on. The extra materials (books, websites, etc. .) were really appreciated, we will have to continue to learn. The installations were quite fun, and very handy, the cluster setup from scratch was really good.
Ericsson
Course:Administrator Training for Apache Hadoop
Richard's training style kept it interesting, the real world examples used helped to drive the concepts home.
Jamie Martin-Royle - NBrown Group
Course:From Data to Decision with Big Data and Predictive Analytics
The content, as I found it very interesting and think it would help me in my final year at University.
Krishan Mistry - NBrown Group
Course:From Data to Decision with Big Data and Predictive Analytics
The hands-on exercises are good for us to appreciate the capability and features of Talent.
Iverson Associates Sdn Bhd
Course:Talend Open Studio for Data Integration
I mostly enjoyed the hands-on Training.
Muraly Muniandy - Iverson Associates Sdn Bhd
Course:Talend Open Studio for Data Integration
I liked the trainer's knowledge and flexibility with discussions slightly off course topic.
Iverson Associates Sdn Bhd
Course:Talend Open Studio for Data Integration
I liked the vast knowledge and experience on topic.
Iverson Associates Sdn Bhd
Course:Talend Open Studio for Data Integration
Lot of hands-on exercises.
Ericsson
Course:Administrator Training for Apache Hadoop
Ambari management tool. Ability to discuss practical Hadoop experiences from other business case than telecom.
Ericsson
Course:Administrator Training for Apache Hadoop
Applicable scenarios and cases
zhaopeng liu - Fmr
Course:Spark for Developers
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case analysis
国栋 张
Course:Spark for Developers
Translated by
all parts of this session
Eric Han - Fmr
Course:Spark for Developers
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The use of dataintegration
Waldemar Wisniewski
Course:Pentaho Business Intelligence (PBI) - moduły raportowe
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trainer's knowledge, available equipment
Mariusz Moskal
Course:Pentaho Business Intelligence (PBI) - moduły raportowe
Translated by
Examples enriched with real data and scenarios useful in everyday practice
Krzysztof Świątczak
Course:Pentaho Business Intelligence (PBI) - moduły raportowe
Translated by
Example exercises; Practical work experience sharing
澳新银行
Course:Data Vault: Building a Scalable Data Warehouse
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The teacher's knowledge of the data warehouse is comprehensive, and he praises it!
澳新银行
Course:Data Vault: Building a Scalable Data Warehouse
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The teacher explained in detail and discussed the atmosphere
澳新银行
Course:Data Vault: Building a Scalable Data Warehouse
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I liked the methodology expected by Jorge
Experian Colombia S.A
Course:Spark para Desarrolladores
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Other additional topics that were suggested were covered
German Alonso Durango Colmenares
Course:Capacitación de Administrador para Apache Hadoop
Translated by
Nothing special.
Arkadiusz Nowakowski - Uniwersytet Śląski w Katowicach
Course:Apache Spark
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| Code | Name | Duration | Overview |
|---|---|---|---|
| samza | Samza for Stream Processing | 14 hours | Apache Samza is an open-source near-realtime, asynchronous computational framework for stream processing. It uses Apache Kafka for messaging, and Apache Hadoop YARN for fault tolerance, processor isolation, security, and resource management. This instructor-led, live training introduces the principles behind messaging systems and distributed stream processing, while walking participants through the creation of a sample Samza-based project and job execution. By the end of this training, participants will be able to: - Use Samza to simplify the code needed to produce and consume messages. - Decouple the handling of messages from an application. - Use Samza to implement near-realtime asynchronous computation. - Use stream processing to provide a higher level of abstraction over messaging systems. Audience - Developers Format of the course - Part lecture, part discussion, exercises and heavy hands-on practice |
| tigon | Tigon: Real-time Streaming for the Real World | 14 hours | Tigon is an open-source, real-time, low-latency, high-throughput, native YARN, stream processing framework that sits on top of HDFS and HBase for persistence. Tigon applications address use cases such as network intrusion detection and analytics, social media market analysis, location analytics, and real-time recommendations to users. This instructor-led, live training introduces Tigon's approach to blending real-time and batch processing as it walks participants through the creation a sample application. By the end of this training, participants will be able to: - Create powerful, stream processing applications for handling large volumes of data - Process stream sources such as Twitter and Webserver Logs - Use Tigon for rapid joining, filtering, and aggregating of streams Audience - Developers Format of the course - Part lecture, part discussion, exercises and heavy hands-on practice |
| maprstreaming | Real-Time Stream Processing with MapR | 7 hours | 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. |
| introtostreamprocessing | A Practical Introduction to Stream Processing | 21 hours | 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 setup 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. - Integrating 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. |
| flink | Flink for Scalable Stream and Batch Data Processing | 28 hours | Apache Flink is an open-source framework for scalable stream and batch data processing. This instructor-led, live training introduces the principles and approaches behind distributed stream and batch data processing, and walks participants through the creation of a real-time, data streaming application. By the end of this training, participants will be able to: - Set up an environment for developing data analysis applications - Package, execute, and monitor Flink-based, fault-tolerant, data streaming applications - Manage diverse workloads - Perform advanced analytics using Flink ML - Set up a multi-node Flink cluster - Measure and optimize performance - Integrate Flink with different Big Data systems - Compare Flink capabilities with those of other big data processing frameworks Audience - Developers - Architects - Data engineers - Analytics professionals - Technical managers Format of the course - Part lecture, part discussion, exercises and heavy hands-on practice |
| nifi | Apache NiFi for Administrators | 21 hours | 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, 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 Audience - System administrators - Data engineers - Developers - DevOps Format of the course - Part lecture, part discussion, exercises and heavy hands-on practice |
| nifidev | Apache NiFi for Developers | 7 hours | 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, participants will learn the fundamentals of flow-based programming as they develop a number of demo extensions, components and processors using Apache NiFi. By the end of this training, participants will be able to: - Understand NiFi's architecture and dataflow concepts - Develop extensions using NiFi and third-party APIs - Custom develop their own Apache Nifi processor - Ingest and process real-time data from disparate and uncommon file formats and data sources Audience - Developers - Data engineers Format of the course - Part lecture, part discussion, exercises and heavy hands-on practice |
| storm | Apache Storm | 28 hours | Apache Storm is a distributed, real-time computation engine used for enabling real-time business intelligence. It does so by enabling applications to reliably process unbounded streams of data (a.k.a. stream processing). "Storm is for real-time processing what Hadoop is for batch processing!" In this instructor-led live training, participants will learn how to install and configure Apache Storm, then develop and deploy an Apache Storm application for processing big data in real-time. Some of the topics included in this training include: - Apache Storm in the context of Hadoop - Working with unbounded data - Continuous computation - Real-time analytics - Distributed RPC and ETL processing Request this course now! Audience - Software and ETL developers - Mainframe professionals - Data scientists - Big data analysts - Hadoop professionals Format of the course - Part lecture, part discussion, exercises and heavy hands-on practice |
| apex | Apache Apex: Processing Big Data-in-Motion | 21 hours | Apache Apex is a YARN-native platform that unifies stream and batch processing. It processes big data-in-motion in a way that is scalable, performant, fault-tolerant, stateful, secure, distributed, and easily operable. This instructor-led, live training introduces Apache Apex's unified stream processing architecture, and walks participants through the creation of a distributed application using Apex on Hadoop. By the end of this training, participants will be able to: - Understand data processing pipeline concepts such as connectors for sources and sinks, common data transformations, etc. - Build, scale and optimize an Apex application - Process real-time data streams reliably and with minimum latency - Use Apex Core and the Apex Malhar library to enable rapid application development - Use the Apex API to write and re-use existing Java code - Integrate Apex into other applications as a processing engine - Tune, test and scale Apex applications Audience - Developers - Enterprise architects Format of the course - Part lecture, part discussion, exercises and heavy hands-on practice |
| beam | Unified Batch and Stream Processing with Apache Beam | 14 hours | Apache Beam is an open source, unified programming model for defining and executing parallel data processing pipelines. It's power lies in its ability to run both batch and streaming pipelines, with execution being carried out by one of Beam's supported distributed processing back-ends: Apache Apex, Apache Flink, Apache Spark, and Google Cloud Dataflow. Apache Beam is useful for ETL (Extract, Transform, and Load) tasks such as moving data between different storage media and data sources, transforming data into a more desirable format, and loading data onto a new system. In this instructor-led, live training (onsite or remote), participants will learn how to implement the Apache Beam SDKs in a Java or Python application that defines a data processing pipeline for decomposing a big data set into smaller chunks for independent, parallel processing. By the end of this training, participants will be able to: - Install and configure Apache Beam. - Use a single programming model to carry out both batch and stream processing from withing their Java or Python application. - Execute pipelines across multiple environments. Audience - Developers Format of the Course - Part lecture, part discussion, exercises and heavy hands-on practice Note - This course will be available Scala in the future. Please contact us to arrange. |
| ApacheIgnite | Apache Ignite: Improve Speed, Scale and Availability with In-Memory Computing | 14 hours | Apache Ignite is an in-memory computing platform that sits between the application and data layer to improve speed, scale, and availability. In this instructor-led, live training, participants will learn the principles behind persistent and pure in-memory storage as they step through the creation of a sample in-memory computing project. By the end of this training, participants will be able to: - Use Ignite for in-memory, on-disk persistence as well as a purely distributed in-memory database. - Achieve persistence without syncing data back to a relational database. - Use Ignite to carry out SQL and distributed joins. - Improve performance by moving data closer to the CPU, using RAM as a storage. - Spread data sets across a cluster to achieve horizontal scalability. - Integrate Ignite with RDBMS, NoSQL, Hadoop and machine learning processors. Audience - Developers Format of the course - Part lecture, part discussion, exercises and heavy hands-on practice |
| Course | Course Date | Course Price [Remote / Classroom] |
|---|---|---|
| A Practical Introduction to Stream Processing - Suntec Tower 2 - NobleProg Singapore | Mon, 2018-10-29 09:30 | 6045SGD / 10025SGD |
| Apache NiFi for Developers - United Square - NobleProg Singapore | Wed, 2018-10-31 09:30 | 2015SGD / 4415SGD |
| Real-Time Stream Processing with MapR - The Signature - NobleProg Singapore | Fri, 2018-11-02 09:30 | 2015SGD / 4415SGD |
| Unified Batch and Stream Processing with Apache Beam - Collyer Quay - NobleProg Singapore | Tue, 2018-11-13 09:30 | 4030SGD / 6930SGD |
| Apache NiFi for Administrators - One Fullerton - NobleProg Singapore | Wed, 2018-11-14 09:30 | 6045SGD / 10025SGD |
| Course | Venue | Course Date | Course Price [Remote / Classroom] |
|---|---|---|---|
| Codius: Setting Up a Host System | Centennial Tower - NobleProg Singapore | Wed, 2018-12-05 09:30 | 1814SGD / 4174SGD |
| Tableau for Developers | Centennial Tower - NobleProg Singapore | Wed, 2019-01-23 09:30 | 7363SGD / 11043SGD |
| IoT security | Centennial Tower - NobleProg Singapore | Wed, 2019-01-30 09:30 | 7254SGD / 10934SGD |
| Tableau for Developers | Centennial Tower - NobleProg Singapore | Mon, 2019-03-18 09:30 | 7363SGD / 11043SGD |
| Big Data - Data Science | Centennial Tower - NobleProg Singapore | Mon, 2019-04-01 09:30 | 4909SGD / 7929SGD |
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