
Online or onsite, instructor-led live Big Data training courses start with an introduction to elemental concepts of Big Data, then progress into the programming languages and methodologies used to perform Data Analysis. Tools and infrastructure for enabling Big Data storage, Distributed Processing, and Scalability are discussed, compared and implemented in demo practice sessions.
Big Data training is available as "online live training" or "onsite live training". Online live training (aka "remote live training") is carried out by way of an interactive, remote desktop. Onsite live Big Data trainings in the Philippines can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
Testimonials
how the trainor shows his knowledge in the subject he's teachign
john ernesto ii fernandez - Philippine AXA Life Insurance Corporation
Course: Data Vault: Building a Scalable Data Warehouse
The Topic
Accenture Inc.
Course: Data Vault: Building a Scalable Data Warehouse
Recalling/reviewing keypoints of the topics discussed.
Paolo Angelo Gaton - SMS Global Technologies Inc.
Course: Building Stream Processing Applications with Kafka Streams
He knows the subject very well
Thakral One
Course: Apache Druid for Real-Time Data Analysis
Ajay created a very helpful repository, filled with notes on processes and setups. He also goes through each of our virtual machines to make sure we are keeping up to speed, and he guides us when we're not. He is generous and helpful in training newbies like us.
Thakral One
Course: Apache Druid for Real-Time Data Analysis
Instructor provided different ways to setup druid
Thakral One
Course: Apache Druid for Real-Time Data Analysis
Detailed explanation and very approachable trainer
Thakral One
Course: Apache Druid for Real-Time Data Analysis
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
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
I generally liked the fernando's knowledge.
Valentin de Dianous - Informatique ProContact INC.
Course: Big Data Architect
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 tutor, Mr. Michael Yan, 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 introduction of new packages
Ottawa Research and Development Center, Science Technology Branch, Agriculture and Agri-Food Canada
Course: Programming with Big Data in R
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 to meet 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
The broad coverage of the subjects
Roche
Course: Big Data Storage Solution - NoSQL
Intensity, Training materials and expertise, Clarity, Excellent communication with Alessandra
Marija Hornis Dmitrovic - Marija Hornis
Course: Data Science for Big Data Analytics
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
I liked the way that my trainer was teaching us, and the Meeting Room was taken for our course.
Mohammed Othman Karim, Sulaymaniyah Asayish Agency
Course: A Practical Introduction to Data Analysis and Big Data
Interactive topics and the style used by the lecture to simplified the topics for the students
Miran Saeed - Mohammed Othman Karim, Sulaymaniyah Asayish Agency
Course: A Practical Introduction to Data Analysis and Big Data
Smart and cleverness
Mohammed Othman Karim, Sulaymaniyah Asayish Agency
Course: A Practical Introduction to Data Analysis and Big Data
the trainer and his ability to lecture
ibrahim hamakarim - Mohammed Othman Karim, Sulaymaniyah Asayish Agency
Course: A Practical Introduction to Data Analysis and Big Data
Practical exercises
JOEL CHIGADA - University of the Western Cape
Course: A Practical Introduction to Data Analysis and Big Data
R programming
Osden Jokonya - University of the Western Cape
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
presentation of technologies
Continental AG / Abteilung: CF IT Finance
Course: A practical introduction to Data Analysis and Big Data
Willingness to share more
Balaram Chandra Paul
Course: A practical introduction to Data Analysis and Big Data
trainer's knowledge
Fatma Badi - Maitha Jamal Alshamsi, Dubai Electricity & Water Authority
Course: Big Data - Data Science
I found this course gave a great overview and quickly touched some areas I wasn't even considering.
Veterans Affairs Canada
Course: Hadoop Administration
I found the training good, very informative....but could have been spread over 4 or 5 days, allowing us to go into more details on different aspects.
Veterans Affairs Canada
Course: Hadoop Administration
Ambari management tool. Ability to discuss practical Hadoop experiences from other business case than telecom.
Ericsson
Course: Administrator Training for Apache Hadoop
Lot of hands-on exercises.
Ericsson
Course: Administrator Training for Apache Hadoop
It was very hands-on, we spent half the time actually doing things in Cloudera/Hadoop, 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
Many hands-on sessions.
Jacek Pieczątka
Course: Administrator Training for Apache Hadoop
Big competences of Trainer
Grzegorz Gorski
Course: Administrator Training for Apache Hadoop
Trainer give reallive Examples
Simon Hahn
Course: Administrator Training for Apache Hadoop
practical things of doing, also theory was served good by Ajay
Dominik Mazur - Capgemini Polska Sp. z o.o.
Course: Hadoop Administration on MapR
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
I like how he was able to elaborate about Nifi and how powerful it is. You can basically use it for any infrastructure and use many different computer languages. Also i was glad we were able to fix the Nifi cert renewal issue we were having with the Truststore.
Joachim Martin - Jacob Jaskolka, BHG Financial
Course: Apache NiFi for Administrators
general knowledge and the possibilities that the training offered in terms on the tool.
Nalfis Tobar - Jacob Jaskolka, BHG Financial
Course: Apache NiFi for Administrators
The working sessions where we worked on real issues we are trying to solve and built out solutions together.
Jacob Jaskolka, BHG Financial
Course: Apache NiFi for Administrators
Hands on
Isaac Hastings, New Zealand Defence Force
Course: Apache NiFi for Administrators
Hands on.
Dwayne McDonald - Isaac Hastings, New Zealand Defence Force
Course: Apache NiFi for Administrators
Virtual environment working well and trainer positive attitude
Wojciech Lukawski - Orsted Polska sp. z o.o.
Course: Apache NiFi for Developers
Big Data Course Outlines in the Philippines
- Set up the necessary environment to start processing big data with Spark, Hadoop, and Python.
- Understand the features, core components, and architecture of Spark and Hadoop.
- Learn how to integrate Spark, Hadoop, and Python for big data processing.
- Explore the tools in the Spark ecosystem (Spark MlLib, Spark Streaming, Kafka, Sqoop, Kafka, and Flume).
- Build collaborative filtering recommendation systems similar to Netflix, YouTube, Amazon, Spotify, and Google.
- Use Apache Mahout to scale machine learning algorithms.
- Install and configure Weka.
- Understand the Weka environment and workbench.
- Perform data mining tasks using Weka.
- Understand the fundamentals of data mining.
- Learn how to import and assess data quality with the Modeler.
- Develop, deploy, and evaluate data models efficiently.
- Learn how to use Spark with Python to analyze Big Data.
- Work on exercises that mimic real world cases.
- Use different tools and techniques for big data analysis using PySpark.
- Data analysts or anyone interested in learning how to interpret data to solve problems
- 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.
- Consume real-time streaming data using Kylin
- Utilize Apache Kylin's powerful features, rich SQL interface, spark cubing and subsecond query latency
- We use the latest version of Kylin (as of this writing, Apache Kylin v2.0)
- Big data engineers
- Big Data analysts
- Part lecture, part discussion, exercises and heavy hands-on practice
- Create, curate, and interactively explore an enterprise data lake
- Access business intelligence data warehouses, transactional databases and other analytic stores
- Use a spreadsheet user-interface to design end-to-end data processing pipelines
- Access pre-built functions to explore complex data relationships
- Use drag-and-drop wizards to visualize data and create dashboards
- Use tables, charts, graphs, and maps to analyze query results
- Data analysts
- Part lecture, part discussion, exercises and heavy hands-on practice
- By the end of this training, participants will be able to:
- Explore data with Excel to perform data mining and analysis.
- Use Microsoft algorithms for data mining.
- Understand concepts in Excel data mining.
- Install and configure Dremio
- Execute queries against multiple data sources, regardless of location, size, or structure
- Integrate Dremio with BI and data sources such as Tableau and Elasticsearch
- Data scientists
- Business analysts
- Data engineers
- Part lecture, part discussion, exercises and heavy hands-on practice
- To request a customized training for this course, please contact us to arrange.
- Perform "self-service" exploration on structured and semi-structured data on Hadoop
- Query known as well as unknown data using SQL queries
- Understand how Apache Drills receives and executes queries
- Write SQL queries to analyze different types of data, including structured data in Hive, semi-structured data in HBase or MapR-DB tables, and data saved in files such as Parquet and JSON.
- Use Apache Drill to perform on-the-fly schema discovery, bypassing the need for complex ETL and schema operations
- Integrate Apache Drill with BI (Business Intelligence) tools such as Tableau, Qlikview, MicroStrategy and Excel
- Data analysts
- Data scientists
- SQL programmers
- Part lecture, part discussion, exercises and heavy hands-on practice
- Install and configure Apache Arrow in a distributed clustered environment
- Use Apache Arrow to access data from disparate data sources
- Use Apache Arrow to bypass the need for constructing and maintaining complex ETL pipelines
- Analyze data across disparate data sources without having to consolidate it into a centralized repository
- Data scientists
- Data engineers
- Part lecture, part discussion, exercises and heavy hands-on practice
- To request a customized training for this course, please contact us to arrange.
- Mobile devices and applications
- Cloud services
- Social business technologies and networking
- Big Data and analytics
- 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.
- Developers
- Part lecture, part discussion, exercises and heavy hands-on practice
- 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
- Law Enforcement specialists with a technical background
- Part lecture, part discussion, exercises and heavy hands-on practice
- 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.
- 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.
- 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.
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