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. Finland onsite live Big Data trainings can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
Testimonials
I generally liked the fernando's knowledge.
Valentin de Dianous - Informatique ProContact INC.
Course: Big Data Architect
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
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
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
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
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
The VM I liked very much The Teacher was very knowledgeable regarding the topic as well as other topics, he was very nice and friendly I liked the facility in Dubai.
Safar Alqahtani - Elm Information Security
Course: Big Data Analytics in Health
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
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 trainer was always open for questions and willing to answer and explain everything. He seems to have very good and deep knowledge of what he is teaching. We were able to focus more on topics that might bring value for us since we were only two students.
DEVK Deutsche Eisenbahn Versicherung Sach- und HUK-Versicherungsverein a.G.
Course: Hadoop and Spark 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
James answered my every question, was extremely patient and explained me everything. NIFI was Greek and latin for me and i have learnt what was a processor, flunnel and the root process from Beginner level to Advanced Level.
Firdous Hashim Ali - MOD A BLOCK
Course: Apache NiFi for Developers
That I had it in the first place.
Peter Scales - CACI Ltd
Course: Apache NiFi for Developers
Work exercises with cluster to see performance of nodes across cluster and extended functionality
CACI Ltd
Course: Apache NiFi for Developers
The fact that we were able to take with us most of the information/course/presentation/exercises done, so that we can look over them and perhaps redo what we didint understand first time or improve what we already did.
Raul Mihail Rat - Edina Kiss, Accenture Industrial SS
Course: Python, Spark, and Hadoop for Big Data
I liked that it managed to lay the foundations of the topic and go to some quite advanced exercises. Also provided easy ways to write/test the code.
Ionut Goga - Edina Kiss, Accenture Industrial SS
Course: Python, Spark, and Hadoop for Big Data
The live examples
Ahmet Bolat - Edina Kiss, Accenture Industrial SS
Course: Python, Spark, and Hadoop for Big Data
good overview, good balance between theory and exercises
Proximus
Course: Data Analysis with Hive/HiveQL
It was a very practical training, I liked the hands-on exercises.
Proximus
Course: Data Analysis with Hive/HiveQL
Liked very much the interactive way of learning.
Luigi Loiacono
Course: Data Analysis with Hive/HiveQL
The information given was interesting and the best part was towards the end when we were provided with Data from Murex and worked on Data we are familiar with and perform operations to get results.
Jessica Chaar
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
I like the exercices done
Nour Assaf
Course: Data Mining and Analysis
I really enjoyed learning new and interesting things.
SIVECO Romania SA
Course: Data Mining
very tailored to needs
Yashan Wang
Course: Data Mining with R
The Topic
Accenture Inc.
Course: Data Vault: Building a Scalable Data Warehouse
Learning about all the chart types and what they are used for. Learning the value of decluttering. Learning about the methods to show time data.
Susan Williams
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
I thought that the information was interesting.
Allison May
Course: Data Visualization
The trainer was so knowledgeable and included areas I was interested in
Mohamed Salama
Course: Data Mining & Machine Learning with R
The trainer was very concern about individual understanding.
Muhammad Surajo Sanusi - Birmingham City University
Course: Foundation R
I genuinely enjoyed the hands passed exercises.
Yunfa Zhu - Environmental and Climate Change Canada
Course: Foundation R
I was benefit from the good examples and opportunity to follow along.
Environmental and Climate Change Canada
Course: Foundation R
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
Course: KNIME Analytics Platform for BI
The way it was conducted, the way trainer keeps contact with audience, materials, everything was really good!
Marcin Prewo - GE Medical Systems Polska Sp. Z O.O.
Course: Process Mining
Open discussion with trainer
Tomek Danowski - GE Medical Systems Polska Sp. Z O.O.
Course: Process Mining
A lot of exercises, trainer was always helping us and giving the solution, he was always answering our questions & explaining our doubts. The trainer was also always checking with us about the break we would like to take etc.
GE Medical Systems Polska Sp. Z O.O.
Course: Process Mining
Sufficient hands on, trainer is knowledgable
Chris Tan
Course: A Practical Introduction to Stream Processing
The trainer was passionate and well-known what he said I appreciate his help and answers all our questions and suggested cases.
Course: A Practical Introduction to Stream Processing
The trainer was passionate and well-known what he said I appreciate his help and answers all our questions and suggested cases.
Course: A Practical Introduction to Stream Processing
Big Data Course Outlines in Finland
Course Name
Duration
Overview
Course Name
Duration
Overview
21 hours
This instructor-led, live training in Finland (online or onsite) is aimed at developers who wish to use and integrate Spark, Hadoop, and Python to process, analyze, and transform large and complex data sets.
By the end of this training, participants will be able to:
- 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.
14 hours
This instructor-led, live training in Finland (online or onsite) is aimed at beginner to intermediate-level data analysts and data scientists who wish to use Weka to perform data mining tasks.
By the end of this training, participants will be able to:
- Install and configure Weka.
- Understand the Weka environment and workbench.
- Perform data mining tasks using Weka.
14 hours
This instructor-led, live training in Finland (online or onsite) is aimed at data analysts or anyone who wishes to use SPSS Modeler to perform data mining activities.
By the end of this training, participants will be able to:
- Understand the fundamentals of data mining.
- Learn how to import and assess data quality with the Modeler.
- Develop, deploy, and evaluate data models efficiently.
35 hours
Participants who complete this instructor-led, live training in Finland 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.
21 hours
In this instructor-led, live training in Finland, participants will learn how to use Python and Spark together to analyze big data as they work on hands-on exercises.
By the end of this training, participants will be able to:
- Learn how to use Spark with Python to analyze Big Data.
- Work on exercises that mimic real world cases.
- Use different tools and techniques for big data analysis using PySpark.
7 hours
This course covers how to use Hive SQL language (AKA: Hive HQL, SQL on Hive, HiveQL) for people who extract data from Hive
21 hours
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
- 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.
14 hours
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
- 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
14 hours
Datameer is a business intelligence and analytics platform built on Hadoop. It allows end-users to access, explore and correlate large-scale, structured, semi-structured and unstructured data in an easy-to-use fashion.
In this instructor-led, live training, participants will learn how to use Datameer to overcome Hadoop's steep learning curve as they step through the setup and analysis of a series of big data sources.
By the end of this training, participants will be able to:
- 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
14 hours
This instructor-led, live training in Finland (online or onsite) is aimed at data scientists who wish to use Excel for data mining.
- By the end of this training, participants will be able to:
- Explore data with Excel to perform data mining and analysis.
- Use Microsoft algorithms for data mining.
- Understand concepts in Excel data mining.
21 hours
Dremio is an open-source "self-service data platform" that accelerates the querying of different types of data sources. Dremio integrates with relational databases, Apache Hadoop, MongoDB, Amazon S3, ElasticSearch, and other data sources. It supports SQL and provides a web UI for building queries.
In this instructor-led, live training, participants will learn how to install, configure and use Dremio as a unifying layer for data analysis tools and the underlying data repositories.
By the end of this training, participants will be able to:
- 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.
21 hours
Apache Drill is a schema-free, distributed, in-memory columnar SQL query engine for Hadoop, NoSQL and other Cloud and file storage systems. The power of Apache Drill lies in its ability to join data from multiple data stores using a single query. Apache Drill supports numerous NoSQL databases and file systems, including HBase, MongoDB, MapR-DB, HDFS, MapR-FS, Amazon S3, Azure Blob Storage, Google Cloud Storage, Swift, NAS and local files. Apache Drill is the open source version of Google's Dremel system which is available as an infrastructure service called Google BigQuery.
In this instructor-led, live training, participants will learn the fundamentals of Apache Drill, then leverage the power and convenience of SQL to interactively query big data across multiple data sources, without writing code. Participants will also learn how to optimize their Drill queries for distributed SQL execution.
By the end of this training, participants will be able to:
- 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
14 hours
Apache Arrow is an open-source in-memory data processing framework. It is often used together with other data science tools for accessing disparate data stores for analysis. It integrates well with other technologies such as GPU databases, machine learning libraries and tools, execution engines, and data visualization frameworks.
In this onsite instructor-led, live training, participants will learn how to integrate Apache Arrow with various Data Science frameworks to access data from disparate data sources.
By the end of this training, participants will be able to:
- 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.
14 hours
The objective of the course is to enable participants to gain a mastery of how to work with the SQL language in Oracle database for data extraction at intermediate level.
14 hours
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)
35 hours
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
35 hours
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.
35 hours
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
- Law Enforcement specialists with a technical background
- Part lecture, part discussion, exercises and heavy hands-on practice
14 hours
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.
21 hours
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
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.
14 hours
The course is part of the Data Scientist skill set (Domain: Data and Technology).
14 hours
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
21 hours
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.
35 hours
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.
7 hours
This instructor-led, live training in Finland (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.
7 hours
This instructor-led, live training in Finland (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.
28 hours
This instructor-led, live training in Finland (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.
14 hours
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.
- Developers
- Part lecture, part discussion, exercises and heavy hands-on practice
7 hours
Alluxio is an open-source virtual distributed storage system that unifies disparate storage systems and enables applications to interact with data at memory speed. It is used by companies such as Intel, Baidu and Alibaba.
In this instructor-led, live training, participants will learn how to use Alluxio to bridge different computation frameworks with storage systems and efficiently manage multi-petabyte scale data as they step through the creation of an application with Alluxio.
By the end of this training, participants will be able to:
- Develop an application with Alluxio
- Connect big data systems and applications while preserving one namespace
- Efficiently extract value from big data in any storage format
- Improve workload performance
- Deploy and manage Alluxio standalone or clustered
- Data scientist
- Developer
- System administrator
- Part lecture, part discussion, exercises and heavy hands-on practice
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