Local, 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 "onsite live training" or "remote live training". Israel onsite live Big Data trainings can be carried out locally on customer premises or in NobleProg corporate training centers. Remote live training is carried out by way of an interactive, remote desktop.
NobleProg -- Your Local Training Provider
Testimonials
Exercises
Capgemini Polska Sp. z o.o.
Course: Hadoop Administration on MapR
Resources
Hafiz Rana - Birmingham City University
Course: Foundation R
Good explanations on how we do things
Birmingham City University
Course: Foundation R
I feel more confident with coding now. I've never done it before but now I understand that it's not rocket science and I can do it when necessary.
Anna Yartseva - Birmingham City University
Course: Foundation R
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
I found this course gave a great overview and quickly touched some areas I wasn't even considering.
Veterans Affairs Canada
Course: Hadoop Administration
The broad coverage of the subjects
Roche
Course: Big Data Storage Solution - NoSQL
Small group (4 trainees) and we could progress together. Also the trainer could so help everybody.
ICE International Copyright Enterprise Germany GmbH
Course: Spark for Developers
Ajay was very friendly, helpful and also knowledgable about the topic he was discussing.
Biniam Guulay - ICE International Copyright Enterprise Germany GmbH
Course: Spark for Developers
The lab exercises. Applying the theory from the first day in subsequent days.
Dell
Course: A Practical Introduction to Stream Processing
* Organization * Trainer's expertise with the subject
ENGIE- 101 Arch Street
Course: Python and Spark for Big Data (PySpark)
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 good energy of the trainer, he explained everything clearly and precisely
MicroStrategy Poland Sp. z o.o.
Course: Teradata Fundamentals
Materials (code, presentation) were good
MicroStrategy Poland Sp. z o.o.
Course: Teradata Fundamentals
Nice training, full of interesting topics. After each topic helpful examples were provided.
Pawel Wojcikowski - MicroStrategy Poland Sp. z o.o.
Course: Teradata Fundamentals
I like the comprehensive approach to the subject taught. The course was well-structured, the topics were prepared and presented in a good order, helping us understand step-by-step logic how particular aspects of Teradata work. Especially I like that a lot of impact was put on the topic of indices. Pablo is a very likeable person and a good teacher. The time spent on this training was definitely spent well.
MicroStrategy Poland Sp. z o.o.
Course: Teradata Fundamentals
interaction
Maastricht University | UMIO
Course: Teradata Fundamentals
Discussing many examples.
Emily Chesworth - Maastricht University | UMIO
Course: Teradata Fundamentals
exercises & subquery
Maastricht University | UMIO
Course: 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
Course: Teradata Fundamentals
passionate trainer
Sylvia Baniak - Maastricht University | UMIO
Course: Teradata Fundamentals
x
Maastricht University | UMIO
Course: Teradata Fundamentals
New learned skills
Maastricht University | UMIO
Course: Teradata Fundamentals
The enthusiasm of the trainer
Maastricht University | UMIO
Course: Teradata Fundamentals
Explaining the step to take when trying to answer a question via the SQL
Maastricht University | UMIO
Course: Teradata Fundamentals
The Topic
Accenture Inc.
Course: Data Vault: Building a Scalable Data Warehouse
Content was good
Northrop Grumman
Course: 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
Course: Apache Accumulo Fundamentals
the follow along style instead of just sitting and listening.
Jack Gallacher - Northrop Grumman
Course: Apache Accumulo Fundamentals
The ability of the trainer to break down complex topics to simple topics.
Northrop Grumman
Course: 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
Course: Apache Accumulo Fundamentals
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
Course: Apache Accumulo Fundamentals
I genuinely liked work exercises with cluster to see performance of nodes across cluster and extended functionality.
CACI Ltd
Course: Apache NiFi for Developers
The trainers in depth knowledge of the subject
CACI Ltd
Course: Apache NiFi for Administrators
Ajay was a very experienced consultant and was able to answer all our questions and even made suggestions on best practices for the project we are currently engaged on.
CACI Ltd
Course: Apache NiFi for Administrators
That I had it in the first place.
Peter Scales - CACI Ltd
Course: Apache NiFi for Developers
content & detail to the point
Sirat Khwaja - Northrop Grumman
Course: Apache Accumulo Fundamentals
Code examples following into practical exercises.
Northrop Grumman
Course: Apache Accumulo Fundamentals
The NIFI workflow excercises
Politiets Sikkerhetstjeneste
Course: Apache NiFi for Administrators
New knowledge about SOAP and Rest API.
Total Access Communication Public Company Limited
Course: Talend Open Studio for Data Integration
It Good for many exercise.
Gun Thepinjai - Total Access Communication Public Company Limited
Course: Talend Open Studio for Data Integration
answers to our specific questions
MOD BELGIUM
Course: 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.
Course: SolrCloud
wide aspect of Teradata knolwedge - starting basic architecture monitoring finishing
MicroStrategy Poland Sp. z o.o.
Course: Teradata Fundamentals
Doing similar exercises different ways really help understanding what each component (Hadoop/Spark, standalone/cluster) can do on its own and together. It gave me ideas on how I should test my application on my local machine when I develop vs when it is deployed on a cluster.
Thomas Carcaud - IT Frankfurt GmbH
Course: Spark for Developers
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
Course: Python and Spark for Big Data (PySpark)
Intensity, Training materials and expertise, Clarity, Excellent communication with Alessandra
Marija Hornis Dmitrovic - Marija Hornis
Course: Data Science for Big Data Analytics
NA
DBS
Course: Python and Spark for Big Data (PySpark)
R programming
Osden Jokonya - University of the Western Cape
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
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 Israel
By the end of this training, participants will be able to:
- Install and configure Python and MySql.
- Understand what Data Science is and how it can add value to virtually any business.
- Learn the fundamentals of coding in Python
- Learn supervised and unsupervised Machine Learning techniques, and how to implement them and interpret the 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.
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
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 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 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
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.
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.
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
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.
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.
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
Summary
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An advanced training program covering the current state of the art in Internet of Things
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Cuts across multiple technology domains to develop awareness of an IoT system and its components and how it can help businesses and organizations.
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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
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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.
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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.
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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
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M2M Wireless protocols for IoT- WiFi, Zigbee/Zwave, Bluetooth, ANT+ : When and where to use which one?
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Mobile/Desktop/Web app- for registration, data acquisition and control –Available M2M data acquisition platform for IoT-–Xively, Omega and NovoTech, etc.
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Security issues and security solutions for IoT
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Open source/commercial electronics platform for IoT-Raspberry Pi, Arduino , ArmMbedLPC etc
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Open source /commercial enterprise cloud platform for AWS-IoT apps, Azure -IOT, Watson-IOT cloud in addition to other minor IoT clouds
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Studies of business and technology of some of the common IoT devices like Home automation, Smoke alarm, vehicles, military, home health etc.
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 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 (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.
- Developers
Format of the Course
- Lectures, hands-on practice, small tests along the way to gauge understanding
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.
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.
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
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
This course is intended to demystify big data/hadoop technology and to show it is not difficult to understand.
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
By the end of this training, participants will:
- Understand the evolution and trends for machine learning.
- Know how machine learning is being used across different industries.
- Become familiar with the tools, skills and services available to implement machine learning within an organization.
- Understand how machine learning can be used to enhance data mining and analysis.
- Learn what a data middle backend is, and how it is being used by businesses.
- Understand the role that big data and intelligent applications are playing across industries.
By the end of this training, participants will 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: developers
Duration: three days
Format: lectures (50%) and hands-on labs (50%).
- 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.
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
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
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