Online or onsite, instructor-led live Apache Hadoop training courses demonstrate through interactive hands-on practice the core components of the Hadoop ecosystem and how these technologies can be used to solve large-scale problems.
Hadoop 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. Malta onsite live Hadoop trainings can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
Testimonials
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
Hadoop Subcategories in Malta
Apache Hadoop Course Outlines in Malta
Course Name
Duration
Overview
Course Name
Duration
Overview
21 hours
This instructor-led, live training in Malta (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.
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
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
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
35 hours
Audience:
The course is intended for IT specialists looking for a solution to store and process large data sets in a distributed system environment
Goal:
Deep knowledge on Hadoop cluster administration.
21 hours
This course is intended for developers, architects, data scientists or any profile that requires access to data either intensively or on a regular basis. The major focus of the course is data manipulation and transformation. Among the tools in the Hadoop ecosystem this course includes the use of Pig and Hive both of which are heavily used for data transformation and manipulation. This training also addresses performance metrics and performance optimisation. The course is entirely hands on and is punctuated by presentations of the theoretical aspects.
21 hours
Big data analytics involves the process of examining large amounts of varied data sets in order to uncover correlations, hidden patterns, and other useful insights.
The health industry has massive amounts of complex heterogeneous medical and clinical data. Applying big data analytics on health data presents huge potential in deriving insights for improving delivery of healthcare. However, the enormity of these datasets poses great challenges in analyses and practical applications to a clinical environment.
In this instructor-led, live training (remote), participants will learn how to perform big data analytics in health as they step through a series of hands-on live-lab exercises.
By the end of this training, participants will be able to:
- Install and configure big data analytics tools such as Hadoop MapReduce and Spark
- Understand the characteristics of medical data
- Apply big data techniques to deal with medical data
- Study big data systems and algorithms in the context of health applications
- Developers
- Data Scientists
- 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
The course is dedicated to IT specialists that are looking for a solution to store and process large data sets in distributed system environment
Course goal:
Getting knowledge regarding Hadoop cluster administration
21 hours
Apache Hadoop is the most popular framework for processing Big Data on clusters of servers. In this three (optionally, four) days course, attendees will learn about the business benefits and use cases for Hadoop and its ecosystem, how to plan cluster deployment and growth, how to install, maintain, monitor, troubleshoot and optimize Hadoop. They will also practice cluster bulk data load, get familiar with various Hadoop distributions, and practice installing and managing Hadoop ecosystem tools. The course finishes off with discussion of securing cluster with Kerberos.
“…The materials were very well prepared and covered thoroughly. The Lab was very helpful and well organized”
— Andrew Nguyen, Principal Integration DW Engineer, Microsoft Online Advertising Audience Hadoop administrators Format Lectures and hands-on labs, approximate balance 60% lectures, 40% labs.
— Andrew Nguyen, Principal Integration DW Engineer, Microsoft Online Advertising Audience Hadoop administrators Format Lectures and hands-on labs, approximate balance 60% lectures, 40% labs.
21 hours
Apache Hadoop is the most popular framework for processing Big Data. Hadoop provides rich and deep analytics capability, and it is making in-roads in to tradional BI analytics world. This course will introduce an analyst to the core components of Hadoop eco system and its analytics
Audience
Business Analysts
Duration
three days
Format
Lectures and hands on labs.
28 hours
Apache Hadoop is the most popular framework for processing Big Data on clusters of servers. This course will introduce a developer to various components (HDFS, MapReduce, Pig, Hive and HBase) Hadoop ecosystem.
21 hours
Apache Hadoop is one of the most popular frameworks for processing Big Data on clusters of servers. This course delves into data management in HDFS, advanced Pig, Hive, and HBase. These advanced programming techniques will be beneficial to experienced Hadoop developers.
Audience: developers
Duration: three days
Format: lectures (50%) and hands-on labs (50%).
21 hours
Hadoop is the most popular Big Data processing framework.
14 hours
In this instructor-led training in Malta, participants will learn the core components of the Hadoop ecosystem and how these technologies can be used to solve large-scale problems. By learning these foundations, participants will 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
28 hours
Audience:
This course is intended to demystify big data/hadoop technology and to show it is not difficult to understand.
28 hours
Hadoop is a popular Big Data processing framework. Python is a high-level programming language famous for its clear syntax and code readibility.
In this instructor-led, live training, participants will learn how to work with Hadoop, MapReduce, Pig, and Spark using Python as they step through multiple examples and use cases.
By the end of this training, participants will be able to:
- Understand the basic concepts behind Hadoop, MapReduce, Pig, and Spark
- Use Python with Hadoop Distributed File System (HDFS), MapReduce, Pig, and Spark
- Use Snakebite to programmatically access HDFS within Python
- Use mrjob to write MapReduce jobs in Python
- Write Spark programs with Python
- Extend the functionality of pig using Python UDFs
- Manage MapReduce jobs and Pig scripts using Luigi
- Developers
- IT Professionals
- Part lecture, part discussion, exercises and heavy hands-on practice
35 hours
This instructor-led, live training in Malta (online or onsite) is aimed at system administrators who wish to learn how to set up, deploy and manage Hadoop clusters within their organization.
By the end of this training, participants will be able to:
- Install and configure Apache Hadoop.
- Understand the four major components in the Hadoop ecoystem: HDFS, MapReduce, YARN, and Hadoop Common.
- Use Hadoop Distributed File System (HDFS) to scale a cluster to hundreds or thousands of nodes.
- Set up HDFS to operate as storage engine for on-premise Spark deployments.
- Set up Spark to access alternative storage solutions such as Amazon S3 and NoSQL database systems such as Redis, Elasticsearch, Couchbase, Aerospike, etc.
- Carry out administrative tasks such as provisioning, management, monitoring and securing an Apache Hadoop cluster.
21 hours
This course introduces HBase – a NoSQL store on top of Hadoop. The course is intended for developers who will be using HBase to develop applications, and administrators who will manage HBase clusters.
We will walk a developer through HBase architecture and data modelling and application development on HBase. It will also discuss using MapReduce with HBase, and some administration topics, related to performance optimization. The course is very hands-on with lots of lab exercises.
Duration : 3 days Audience : Developers & Administrators
Duration : 3 days Audience : Developers & Administrators
14 hours
Audience
- Developers
- Lectures, hands-on practice, small tests along the way to gauge understanding
21 hours
In this instructor-led, live training in Malta (onsite or remote), participants will learn how to deploy and manage Apache NiFi in a live lab environment.
By the end of this training, participants will be able to:
- Install and configure Apachi NiFi.
- Source, transform and manage data from disparate, distributed data sources, including databases and big data lakes.
- Automate dataflows.
- Enable streaming analytics.
- Apply various approaches for data ingestion.
- Transform Big Data and into business insights.
7 hours
In this instructor-led, live training in Malta, 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.
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.
- Developers
- Part lecture, part discussion, exercises and heavy hands-on practice
14 hours
Sqoop is an open source software tool for transfering data between Hadoop and relational databases or mainframes. It can be used to import data from a relational database management system (RDBMS) such as MySQL or Oracle or a mainframe into the Hadoop Distributed File System (HDFS). Thereafter, the data can be transformed in Hadoop MapReduce, and then re-exported back into an RDBMS.
In this instructor-led, live training, participants will learn how to use Sqoop to import data from a traditional relational database to Hadoop storage such HDFS or Hive and vice versa.
By the end of this training, participants will be able to:
- Install and configure Sqoop
- Import data from MySQL to HDFS and Hive
- Import data from HDFS and Hive to MySQL
- System administrators
- 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
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
- Developers
- Part lecture, part discussion, exercises and heavy hands-on practice
21 hours
Cloudera Impala is an open source massively parallel processing (MPP) SQL query engine for Apache Hadoop clusters.
Impala enables users to issue low-latency SQL queries to data stored in Hadoop Distributed File System and Apache Hbase without requiring data movement or transformation.
Audience
This course is aimed at analysts and data scientists performing analysis on data stored in Hadoop via Business Intelligence or SQL tools.
After this course delegates will be able to
- Extract meaningful information from Hadoop clusters with Impala.
- Write specific programs to facilitate Business Intelligence in Impala SQL Dialect.
- Troubleshoot Impala.
21 hours
Apache Ambari is an open-source management platform for provisioning, managing, monitoring and securing Apache Hadoop clusters.
In this instructor-led live training participants will learn the management tools and practices provided by Ambari to successfully manage Hadoop clusters.
By the end of this training, participants will be able to:
- Set up a live Big Data cluster using Ambari
- Apply Ambari's advanced features and functionalities to various use cases
- Seamlessly add and remove nodes as needed
- Improve a Hadoop cluster's performance through tuning and tweaking
- DevOps
- System Administrators
- DBAs
- Hadoop testing professionals
- Part lecture, part discussion, exercises and heavy hands-on practice
21 hours
This instructor-led, live training in Malta (online or onsite) introduces Hortonworks Data Platform (HDP) and walks participants through the deployment of Spark + Hadoop solution.
By the end of this training, participants will be able to:
- Use Hortonworks to reliably run Hadoop at a large scale.
- Unify Hadoop's security, governance, and operations capabilities with Spark's agile analytic workflows.
- Use Hortonworks to investigate, validate, certify and support each of the components in a Spark project.
- Process different types of data, including structured, unstructured, in-motion, and at-rest.
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