Hadoop Training Courses

Hadoop Training

Apache Hadoop is an open-source implementation of two core Google BigData solutions: GFS (Google File System) and MapReduce programming paradigm. It is a complete framework destined for storing and processing large data sets. Hadoop is used by most of the global cloud service providers including such leaders like Yahoo, Facebook or LinkedIn.

NobleProg onsite live Hadoop training courses demonstrate through discussion and hands-on practice the core components of the Hadoop ecosystem and how these technologies can used to solve large-scale problems.

Hadoop training is available in various formats, including onsite live training and live instructor-led training using an interactive, remote desktop setup. Local Hadoop training can be carried out live on customer premises or in NobleProg local training centers.

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Hadoop Course Outlines

Code Name Duration Overview
bigddbsysfun Big Data & Database Systems Fundamentals 14 hours The course is part of the Data Scientist skill set (Domain: Data and Technology).
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
HadoopDevAd Hadoop for Developers and Administrators 21 hours Hadoop is the most popular Big Data processing framework.
graphcomputing Introduction to Graph Computing 28 hours A large number of real world problems can be described in terms of graphs. For example, the Web graph, the social network graph, the train network graph and the language graph. These graphs tend to be extremely large; processing them requires a specialized set of tools and mindset referred to as graph computing. In this instructor-led, live training, participants will learn about the various technology offerings and implementations for processing graph data. The aim is to identify real-world objects, their characteristics and relationships, then model these relationships and process them as data using graph computing approaches. We start with a broad overview and narrow in on specific tools as we step through a series of case studies, hands-on exercises and live deployments. By the end of this training, participants will be able to: Understand how graph data is persisted and traversed Select the best framework for a given task (from graph databases to batch processing frameworks) Implement Hadoop, Spark, GraphX and Pregel to carry out graph computing across many machines in parallel View real-world big data problems in terms of graphs, processes and traversals Audience Developers Format of the course Part lecture, part discussion, exercises and heavy hands-on practice
IntroToAvro Apache Avro: Data serialization for distributed applications 14 hours This course is intended for Developers Format of the course Lectures, hands-on practice, small tests along the way to gauge understanding
hadooppython Hadoop with Python 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 Audience Developers IT Professionals Format of the course Part lecture, part discussion, exercises and heavy hands-on practice
mdlmrah Model MapReduce and Apache Hadoop 14 hours The course is intended for IT specialist that works with the distributed processing of large data sets across clusters of computers.
druid Druid: Build a fast, real-time data analysis system 21 hours Druid is an open-source, column-oriented, distributed data store written in Java. It was designed to quickly ingest massive quantities of event data and execute low-latency OLAP queries on that data. Druid is commonly used in business intelligence applications to analyze high volumes of real-time and historical data. It is also well suited for powering fast, interactive, analytic dashboards for end-users. Druid is used by companies such as Alibaba, Airbnb, Cisco, eBay, Netflix, Paypal, and Yahoo. In this course we explore some of the limitations of data warehouse solutions and discuss how Druid can compliment those technologies to form a flexible and scalable streaming analytics stack. We walk through many examples, offering participants the chance to implement and test Druid-based solutions in a lab environment. Audience     Application developers     Software engineers     Technical consultants     DevOps professionals     Architecture engineers Format of the course     Part lecture, part discussion, heavy hands-on practice, occasional tests to gauge understanding
apachedrill Apache Drill for On-the-Fly Analysis of Multiple Big Data Formats 21 hours Apache Drill is a schema-free, distributed, in-memory columnar SQL query engine for Hadoop, NoSQL and and other Cloud and file storage systems. Apache Drill's power 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. 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 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 Audience Data analysts Data scientists SQL programmers Format of the course Part lecture, part discussion, exercises and heavy hands-on practice
68737 Hadoop for Data Analysts 14 hours
voldemort Voldemort: Setting up a key-value distributed data store 14 hours Voldemort is an open-source distributed data store that is designed as a key-value store.  It is used at LinkedIn by numerous critical services powering a large portion of the site. This course will introduce the architecture and capabilities of Voldomort and walk participants through the setup and application of a key-value distributed data store. Audience     Software developers     System administrators     DevOps engineers Format of the course     Part lecture, part discussion, heavy hands-on practice, occasional tests to gauge understanding
68780 Apache Spark 14 hours
BigData_ A practical introduction to Data Analysis and Big Data 35 hours Participants who complete this training 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. Audience Developers / programmers IT consultants Format of the course Part lecture, part discussion, hands-on practice and implementation, occasional quizing to measure progress.
hadoopmapr Hadoop Administration on MapR 28 hours Audience: This course is intended to demystify big data/hadoop technology and to show it is not difficult to understand.
ApHadm1 Apache Hadoop: Manipulation and Transformation of Data Performance 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.
68736 Hadoop for Developers (2 days) 14 hours
hadoopforprojectmgrs Hadoop for Project Managers 14 hours As more and more software and IT projects migrate from local processing and data management to distributed processing and big data storage, Project Managers are finding the need to upgrade their knowledge and skills to grasp the concepts and practices relevant to Big Data projects and opportunities. 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
apacheh Administrator Training for Apache Hadoop 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.
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
hadoopadm Hadoop Administration 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
ambari Apache Ambari: Efficiently manage Hadoop clusters 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 Audience DevOps System Administrators DBAs Hadoop testing professionals Format of the course Part lecture, part discussion, exercises and heavy hands-on practice
hadoopdeva Advanced Hadoop for Developers 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%).  
kylin Apache Kylin: From classic OLAP to real-time data warehouse 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, including snowflake schema support, a 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
hadoopdev Hadoop for Developers (4 days) 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.  
hdp Hortonworks Data Platform (HDP) for administrators 21 hours Hortonworks Data Platform is an open-source Apache Hadoop support platform that provides a stable foundation for developing big data solutions on the Apache Hadoop ecosystem. This instructor-led live training introduces Hortonworks 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. Audience Hadoop administrators Format of the course Part lecture, part discussion, exercises and heavy hands-on practice
hadoopba Hadoop for Business Analysts 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.
alluxio Alluxio: Unifying disparate storage systems 7 hours Alexio 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 Alexio 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 Audience Data scientist Developer System administrator Format of the course Part lecture, part discussion, exercises and heavy hands-on practice
hadoopadm1 Hadoop For Administrators 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.
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
hbasedev HBase for Developers 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
datameer Datameer for Data Analysts 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 Audience Data analysts Format of the course Part lecture, part discussion, exercises and heavy hands-on practice
hivehiveql Data Analysis with Hive/HiveQL 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
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

Upcoming Courses

CourseCourse DateCourse Price [Remote / Classroom]
Hadoop for Business Analysts - UppsalaWed, 2018-03-14 09:305450EUR / 7300EUR
Hadoop Administration on MapR - Malmö, StadskärnaMon, 2018-04-09 09:307290EUR / 7490EUR
Weekend Hadoop courses, Evening Hadoop training, Hadoop boot camp, Hadoop instructor-led , Hadoop instructor, Hadoop trainer , Hadoop one on one training , Hadoop on-site, Hadoop private courses, Hadoop training courses,Weekend Hadoop training, Hadoop coaching, Evening Hadoop courses

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Training Material Sample

* Hadoop is designed to work on the commodity hardware Hadoop cluster scales out rather than up * Hadoop is designed for processing local data HDFS is distributed and shared by design * Hadoop is designed to provide data durability RAID introduces additional limitations and overhead * Hadoop worker nodes don't benefit from virtualization Hadoop is a clustering solution which is the opposite to virtualization