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Big data in practice using Hadoop


Nowadays everybody seems to be working with "big data". Do you also want to interrogate your several data sources (click streams, social media, relational data, sensor data, ...) and are you experiencing the shortcomings of traditional data tools? Maybe you are in need of distributed data stores like HDFS and a MapReduce infrastructure like Hadoop's.

This course builds on the concepts which are set forth in the Big data concepts course. you will get hands-on practice on linux with Apache Hadoop: HDFS, Yarn, Pig, and Hive. You learn how to implement robust data processing with an SQL-style interface which generates MapReduce jobs. You also learn to work with the graphical tools which allow for easy follow-up of the jobs and the workflows on the distributed Hadoop cluster.

After successful completion of the course, you will have sufficient basic expertise to set up a Hadoop cluster, to import data into HDFS, and to interrogate it clevery using MapReduce.

When you want to use Hadoop with Spark, you are referred to the course Big data in practice using Spark.

Main topics

  • Motivation for Hadoop & base concepts
  • The Apache Hadoop project and the components of Hadoop
  • HDFS: the Hadoop Distributed File System
  • MapReduce: what and how
  • The workings of a Hadoop cluster
  • Writing a MapReduce program
  • Implementing MapReduce drivers, mappers, and reducers in Java
  • Writing Mappers and Reducers by use of an other progamming or scripting language (e.g. Perl)
  • Unit testing
  • Writing partitioners for optimizing the load balancing
  • Debugging a MapReduce program
  • Data Input / Output
  • Reading and writing sequential data from a MapReduce program
  • The use of binary data
  • Data compression
  • Some frequently used MapReduce components
  • Sorting, searching, and indexing of data
  • Word counts and counting pairs of words
  • Working with Hive and Pig
  • Pig as a high-level basic interface for letting generate a sequence of MapReduce jobs
  • Hive as a high-level SQL-style interface for letting generate a sequence of MapReduce jobs
  • Short introduction to HBase and Cassandra as alternative data stores

Intended for

Whoever wants to start practising "big data": developers, data architects, and anyone who needs to work with big data technology.


Familiarity with the concepts of data stores and more specifically of "big data" is necessary; see our course Big data concepts. Additionally, minimal knowledge of SQL, UNIX and Java are useful. Experience with a programming language (Java, PHP, Python, Perl, C++ or C#) is a must.

Training method

Classroom instruction, with practical examples and supported by extensive practical exercises.

Course leader

Peter Vanroose.


2 days.


You can enrol by clicking on a date
datedur.lang.  location  price
02 Nov2NWoerden  (NL)1000 EUR  (exempt from VAT) 
20 Nov2?Leuven  (BE)1000 EUR  (excl. VAT) 

Global score

4.1/5 (based on 26 evaluations)


Zeer goede introductie (, )
Interessante kennismaking. Voor mij soms te veel theorie (, )
Prima cursus, goede basis voor het opzetten hadoop kennis (, )
Een dag langer? (, )
De meeste belangrijke punten zijn behandeld in de cursus. (, )
goed overzicht van big data architectuur en de samenhang tussen producten en tools (, )
Redelijk veel info voor de beschikbare periode (, )
Wel ok, ik denk dat de algemene uitleg veel sneller kan. Soms veel focus op details die voor mij bijna irrelevant lijken. Kan ook aan mij liggen. (, )
Goed om een overzicht te krijgen (, )
Happy with the training even if I would spend less time on HDFS and MapReduce and more time in others components (Pig, Hive,...) (, )

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