Big data in practice using Hadoop (EN/NL/FR)

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computer Online:
15 nov. 2021 tot 16 nov. 2021

Beschrijving

This 2 day ABIS course builds on the concepts which are set forth in the Big data architecture and infrastructure 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.

Intended for whoever wants to start practising "big data" (Hadoop); Familiarity with the concepts of data stores and  "big data" is necessary in order to attend this cours, and minimal knowledge of SQL, UNIX and Java is usefull.

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Nog niet gevonden wat je zocht? Bekijk deze onderwerpen: Apache Hadoop, Big Data, Data warehouse, Apache en Data mining.

This 2 day ABIS course builds on the concepts which are set forth in the Big data architecture and infrastructure 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.

Intended for whoever wants to start practising "big data" (Hadoop); Familiarity with the concepts of data stores and  "big data" is necessary in order to attend this cours, and minimal knowledge of SQL, UNIX and Java is usefull.

Remark: Course description in English; Dutch and French versions are available on the ABIS website. Courses are planned in Dutch, English, and French. Consult the ABIS website for alternate course formats.

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, which will generate a sequence of MapReduce jobs for us
    • Hive as a high-level SQL-style interface, which generates a sequence of MapReduce jobs
  • The Parquet file format: structure and typical use; advantages of data compression; interoperability
  • Short introduction to HBase and Cassandra as alternative data stores

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

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

Training Method/Didactics: Classroom instruction, with practical examples and supported by extensive practical exercises.

Duration: 2 days.

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