This exercise aims to get you to: • Install and configure HBase, and Manage data using HBase Shell • Install and configure Hive, and Manage data using Hive HBase Installation and Configuration 1. Download HBase 2.4.8 $ wget https://dlcdn.apache.org/hbase/2.4.8/hbase-2.4.8-bin.tar.gz Then unpack the package: $ tar xvf hbase-2.4.8-bin.tar.gz 2. Define environment variables for HBase We need […]
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Write Self-Contained Spark Applications in Eclipse 0. Download and configure Java 8 The Scala-IDE only supports Java 8, but the VM installed Java 11. Please follow the below steps to install and configure Java 8. $ sudo apt install openjdk-8-jdk $ sudo update-alternatives –config java You will see both Java 8 and Java 11, please
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COMP9313: Big Data Management Course web site: http://www.cse.unsw.edu.au/~cs9313/ Chapter 5.2: V Part 1: APIs A Brief Review of RDD ❖ The RDD is the most basic abstraction in Spark. There are three vital characteristics associated with an RDD: ➢ Dependencies (lineage) When necessary to reproduce results, Spark can recreate an RDD from the dependencies
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This exercise aims to get you to apply the design patterns you have learned in Chapter 2.2 in MapReduce programming. If you have not finished solving the problems in Lab 2, please keep working on them first and then move to Lab 3. Create a folder ¡°Lab3¡±. Put all your codes written in this week¡¯s
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This exercise aims to get you to apply the order inversion and value-to-key conversion design patterns for MapReduce programming you have learned in Chapter 3.1. If you have not finished solving the problems in Lab 3, please keep working on them, and then move to Lab 4. Create a project “Lab4” in Eclipse, and create
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COMP9313: Big Data Management Course web site: http://www.cse.unsw.edu.au/~cs9313/ Chapter 4: MapReduce IV Graph Data Processing in MapReduce What’s a Graph? ❖ G = (V,E), where ➢ V represents the set of vertices (nodes) ➢ E represents the set of edges (links) ➢ Both vertices and edges may contain additional information ❖ Different types of graphs:
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COMP9313 2021T3 Project 3 (20 marks) Set Similarity Join Using Spark on Google Dataproc Problem Definition: Given two collections of records R and S, a similarity function sim(., .), and a threshold τ, the set similarity join between R and S, is to find all record pairs r (from R) and s (from S), such
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This exercise aims to get you to: • Package self-contained Spark applications using sbt • practice more on using RDD Background The detailed Spark programming guide is available at: http://spark.apache.org/docs/latest/programming-guide.html The RDD transformation and action functions examples are available at: http://homepage.cs.latrobe.edu.au/zhe/ZhenHeSparkRDDAPIExamples.html The documentation of sbt is at: http://www.scala-sbt.org/1.x/docs/index.html A tutorial of Scala is available at:
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This exercise aims to get you to: • Install and configure Hadoop MapReduce • Practice HDFS operations • Test Hadoop MapReduce with the pseudo-distributed mode Background In the examples below, we have used the $ sign to represent the prompt from the command interpreter (shell). The actual prompt may look quite different on your computer
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COMP9313: Big Data Management Course web site: http://www.cse.unsw.edu.au/~cs9313/ Chapter 1.2 Introduction to HDFS, YARN, and MapReduce Part 1: HDFS File System ❖ A filesystem is the methods and data structures that an operating system uses to keep track of files on a disk or partition; that is, the way the files are organized on the
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