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Hive and MapReduce: A Comprehensive Two-Day Course, Exams of Architecture

A detailed curriculum for a two-day course on hive and mapreduce. The course covers the basics of hadoop distributed file system (hdfs), hive's architecture and usage, mapreduce process and components, and various hiveql queries and data manipulation techniques. Students will learn how to run mapreduce programs, create tables, load data, and execute queries. The document also includes sections on hive views, indexes, and debugging.

What you will learn

  • What are the key topics covered in the MapReduce section of the course?
  • What is the purpose of the Hive course outlined in the document?
  • How can students load data into Hive tables according to the document?

Typology: Exams

2021/2022

Uploaded on 09/12/2022

heathl
heathl 🇺🇸

4.5

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HIVE
Duration: 2 Days
Pre-requisite:
Knowledge of SQL, data modeling, and scripting is also helpful. No prior Hadoop Knowledge
is needed
Hadoop Distributed File System (HDFS)
• HDFS overview and design
• HDFS architecture
• HDFS file storage
• Component failures and recoveries
• Block placement
Map-Reduce Abstraction
• What MapReduce is and why it is popular
• The Big Picture of the MapReduce
• MapReduce process and terminology
• MapReduce components failures and recoveries
• Working with MapReduce
• Lab: Working with MapReduce
Hive
Overview of Hive
v Why Hive?
v Use cases
v Hive architecture – building blocks
v Hive CLI and language (exercise)
v Variables and Properties
v Executing Hive Queries from Files
v Primitive Data Types
v Collection Data Types
v Difference between HivelQL and SQL92
v Custom UDF
HiveQL Queries
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pf4

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HIVE

Duration: 2 Days Pre-requisite:

  • Knowledge of SQL, data modeling, and scripting is also helpful. No prior Hadoop Knowledge is needed Hadoop Distributed File System (HDFS)
  • HDFS overview and design
  • HDFS architecture
  • HDFS file storage
  • Component failures and recoveries
  • Block placement Map-Reduce Abstraction
  • What MapReduce is and why it is popular
  • The Big Picture of the MapReduce
  • MapReduce process and terminology
  • MapReduce components failures and recoveries
  • Working with MapReduce
  • Lab: Working with MapReduce Hive Overview of Hive v Why Hive? v Use cases v Hive architecture – building blocks v Hive CLI and language (exercise) v Variables and Properties v Executing Hive Queries from Files v Primitive Data Types v Collection Data Types v Difference between HivelQL and SQL v Custom UDF HiveQL Queries

v SELECT … FROM Clauses

  • Specify Columns with Regular Expressions
  • Computing with Column Values
  • Arithmetic Operators
  • Using Mathematical, Aggregate, Table generating and other built-in functions
  • LIMIT Clause
  • Column Aliases
  • Nested SELECT Statements
  • CASE … WHEN … THEN Statements v WHERE Clauses
  • Predicate Operators
  • Gotchas with Floating-Point Comparisons
  • LIKE and RLIKE v GROUP BY Clauses
  • HAVING Clauses HiveQL: Data Manipulation v Hive Variables v Partitioned, Managed Tables v External Partitioned Tables v Loading Data into Managed Tables v Inserting Data into Tables from Queries v Dynamic Partition Inserts v Creating Tables and Loading Them in One Query v Bucketing Data **Honds-On
  1. Running map reduce program
  2. Creating Static, Dynamic Partition and temporary table in hive
  3. Loading data into tables
  4. Running queries and storing the results**
  5. ORC file , Parquet file example

v Sequence File Record Formats: SerDes v CSV and TSV SerDes v JSON SerDe v Avro Hive SerDe