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    From 0 to 1: Hive for Processing Big Data

    BY
    Udemy

    Learn how to use Hive's features to your advantage to navigate the issues and strategies associated with big data processing.

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    Mode

    Online

    Fees

    ₹ 649 3499

    Quick Facts

    particular details
    Medium of instructions English
    Mode of learning Self study
    Mode of Delivery Video and Text Based

    Course overview

    Big data processing is a collection of methods or programming models for gaining access to enormous amounts of data and extracting information that can be used to advise and assist management. Big data is used by businesses to enhance operations, deliver better customer service, develop individualized marketing campaigns, and carry out other tasks that can ultimately boost sales and profits. From 0 to 1: Hive for Processing Big Data certification course is designed by Loony Corn - IT Profesional & Instructor, which is delivered by Udemy.

    From 0 to 1: Hive for Processing Big Data online classes offer more than 15.5 hours of extensive lectures supported by 137 downloadable study materials which are designed to help individuals learn how to manage Hive as their data warehousing solution and master the understanding of the techniques and tools available for big data analysis. From 0 to 1: Hive for Processing Big Data online training discusses the techniques and methodologies involved with query optimization, bucketing, partitioning, big data analytics, analytical queries, and more.

    The highlights

    • Certificate of completion
    • Self-paced course
    • 15.5 hours of pre-recorded video content
    • 137 downloadable resources

    Program offerings

    • Online course
    • Learning resources
    • 30-day money-back guarantee
    • Unlimited access
    • Accessible on mobile devices and tv

    Course and certificate fees

    Fees information
    ₹ 649  ₹3,499
    certificate availability

    Yes

    certificate providing authority

    Udemy

    What you will learn

    Java Knowledge of python Knowledge of big data

    After completing the From 0 to 1: Hive for Processing Big Data online certification, individuals will be introduced to the foundational concepts and strategies involved with big data using the functionalities of Apache Hive for big data analytics and big data processing. In this big data processing course, individuals will explore the strategies involved with customizing Hive using the features of Java and Python as well as will acquire knowledge of the fundamentals associated with HDFS and MapReduce. In this big data processing certification, individuals will also learn about strategies involved with analytical queries, query optimization, bucketing, and partitioning.

    The syllabus

    You, Us & This Course

    • You, Us & This Course

    Introducing Hive

    • Hive: An Open-Source Data Warehouse
    • Hive and Hadoop
    • Hive vs Traditional Relational DBMS
    • HiveQL and SQL

    Hadoop and Hive Install

    • Hadoop Install Modes
    • Hadoop Install Step 1 : Standalone Mode
    • Hadoop Install Step 2 : Pseudo-Distributed Mode
    • Hive install
    • Code-Along: Getting started

    Hadoop and HDFS Overview

    • What is Hadoop?
    • HDFS or the Hadoop Distributed File System

    Hive Basics

    • Primitive Datatypes
    • Collections_Arrays_Maps
    • Structs and Unions
    • Create Table
    • Insert Into Table
    • Insert into Table 2
    • Alter Table
    • HDFS
    • HDFS CLI - Interacting with HDFS
    • Code-Along: Create Table
    • Code-Along : Hive CLI

    Built-in Functions

    • Three types of Hive functions
    • The Case-When statement, the Size function, the Cast function
    • The Explode function
    • Code-Along : Hive Built - in functions

    Sub-Queries

    • Quirky Sub-Queries
    • More on subqueries: Exists and In
    • Inserting via subqueries
    • Code-Along : Use Subqueries to work with Collection Datatypes
    • Views

    Partitioning

    • Indices
    • Partitioning Introduced
    • The Rationale for Partitioning
    • How Tables are Partitioned
    • Using Partitioned Tables
    • Dynamic Partitioning: Inserting data into partitioned tables
    • Code-Along : Partitioning

    Bucketing

    • Introducing Bucketing
    • The Advantages of Bucketing
    • How Tables are Bucketed
    • Using Bucketed Tables
    • Sampling

    Windowing

    • Windowing Introduced
    • Windowing - A Simple Example: Cumulative Sum
    • Windowing - A More Involved Example: Partitioning
    • Windowing - Special Aggregation Functions

    Understanding MapReduce

    • The basic philosophy underlying MapReduce
    • MapReduce - Visualized and Explained
    • MapReduce - Digging a little deeper at every step

    MapReduce logic for queries: Behind the scenes

    • MapReduce Overview: Basic Select-From-Where
    • MapReduce Overview: Group-By and Having
    • MapReduce Overview: Joins

    Join Optimizations in Hive

    • Improving Join performance with tables of different sizes
    • The Where clause in Joins
    • The Left Semi Join
    • Map Side Joins: The Inner Join
    • Map Side Joins: The Left, Right and Full Outer Joins
    • Map Side Joins: The Bucketed Map Join and the Sorted Merge Join

    Custom Functions in Python

    • Custom functions in Python
    • Code-Along : Custom Function in Python

    Custom functions in Java

    • Introducing UDFs - you're not limited by what Hive offers
    • The Simple UDF: The standard function for primitive types
    • The Simple UDF: Java implementation for replacetext()
    • Generic UDFs, the Object Inspector and DeferredObjects
    • The Generic UDF: Java implementation for containsstring()
    • The UDAF: Custom aggregate functions can get pretty complex
    • The UDAF: Java implementation for max()
    • The UDAF: Java implementation for Standard Deviation
    • The Generic UDTF: Custom table generating functions
    • The Generic UDTF: Java implementation for namesplit()

    SQL Primer - Select Statemets

    • Select Statements
    • Select Statements 2
    • Operator Functions

    SQL Primer - Group By, Order By and Having

    • Aggregation Operators Introduced
    • The Group By Clause
    • More Group By Examples
    • Order By
    • Having

    SQL Primer - Joins

    • Introduction to SQL Joins
    • Cross Joins aka Cartesian Joins
    • Inner Joins
    • Left Outer Joins
    • RIght, Full Outer Joins, Natural Joins, Self Joins

    Appendix

    • [For Linux/Mac OS Shell Newbies] Path and other Environment Variables
    • Setting up a Virtual Linux Instance - For Windows Users

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