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In this course you will learn about the Apache Hive which is an open-source data that is specifically designed to support the analysis of large datasets.

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Last Updated

January 27, 2021

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Course Description

Apache Hive is a distributed data warehouse system that enables analytics at a massive scale. A data warehouse provides a central store of information that can easily be analyzed to make informed, data driven decisions. Apache Hive is an open source data warehouse software for reading, writing and managing large data set files. In present  hadoop has the advantage of being one of the most widespread technologies when it comes to large amounts of Big Data.  Apache Hive is used for data analysis and summarization. Hive has such functions that offer effective ways of solving problems. Apache Hive lets you work with Hadoop in a very effective way. Hive has a advantage of deploying high-speed data reads and writes within data warehouses while managing large datasets distributed across multiple locations. Multiple users can query the data with the help of this. 

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Course Curriculum

    • Theory 00:00:00
    • Quick Introduction to Hadoop 00:00:00
    • Why Hadoop? 00:00:00
    • Quick Introduction to Hadoop Distributed File System 00:00:00
    • Block Placement in HDFS 00:00:00
    • HDFS Architecture 00:00:00
    • Introduction to MapReduce 00:00:00
    • Architecture of MapReduce 00:00:00
    • Processing Data with MapReduce 00:00:00
    • 3V’s of Hadoop 00:00:00
    • Introduction to Hive 00:00:00
    • Hive Query Language 00:00:00
    • Task 1: Download Cloudera CDH Quickstart VM 00:00:00
    • Task 2: Download and Install VMWare 00:00:00
    • Task 3: Getting Started with CDH Quickstart VM 00:00:00
    • Task 4: Creating a table in Hive 00:00:00
    • Task 5: Loading data in Hive Table 00:00:00
    • Task 6: Running Hive Queries 00:00:00
      • Theory 00:00:00
      • Hive Architecture 00:00:00
      • Hive Query Process Fow 00:00:00
      • Data Types in Hive 00:00:00
      • Primitive Data Types 00:00:00
      • Delimiters in Hive 00:00:00
        • Task 1: Creating table using Primitive Numeric Types 00:00:00
        • Task 2: Creating table using other Primitive Types 00:00:00
        • Task 3: Creating table using Complex Data Types 00:00:00
        • Task 4: Querying collections in a Hive table 00:00:00
        • Task 5: Using Type Casting 00:00:00
          • Theory 00:00:00
          • Comparing Hive with RDBMS 00:00:00
          • Data Model in Hive 00:00:00
          • Data Definition Language operators 00:00:00
          • Data Manipulation Language operators 00:00:00
          • Hive Database 00:00:00
          • Task 1: Database Operations 00:00:00
          • Task 2: Creating a Managed Hive table 00:00:00
          • Task 3: Creating an External Hive table 00:00:00
          • Task 4: Using Partitions 00:00:00
          • Task 5: Using Bucketing 00:00:00
          • Task 6: Altering Tables 00:00:00
            • Theory 00:00:00
            • Hive Operators 00:00:00
            • Hive Functions 00:00:00
            • HiveQL Statements 00:00:00
              • Task 1: Using SELECT Statements 00:00:00
              • Task 2: Using WHERE Clause 00:00:00
              • Task 3: Using GROUP BY Clause 00:00:00
              • Task 4: Using ORDER BY & SORT BY Clauses 00:00:00
              • Task 5: Performing Joins – I 00:00:00
              • Task 6:Performing Joins – II 00:00:00
                • Theory 00:00:00
                • Hive Views 00:00:00
                • Hive Indexes 00:00:00
                • User Defined Functions 00:00:00
                • Performance Tuning 00:00:00
                • Task 1: Creating Views 00:00:00
                • Task 2: Creating Indexes 00:00:00
                • Task 3: Writing a UDF 00:00:00
                • Task 4: Using EXPLAIN 00:00:00
                • Task 5: Using EXPLAIN EXTENDED 00:00:00
                • Task 6: Setting Configurations 00:00:00

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