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Cloudera Data Analyst Training

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Tuition (CAD) N/A
Tuition (USD) 3195.00

Apache Hive makes transformation and analysis of complex, multi-structured data scalable in Hadoop. Apache Impala enables real-time interactive analysis of the data stored in Hadoop using a native SQL environment. Together, they make multi-structured data accessible to analysts, database administrators, and others without Java programming expertise.

Who Can Benefit

  • This course is designed for data analysts, business intelligence specialists, developers, system architects, and database administrators.

Skills Gained

  • How the open source ecosystem of big data tools addresses challenges not met by traditional RDBMSs
  • How Apache Hive and Apache Impala are used to provide SQL access to data
  • How Hive and Impala syntax and data formats, including functions and subqueries, help answer questions about data
  • How to create, modify, and delete tables, views, and databases; load data; and store results of queries
  • How to create and use partitions and different file formats
  • How to combine two or more datasets using JOIN or UNION, as appropriate
  • What analytic and windowing functions are, and how to use them
  • How to store and query complex or nested data structures
  • How to process and analyze semi-structured and unstructured data
  • Different techniques for optimizing Hive and Impala queries
  • How to extend the capabilities of Hive and Impala using parameters, custom file formats and SerDes, and external scripts
  • How to determine whether Hive, Impala, an RDBMS, or a mix of these is best for a given task


  • This course is designed for data analysts, business intelligence specialists, developers, system architects, and database administrators. Some knowledge of SQL is assumed, as is basic Linux command-line familiarity. Prior knowledge of Apache Hadoop is not required.

Course Content


    Apache Hadoop Fundamentals

    • The Motivation for Hadoop
    • Hadoop Overview
    • Data Storage: HDFS
    • Distributed Data Processing: YARN, MapReduce, and Spark
    • Data Processing and Analysis: Hive and Impala
    • Database Integration: Sqoop
    • Other Hadoop Data Tools
    • Exercise Scenario Explanation

    Introduction to Apache Hive and Impala

    • What Is Hive?
    • What Is Impala?
    • Why Use Hive and Impala?
    • Schema and Data Storage
    • Comparing Hive and Impala to Traditional Databases
    • Use Cases

    Querying with Apache Hive and Impala

    • Databases and Tables
    • Basic Hive and Impala Query Language Syntax
    • Data Types
    • Using Hue to Execute Queries
    • Using Beeline (Hive's Shell)
    • Using the Impala Shell

    Common Operators and Built-In Functions

    • Operators
    • Scalar Functions
    • Aggregate Functions

    Data Management

    • Data Storage
    • Creating Databases and Tables
    • Loading Data
    • Altering Databases and Tables
    • Simplifying Queries with Views
    • Storing Query Results

    Data Storage and Performance

    • Partitioning Tables
    • Loading Data into Partitioned Tables
    • When to Use Partitioning
    • Choosing a File Format
    • Using Avro and Parquet File Formats

    Working with Multiple Datasets

    • UNION and Joins
    • Handling NULL Values in Joins
    • Advanced Joins

    Analytic Functions and Windowing

    • Using Analytic Functions
    • Other Analytic Functions
    • Sliding Windows

    Complex Data

    • Complex Data with Hive
    • Complex Data with Impala

    Analyzing Text

    • Using Regular Expressions with Hive and Impala
    • Processing Text Data with SerDes in Hive
    • Sentiment Analysis and n-grams in Hive

    Apache Hive Optimization

    • Understanding Query Performance
    • Cost-Based Optimization and Statistics
    • Bucketing
    • ORC File Optimizations

    Apache Impala Optimization

    • How Impala Executes Queries
    • Improving Impala Performance

    Extending Apache Hive and Impala

    • Custom SerDes and File Formats in Hive
    • Data Transformation with Custom Scripts in Hive
    • User-Defined Functions
    • Parameterized Queries

    Choosing the Best Tool for the Job

    • Comparing Hive, Impala, and Relational Databases
    • Which to Choose?


    • Apache Kudu
    • What Is Kudu?
    • Kudu Tables
    • Using Impala with Kudu

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