Cloudera Data Analyst Training

28 hours
1840,00 €
Classroom or Live Virtual Class
Classroom or Live Virtual Class


This course focuses on Apache Hive and Cloudera Impala, and aims to teach students how to apply traditional data analysis and gain the ability to manage business intelligence tools for Big Data. Cloudera presents data on the tools professionals need to access, manipulate, transform and analyze complex data sets using SQL and similar scripting languages.

Apache Hive makes multi-structured data accessible to analysts, database administrators, and other people without knowledge of Java programming. Cloudera Impala enables real-time interactive analysis of data stored in Hadoop through a native SQL environment.

PUE, Cloudera Strategic Partner, is authorized by this multinational to provide official training in Cloudera technologies.

PUE is also accredited and recognized to carry out consulting and mentoring services in the implementation of Cloudera solutions in the business field with the added value in the practical and business approach to knowledge that is translated in its official courses.

Audience and prerequisites

This course is designed for data analysts, business intelligence specialists, developers, system architects, and database administrators. Knowledge of SQL is assumed, as is basic Linux command-line familiarity. Knowledge of at least one scripting language (e.g., Bash scripting, Perl, Python, Ruby) would be helpful but is not essential. Prior knowledge of Apache Hadoop is not required.


At the end of the training, the participant will know:

  • How the open source ecosystem of big data tools addresses challenges not met by traditional RDBMSs.
  • Using Apache Hive and Apache Impala to provide SQL access to data.
  • Hive and Impala syntax and data formats, including functions and subqueries.
  • Create, modify, and delete tables, views, and databases; load data; and store results of queries.
  • Create and use partitions and different file formats.
  • Combining two or more datasets using JOIN or UNION, as appropriate.
  • What analytic and windowing functions are, and how to use them.
  • Store and query complex or nested data structures.
  • Process and analyze semi-structured and unstructured data.
  • Techniques for optimizing Hive and Impala queries.
  • Extending 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.



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
  • Data Integration: Sqoop
  • Other Hadoop Data Tools
  • Exercise Scenario Explanation

Introduction to Hive and Impala

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

Querying with 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 Common 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

Hive Optimization

  • Understanding Query Performance
  • Bucketing
  • Hive on Spark

Impala Optimization

  • How Impala Executes Queries
  • Improving Impala Performance

Extending 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 MapReduce, Hive, Impala, and Relational Databases
  • Which to Choose?


Apache Kudu

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

Open calls