5 días – 28 horas


Cloudera University’s four-day Data Analyst Training course will teach you to apply traditional data analytics and business intelligence skills to big data. This course presents the tools data professionals need to access, manipulate, transform, and analyze complex data sets using SQL and familiar scripting languages.
Advance Your Ecosystem Expertise.
Apache Hive makes transformation and analysis of complex, multi-structured data scalable in Cloudera environments. 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.


Through instructor-led discussion and interactive, hands-on exercises, participants will navigate the ecosystem, learning:

  1. How the open source ecosystem of big data tools addresses challenges not met by traditional RDBMSs
  2. Using Apache Hive and Apache Impala to provide SQL access to data
  3. Hive and Impala syntax and data formats, including functions and subqueries
  4. Create, modify, and delete tables, views, and databases; load data and store results of queries
  5. Create and use partitions and different formats.
  6. Combining two or more datasets using JOIN or UNION, as appropriate
  7. What analytic and windowing functions are, and how to use them
  8. Store and query complex or nested data structures
  9. Process and analyze semi-structured and unstructured data
  10. Techniques for optimizing Hive and Impala queries
  11. Extending the capabilities of Hive and Impala using parameters, custom file formats and SerDes, and external scripts
  12. How to determine whether Hive, Impala, an RDBMS, or a mix of these is best for a given task

Contenidos del curso

  1. Introduction
  2. 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
  3. 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
  4. 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
  5. Common Operators and Built-In Functions
    • Operators
    • Scalar Functions
    • Aggregate Functions
  6. Data Management
    • Data Storage
    • Creating Databases and Tables
    • Loading Data
    • Altering Databases and Tables
    • Simplifying Queries with Views
    • Storing Query Results
  7. 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
  8. Working with Multiple Datasets
    • UNION and Joins
    • Handling NULL Values in Joins
    • Advanced Joins
  9. Analytic Functions and Windowing
    • Using Common Analytic Functions
    • Other Analytic Functions
    • Sliding Windows
  10. Complex Data
    • Complex Data with Hive
    • Complex Data with Impala
  11. Analyzing Text
    • Using Regular Expressions with Hive and Impala
    • Processing Text Data with SerDes in Hive
    • Sentiment Analysis and n-grams
  12. Apache Hive Optimization
    • Understanding Query Performance
    • Bucketing
    • Hive on Spark
  13. Apache Impala Optimization
    • How Impala Executes Queries
    • Improving Impala Performance
  14. Extending Apache Hive and Impala
    • Custom SerDes and File Formats in Hive
    • Data Transformation with Custom Scripts in Hive
    • User-Defined Functions
    • Parameterized Queries
  15. Choosing the Best Tool for the Job
    • Comparing Hive, Impala, and Relational Databases
    • Which to Choose?
  16. Conclusion

Información extra

¿A quién está dirigido?

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.

Certificación de Administrador

Upon completion of the course, attendees are encouraged to continue their study and register for the CCA Data Analyst exam. Certification is a great differentiator. It helps establish you as a leader in the field, providing employers and customers with tangible evidence of your skills and expertise.

Información general

Área formativa

Cloudera Analista de Datos






Propia del fabricante

Garantía juvenil



He leído y acepto la del sitio y las condiciones de tratamiento de mis datos de carácter personal



Fecha y precios de nuestros cursos

Fecha Horario de impartición Ubicación Precio por alumno Previsto en fecha
09 Sep 2019
de 9:00 a 14:35h
Core Madrid
1670€ + IVA
16 Dec 2019
De 9.00 a 14.35h
Core Madrid
1670€ + IVA

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