• Database Training

    databaseInterSource offers live instructor-led courses on all important database programming technologies, including Crystal Reports, Microsoft Access, MySQL, Oracle, SQL, SQL Server, SSAS, SSIS, SSRS and Xcelsius.

    These live classes are offered both on client sites, at our Geneva training center, and via a Web interface.

  • About Database

    A database is a collection of data stored and maintained for one or more uses. Most modern databases are managed by a Database Management System (DBMS), a set of computer programs that controls the creation, maintenance, and the use of the database with computer as a platform or of an organization and its end users. It allows organizations to place control of organization-wide database development in the hands of database administrators (DBAs) and other specialists.

    The proper integration of databases can dramatically increase the functionality of all types of applications, whether or not Web-enabled.

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  • Course Details Database

    Classes are offered at client sites, at our Geneva training center, and via a live web conference. For detailed course outlines and scheduled classes, please see below.

    To book training, navigate to the course you need, then:

    • For scheduled online classes, register from the choices indicated.
    • If you need an alternative dates, time or location, or if you want a live classroom course, click on “request an offer for this course,” to complete the form.

    InterSource clients are active globally. Live web courses run during Eastern Standard Time (New York) business hours and are priced in US dollars.

    We also run live web conference classes during European business hours, which can be invoiced in local currencies. To discuss your requirements please contact us on +41 (22) 958 0114.

Cloudera Data Analyst Training

Course duration

  • 4 days

Course Benefits

  • 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

Course Outline

  1. Apache Hadoop Fundamentals
    1. The Motivation for Hadoop
    2. Hadoop Overview
    3. Data Storage: HDFS
    4. Distributed Data Processing: YARN, MapReduce, and Spark
    5. Data Processing and Analysis: Hive and Impala
    6. Database Integration: Sqoop
    7. Other Hadoop Data Tools
    8. Exercise Scenario Explanation
  2. Introduction to Apache Hive and Impala
    1. What Is Hive?
    2. What Is Impala?
    3. Why Use Hive and Impala?
    4. Schema and Data Storage
    5. Comparing Hive and Impala to Traditional Databases
    6. Use Cases
  3. Querying with Apache Hive and Impala
    1. Databases and Tables
    2. Basic Hive and Impala Query Language Syntax
    3. Data Types
    4. Using Hue to Execute Queries
    5. Using Beeline (Hive's Shell)
    6. Using the Impala Shell
  4. Common Operators and Built-In Functions
    1. Operators
    2. Scalar Functions
    3. Aggregate Functions
  5. Data Management
    1. Data Storage
    2. Creating Databases and Tables
    3. Loading Data
    4. Altering Databases and Tables
    5. Simplifying Queries with Views
    6. Storing Query Results
  6. Data Storage and Performance
    1. Partitioning Tables
    2. Loading Data into Partitioned Tables
    3. When to Use Partitioning
    4. Choosing a File Format
    5. Using Avro and Parquet File Formats
  7. Working with Multiple Datasets
    1. UNION and Joins
    2. Handling NULL Values in Joins
    3. Advanced Joins
  8. Analytic Functions and Windowing
    1. Using Analytic Functions
    2. Other Analytic Functions
    3. Sliding Windows
  9. Complex Data
    1. Complex Data with Hive
    2. Complex Data with Impala
  10. Analyzing Text
    1. Using Regular Expressions with Hive and Impala
    2. Processing Text Data with SerDes in Hive
    3. Sentiment Analysis and n-grams in Hive
  11. Apache Hive Optimization
    1. Understanding Query Performance
    2. Bucketing
    3. Hive on Spark
    4. Apache Impala Optimization
    5. How Impala Executes Queries
    6. Improving Impala Performance
  12. Extending Apache Hive and Impala
    1. Custom SerDes and File Formats in Hive
    2. Data Transformation with Custom Scripts in Hive
    3. User-Defined Functions
    4. Parameterized Queries
  13. Choosing the Best Tool for the Job
    1. Comparing Hive, Impala, and Relational Databases
    2. Which to Choose?
  14. Conclusion
    1. Apache Kudu
    2. What Is Kudu?
    3. Kudu Tables
    4. Using Impala with Kudu

Class Materials

Each student will receive a comprehensive set of materials, including course notes and all the class examples.

Class Prerequisites

Experience in the following is required for this Hadoop class:

  • Some knowledge of SQL.
  • Basic Linux command-line familiarity. .
Since its founding in 1995, InterSource has been providing high quality and highly customized training solutions to clients worldwide. With over 500 course titles constantly updated and numerous course customization and creation possibilities, we have the capability to meet your I.T. training needs.
Instructor-led courses are offered via a live Web connection, at client sites throughout Europe, and at our Geneva Training Center.