Training Cloudera

Training goals dlearning

code: CL-DAT

This four-day data analyst training course focusing on Apache Pig and Hive and Cloudera Impala will teach you to apply traditional data analytics and business intelligence skills to big data. Cloudera presents the tools data professionals need to access, manipulate, transform, and analyze complex data sets using SQL and familiar scripting languages.

Apache Hive makes multi-structured data accessible to analysts, database administrators, and others without Java programming expertise. Apache Pig applies the fundamentals of familiar scripting languages to the Hadoop cluster. Cloudera Impala enables real-time interactive analysis of the data stored in Hadoop via a native SQL environment.

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

  • The features that Pig, Hive, and Impala offer for data acquisition, storage, and analysis
  • The fundamentals of Apache Hadoop and data ETL (extract, transform, load), ingestion, and processing with Hadoop tools
  • How Pig, Hive, and Impala improve productivity for typical analysis tasks
  • Joining diverse datasets to gain valuable business insight
  • Performing real-time, complex queries on datasets

Conspect Show list

  1. Introduction
  2. Hadoop Fundamentals
    • The Motivation for Hadoop
    • Hadoop Overview
    • Data Storage: HDFS
    • Distributed Data Processing: YARN, MapReduce and Spark
    • Data Processing and Analysis: Pig, Hive and Impala
    • Data Integration: Sqoop
    • Other Hadoop Data Tools
    • Exercise Scenarios Explanation
  3. Introduction to Pig
    • What Is Pig?
    • Pig’s Features
    • Pig Use Cases
    • Interacting with Pig
  4. Basic Data Analysis with Pig
    • Pig Latin Syntax
    • Loading Data
    • Simple Data Types
    • Field Definitions
    • Data Output
    • Viewing the Schema
    • Filtering and Sorting Data
    • Commonly-Used Functions
  5. Processing Complex Data with Pig
    • Storage Formats
    • Complex/Nested Data Types
    • Grouping
    • Built-In Functions for Complex Data
    • Iterating Grouped Data
  6. Multi-Dataset Operations with Pig
    • Techniques for Combining Data Sets
    • Joining Data Sets in Pig
    • Set Operations
    • Splitting Data Sets
  7. Pig Troubleshooting and Optimization
    • Troubleshooting Pig
    • Logging
    • Using Hadoop’s Web UI
    • Data Sampling and Debugging
    • Performance Overview
    • Understanding the Execution Plan
    • Tips for Improving the Performance of Your Pig Jobs
  8. Introduction to Hive and Impala
    • What Is Hive?
    • What Is Impala?
    • Schema and Data Storage
    • Comparing Hive to Traditional Databases
    • Hive Use Cases
  9. Querying with Hive and Impala
    • Databases and Tables
    • Basic Hive and Impala Query Language Syntax
    • Data Types
    • Differences Between Hive and Impala Query Syntax
    • Using Hue to Execute Queries
    • Using the Impala Shell
  10. Data Management
    • Data Storage
    • Creating Databases and Tables
    • Loading Data
    • Altering Databases and Tables
    • Simplifying Queries with Views
    • Storing Query Results
  11. Data Storage and Performance
    • Partitioning Tables
    • Choosing a File Format
    • Managing Metadata
    • Controlling Access to Data
  12. Relational Data Analysis with Hive and Impala
    • Joining Datasets
    • Common Built-In Functions
    • Aggregation and Windowing
  13. Working with Impala
    • How Impala Executes Queries
    • Extending Impala with User-Defined Functions
    • Improving Impala Performance
  14. Analyzing Text and Complex Data with Hive
    • Complex Values in Hive
    • Using Regular Expressions in Hive
    • Sentiment Analysis and N-Grams
    • Conclusion
  15. Hive Optimization
    • Understanding Query Performance
    • Controlling Job Execution Plan
    • Bucketing
    • Indexing Data
  16. Extending Hive
    • SerDes
    • Data Transformation with Custom Scripts
    • User-Defined Functions
    • Parameterized Queries
  17. Choosing the Best Tool for the Job
    • Comparing MapReduce, Pig, Hive, Impala and Relational Databases
    • Which to Choose?
  18. Conclusion
Download conspect training as PDF

Additional information

Requirements
  • 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.
Difficulty level
Duration 4 days
Certificate

The participants will obtain certificates signed by Cloudera.

Trainer

Certified Cloudera Instructor.

Cloudera show more courses
Training thematically related

Big Data

Big Data Analysis

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Traditional training

Sessions organised at Compendium CE are usually held in our locations in Kraków and Warsaw, but also in venues designated by the client. The group participating in training meets at a specific place and specific time with a coach and actively participates in laboratory sessions.

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You may participate from at any place in the world. It is sufficient to have a computer (or, actually a tablet, or smartphone) connected to the Internet. Compendium CE provides each Distance Learning training participant with adequate software enabling connection to the Data Center. For more information, please visit dlearning.eu site

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