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Cloudera Data Analyst Training: Using Pig, Hive and Impala with Hadoop (CDAPHIH)
New Age Technologies has been delivering Authorized Training since 1996. We offer Cloudera’s full suite of authorized courses including courses pertaining to Spark, Apache Hadoop, HBase, MapReduce, Data Science, Cloudera Data Analyst and more. If you have any questions or can’t seem to find the Cloudera class that you are interested in, contact one of our Cloudera Training Specialists. Invest in your future today with Cloudera training from New Age Technologies.
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Cloudera Data Analyst Training: Using Pig, Hive and Impala with Hadoop (CDAPHIH) Overview:
The Cloudera Data Analyst Training: Using Pig, Hive and Impala with Hadoop (CDAPHIH) hands-on course is for anyone who wants to access, manipulate, transform, and analyze massive data sets in the Hadoop cluster using SQL and familiar scripting languages. It focuses on Apache Pig and Hive and Cloudera Impala which 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.
Cloudera’s Hadoop Ecosystem -> Hive, Pig, & Impala:
- 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
Cloudera Data Analyst Training: Using Pig, Hive and Impala with Hadoop (CDAPHIH) Prerequisites:
Before attending this course, you must have the following:
- 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
Cloudera Data Analyst Training: Using Pig, Hive and Impala with Hadoop (CDAPHIH) Objectives:
After successfully completing this course, you will be able to:
- Understand the features that Pig, Hive, and Impala offer for data acquisition, storage, and analysis
- Use the fundamentals of Apache Hadoop and data ETL (extract, transform, load), ingestion, and processing with Hadoop tools
- Use Pig, Hive, and Impala to improve productivity for typical analysis tasks
- Join diverse datasets to gain valuable business insight
- Perform real-time, complex queries on datasets
Cloudera Data Analyst Training: Using Pig, Hive and Impala with Hadoop (CDAPHIH) Outline:
Module 1: 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
Module 2: Introduction to Pig
- What Is Pig?
- Pig’s Features
- Pig Use Cases
- Interacting with Pig
Module 3: 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
Module 4: Processing Complex Data with Pig
- Storage Formats
- Complex/Nested Data Types
- Grouping
- Built-In Functions for Complex Data
- Iterating Grouped Data
Module 5: Multi-Dataset Operations with Pig
- Techniques for Combining Data Sets
- Joining Data Sets in Pig
- Set Operations
- Splitting Data Sets
Module 6: 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
Module 7: Introduction to Hive and Impala
- What Is Hive?
- What Is Impala?
- Schema and Data Storage
- Comparing Hive to Traditional Databases
- Hive Use Cases
Module 8: 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
Module 9: Data Management
- Data Storage
- Creating Databases and Tables
- Loading Data
- Altering Databases and Tables
- Simplifying Queries with Views
- Storing Query Results
Module 10: Data Storage and Performance
- Partitioning Tables
- Choosing a File Format
- Managing Metadata
- Controlling Access to Data
Module 11: Relational Data Analysis with Hive and Impala
- Joining Datasets
- Common Built-In Functions
- Aggregation and Windowing
Module 12: Working with Impala
- How Impala Executes Queries
- Extending Impala with User-Defined Functions
- Improving Impala Performance
Module 13: Analyzing Text and Complex Data with Hive
- Complex Values in Hive
- Using Regular Expressions in Hive
- Sentiment Analysis and N-Grams
- Conclusion
Module 14: Hive Optimization
- Understanding Query Performance
- Controlling Job Execution Plan
- Bucketing
- Indexing Data
Module 15: Extending Hive
- SerDes
- Data Transformation with Custom Scripts
- User-Defined Functions
- Parameterized Queries
Module 16: Choosing the Best Tool for the Job
- Comparing MapReduce, Pig, Hive, Impala, and Relational Databases
- Which to Choose?
