Take your knowledge to the next level with Cloudera’s Apache Hadoop Training
Cloudera University’s 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.
Advance Your Ecosystem Expertise
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.
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.
Key Features
- LIVE Instructor-led Classes
- 24x7 on-demand technical support for assignments, queries, quizzes, project, etc.
- Flexibility to attend the class at your convenient time.
- Server Access to Massive's Tech Management System until you get into your dream carrier.
- A huge database of Interview Questions
- Professional Resume Preparation
- Earn a Skill Certificate
- Enroll today and get the advantage.
Curriculum
- 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
- What Is Pig?
- Pig’s Features
- Pig Use Cases
- Interacting with Pig
- Pig Latin Syntax
- Loading Data
- Simple Data Types
- Field Definitions
- Data Output
- Viewing the Schema
- Filtering and Sorting Data
- Commonly-Used Functions
- Storage Formats
- Complex/Nested Data Types
- Grouping
- Built-In Functions for Complex Data
- Iterating Grouped Data
- Techniques for Combining Data Sets
- Joining Data Sets in Pig
- Set Operations
- Splitting Data Sets
- 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
- What Is Hive?
- What Is Impala?
- Schema and Data Storage
- Comparing Hive to Traditional Databases
- Hive Use Cases
- 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
- Data Storage
- Creating Databases and Tables
- Loading Data
- Altering Databases and Tables
- Simplifying Queries with Views
- Storing Query Results
- Partitioning Tables
- Choosing a File Format
- Managing Metadata
- Controlling Access to Data
- Joining Datasets
- Common Built-In Functions
- Aggregation and Windowing
- How Impala Executes Queries
- Extending Impala with User-Defined Functions
- Improving Impala Performance
- Complex Values in Hive
- Using Regular Expressions in Hive
- Sentiment Analysis and N-Grams
- Conclusion
- Understanding Query Performance
- Controlling Job Execution Plan
- Bucketing
- Indexing Data
- SerDes
- Data Transformation with Custom Scripts
- User-Defined Functions
- Parameterized Queries
- Comparing MapReduce, Pig, Hive, Impala, and Relational Databases
- Which to Choose?
Have Any Questions?
We are happy to answer any questions and we appreciate every feedback about our work!