Introduction
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
- Data Integration: Sqoop
- Other Hadoop Data Tools
- Exercise Scenarios Explanation
Introduction to Hive and Impala
- What Is Hive?
- What Is Impala?
- Why Use Hive and Impala?
- Schema and Data Storage
- Comparing Hive to Traditional Databases
- Use Cases
Querying with 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
Common Operators and Built-in functions
- Operators
- Scalar Functions
- Aggregate Functions
Data Management
- Data Storage
- Creating Databases and Tables
- Loading Data
- Altering Databases and Tables
- Simplifying Queries with Views
- Storing Query Results
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
Working with Multiple Datasets
- UNION and Joins
- Handling NULL Values in Joins
- Advanced Joins
Analytic Functions and Windowing
- Using Common Analytic Functions
- Other Analytic Functions
- Sliding Windows
Complex Data
- Complex Data with Hive
- Complex Data with Impala
Analyzing Text
- Using Regular Expressions with Hive and Impala
- Processing Text Data with SerDes in Hive
- Sentiment Analysis and n-grams
Hive Optimization
- Understanding Query Performance
- Bucketing
- Hive on Spark
Impala Optimization
- How Impala Executes Queries
- Improving Impala Performance
Extending Hive and Impala
- Custom SerDes and File Formats in Hive
- Data Transformation with Custom Scripts in Hive
- User-Defined Functions
- Parameterized Queries
Choosing the Best Tool for the Job
- Comparing MapReduce, Hive, Impala, and Relational Databases
- Which to Choose?
Description:
This course focuses on Apache Hive and Cloudera Impala, and aims to teach students how to apply traditional data analysis and gain the ability to manage business intelligence tools for Big Data. Cloudera presents data on the tools professionals need to access, manipulate, transform and analyze complex data sets using SQL and similar scripting languages.
Apache Hive makes multi-structured data accessible to analysts, database administrators, and other people without knowledge of Java programming. Cloudera Impala enables real-time interactive analysis of data stored in Hadoop through a native SQL environment.
PUE is Cloudera's official Training Partner, authorized by this multinational to provide official training in Cloudera technologies.
PUE is also accredited and recognized to carry out consulting and mentoring services in the implementation of Cloudera solutions in the business field with the added value in the practical and business approach to knowledge that is translated in its official courses.