When does class start/end?
Classes begin promptly at 9:00 am, and typically end at 5:00 pm.
Cloudera Data Analyst Training
Apache Hive makes transformation and analysis of complex, multi-structured data scalable in Hadoop. Apache Impala enables real-time interactive analysis of the data stored in Hadoop using a native...
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$3,195 USD
GSA
$2,736.27
Course Code
DATA-ANALYST
Duration
4 days
Available Formats
Classroom, Virtual
“Have been working in Cloudera for several months now, but haven't completely understood the animals and when to use as well as how things are working in the background. This class answered those questions.”
Apache Hive makes transformation and analysis of complex, multi-structured data scalable in Hadoop. Apache Impala enables real-time interactive analysis of the data stored in Hadoop using a native SQL environment. Together, they make multi-structured data accessible to analysts, database administrators, and others without Java programming expertise.
Prerequisites
This course is designed for data analysts, business intelligence specialists, developers, system architects, and database administrators. Some knowledge of SQL is assumed, as is basic Linux command-line familiarity. Prior knowledge of Apache Hadoop is not required.
Course Details
Introduction
Apache 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
- Database Integration: Sqoop
- Other Hadoop Data Tools
- Exercise Scenario Explanation
Introduction to Apache Hive and Impala
- What Is Hive?
- What Is Impala?
- Why Use Hive and Impala?
- Schema and Data Storage
- Comparing Hive and Impala to Traditional Databases
- Use Cases
Querying with Apache 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 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 in Hive
Apache Hive Optimization
- Understanding Query Performance
- Cost-Based Optimization and Statistics
- Bucketing
- ORC File Optimizations
Apache Impala Optimization
- How Impala Executes Queries
- Improving Impala Performance
Extending Apache 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 Hive, Impala, and Relational Databases
- Which to Choose?
Conclusion
Apache Kudu
- What Is Kudu?
- Kudu Tables
- Using Impala with Kudu
Contact Us
1-800-803-3948
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