Cloudera Data Analyst Training

Price
$3,195.00
Duration
 4 Days
Delivery Methods
 VILT    Private Group
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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.

 

Upcoming Class Dates and Times

Mar 01  - Mar 04, 2022
8:00AM - 4:00PM Central
4 Days
Virtual Instructor Led
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Apr 05  - Apr 08, 2022
8:00AM - 4:00PM Central
4 Days
Virtual Instructor Led
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Who Should Attend

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.

Agenda

1 - Introduction
2 - 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
3 - 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
4 - 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
5 - Common Operators and Built-In Functions
  • Operators
  • Scalar Functions
  • Aggregate Functions
6 - Data Management
  • Data Storage
  • Creating Databases and Tables
  • Loading Data
  • Altering Databases and Tables
  • Simplifying Queries with Views
  • Storing Query Results
7 - 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
8 - Working with Multiple Datasets
  • UNION and Joins
  • Handling NULL Values in Joins
  • Advanced Joins
9 - Analytic Functions and Windowing
  • Using Analytic Functions
  • Other Analytic Functions
  • Sliding Windows
10 - Complex Data
  • Complex Data with Hive
  • Complex Data with Impala
11 - Analyzing Text
  • Using Regular Expressions with Hive and Impala
  • Processing Text Data with SerDes in Hive
  • Sentiment Analysis and n-grams in Hive
12 - Apache Hive Optimization
  • Understanding Query Performance
  • Cost-Based Optimization and Statistics
  • Bucketing
  • ORC File Optimizations
13 - Apache Impala Optimization
  • How Impala Executes Queries
  • Improving Impala Performance
14 - Extending Apache Hive and Impala
  • Custom SerDes and File Formats in Hive
  • Data Transformation with Custom Scripts in Hive
  • User-Defined Functions
  • Parameterized Queries
15 - Choosing the Best Tool for the Job
  • Comparing Hive, Impala, and Relational Databases
  • Which to Choose?
16 - Conclusion
  • Apache Kudu
  • What Is Kudu?
  • Kudu Tables
  • Using Impala with Kudu
 

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