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

Oct 19  - Oct 22, 2021
8:00AM - 4:00PM Central
4 Days
Virtual Instructor Led
Enroll
Dec 20  - Dec 23, 2021
8:00AM - 4:00PM Central
4 Days
Virtual Instructor Led
Enroll
Feb 08  - Feb 11, 2022
11:00AM - 7:00PM Central
4 Days
Virtual Instructor Led
Enroll

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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