Description

Cloudera Educational Services' four-day Data Analyst Training course will teach you to apply traditional data analytics and business intelligence skills to big data. This course presents the tools data professionals need to access, manipulate, transform, and analyze complex data sets using SQL and familiar scripting languages.

Skills Gained

How the open source ecosystem of big data tools addresses challenges not met by traditional RDBMSs
How Apache Hive and Apache Impala are used to provide SQL access to data
How Hive and Impala syntax and data formats, including functions and subqueries, help answer questions about data
How to create, modify, and delete tables, views, and databases; load data; and store results of queries
How to create and use partitions and different file formats
How to combine two or more datasets using JOIN or UNION, as appropriate
What analytic and windowing functions are, and how to use them
How to store and query complex or nested data structures
How to process and analyze semi-structured and unstructured data
Different techniques for optimizing Hive and Impala queries
How to extend the capabilities of Hive and Impala using parameters, custom file formats and SerDes, and external scripts
How to determine whether Hive, Impala, an RDBMS, or a mix of these is best for a given task

Course Overview

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
Bucketing
Hive on Spark

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

Cloudera Data Analyst Training Virtual Classroom Live October 31, 2017

Price: $3,195

Enroll today to reserve your spot!