The Best Certified Big Data, Agile, Digital Transformation, DevOps, Robotics, AI, Machine Learning Training

Overview

Cloudera University’s 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.

Advance Your Ecosystem Expertise

Apache Hive makes transformation and analysis of complex, multi-structured data scalable in Cloudera environments. 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.

What to Expect

Through instructor-led discussion and interactive, hands-on exercises, participants will navigate the ecosystem, learning:

How the open source ecosystem of big data tools addresses challenges not met by traditional RDBMSs
Using Apache Hive and Apache Impala to provide _ SQL access to data
Hive and Impala syntax and data formats, including functions and subqueries
Create, modify, and delete tables, views, and databases; load data; and store results of queries
Create and use partitions and different file formats
Combining two or more datasets using JOIN or UNION, as appropriate
What analytic and windowing functions are, and how to use them
Store and query complex or nested data structures
Process and analyze semi-structured and unstructured data
Techniques for optimizing Hive and Impala queries
Extending 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 Curriculum

Hadoop Fundamentals

The Motivation for Hadoop
Hadoop Overview
Data Storage: HDFS
Distributed Data Processing: YARN, MapReduce, and Spark
Data Processing and Analysis: Pig, Hive, and Impala
Data Integration: Sqoop
Other Hadoop Data Tools
Exercise Scenarios Explanation

Introduction to Hive and Impala

What Is Hive?
What Is Impala?
Schema and Data Storage
Comparing Hive to Traditional Databases
Hive Use Cases

Querying with Hive and Impala

Databases and Tables
Basic Hive and Impala Query Language Syntax
Data Types
Differences Between Hive and Impala Query Syntax
Using Hue to Execute Queries
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

Relational Data Analysis with Hive and Impala

Joining Datasets
Common Built-In Functions
Aggregation and Windowing

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

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

Prerequisite

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.

Certification

Upon completion of the course, attendees are encouraged to continue their study and register for the CCA Data Analyst exam. Certification is a great differentiator. It helps establish you as a leader in the field, providing employers and customers with tangible evidence of your skills & expertise.

Reviews

Traning For:

Individual

Corporate

Cloudera Data Analyst

Certification Program (CCA159)