Date: 15 December 2020 - 18 December 2020

Duration: 4 Days

Locations: Online

Take your knowledge to the next level with Cloudera’s Data Analyst Training

This four-day data analyst training course focusing on Apache Pig and Hive and Cloudera Impala will teach you to apply traditional data analytics and business intelligence skills to big data. Cloudera 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 multi-structured data accessible to analysts, database administrators, and others without Java programming expertise. Apache Pig applies the fundamentals of familiar scripting languages to the Hadoop cluster. Cloudera Impala enables real-time interactive analysis of the data stored in Hadoop via a native SQL environment.

Hands-On Hadoop

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

  • The features that Pig, Hive, and Impala offer for data acquisition, storage, and analysis
  • The fundamentals of Apache Hadoop and data ETL (extract, transform, load), ingestion, and processing with Hadoop tools
  • How Pig, Hive, and Impala improve productivity for typical analysis tasks
  • Joining diverse datasets to gain valuable business insight
  • Performing real-time, complex queries on datasets

Audience  & Prerequisites

This course is designed for data analysts, business intelligence specialists, developers, system architects, and database administrators. Knowledge of SQL is assumed, as is basic Linux command- line familiarity. Knowledge of at least one scripting language (e.g., Bash scripting, Perl, Python, Ruby) would be helpful but is not essential. Prior knowledge of Apache Hadoop is not required.

CCA Data Analyst 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 and expertise.

Introduction

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

  • 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
  • Scala 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 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 

  • Complex Values in Hive
  • Using Regular Expressions in Hive
  • Sentiment Analysis and N-Grams
  • Conclusion

Hive Optimization

  • Custom SerDes and File Formats in Hive
  • Data Transformation with Custom Script in Hive
  • User-Defined Functions
  • Parameterized Queries

Choosing the Best Tool for the Job

  • Comparing Hive, Impala, and Relational Databases
  • Which to Choose?

Conclusion