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Cloudera Data Analyst Training

SS Course: 35078

Course Overview

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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.                                                                  

Scheduled Classes

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11/29/22 - TDV - Virtual-Instructor Led - Virtual-Instructor Led (click to enroll)

What You'll Learn

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How the open source ecosystem of big data tools addresses challenges not met by traditional RDBMSs
  • 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
  • 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

Outline

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Viewing outline for:
Complex Data with Hive
  • Complex Data with Impala
  • Using Regular Expressions with Hive and Impala
  • Processing Text Data with SerDes in Hive
  • Sentiment Analysis and n-grams in Hive
  • Understanding Query Performance
  • Cost-Based Optimization and Statistics
  • Bucketing
  • ORC File Optimizations
  • How Impala Executes Queries
  • Improving Impala Performance
  • Custom SerDes and File Formats in Hive
  • Data Transformation with Custom Scripts in Hive
  • User-Defined Functions
  • Parameterized Queries
  • Comparing Hive, Impala, and Relational Databases
  • Which to Choose?
  • 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
  • What Is Hive?
  • What Is Impala?
  • Why Use Hive and Impala?
  • Schema and Data Storage
  • Comparing Hive and Impala to Traditional Databases
  • Use Cases
  • 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
  • Operators
  • Scalar Functions
  • Aggregate Functions
  • Data Storage
  • Creating Databases and Tables
  • Loading Data
  • Altering Databases and Tables
  • Simplifying Queries with Views
  • Storing Query Results
  • Partitioning Tables
  • Loading Data into Partitioned Tables
  • When to Use Partitioning
  • Choosing a File Format
  • Using Avro and Parquet File Formats
  • UNION and Joins
  • Handling NULL Values in Joins
  • Advanced Joins
  • Using Analytic Functions
  • Other Analytic Functions
  • Sliding Windows

Prerequisites

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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.

      Who Should Attend

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      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.

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