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Impala - An Open Source SQL Engine for Hadoop Training Course

Course Summary

The course helps you master in knowledge of the basic concepts of Massively Parallel Processing (MPP) SQL query. It provides an in-depth understanding of querying in Hive and Impala including Impala architecture, daemon, statestore, and catalogue service. The course is best suited for database administrators, software developers, system administrators, and analytics professionals.


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    Course Syllabus


    Course preview

    Impala-An Open Source SQL Engine for Hadoop

    Lesson 0 - Course Introduction 03:39

    0.1 Course Introduction 00:23

    0.2 Table of Contents 00:36

    0.3 Objectives 00:31

    0.4 Course Overview 00:36

    0.5 Value to Professionals and Organizations 00:58

    0.6 Course Prerequisites 00:18

    0.7 Lessons Covered 00:08

    0.8 Conclusion 00:09

    Lesson 1 - Introduction to Impala 14:19

    1.1 Introduction to Impala 00:17

    1.2 Objectives 00:25

    1.3 What is Impala 00:58

    1.4 Benefits of Impala 01:17

    1.5 Benefits of Impala (Contd.) 01:06

    1.6 Exploratory Business Intelligence 00:56

    1.7 Impala Installation

    1.8 Demo - Using Cloudera Manager for Impala 00:08

    1.9 Demo - Using Cloudera Manager for Impala (contd.) 00:08

    1.10 Starting and Stopping Impala 01:23

    1.11 Demo - Starting Impala from Command Line 00:05

    1.12 Demo - Starting Impala from Command Line (contd.) 00:53

    1.13 Data Storage 01:10

    1.14 Managing Metadata 01:20

    1.15 Controlling Access to Data 00:40

    1.16 Impala Shell Commands and Interface 01:31

    1.17 Impala Shell Commands and Interface (contd.) 00:34

    1.18 Demo - Launching Impala Shell and Shell Command 00:06

    1.19 Demo - Launching Impala Shell and Shell Command (contd.) 00:06

    1.20 Quiz

    1.21 Summary 00:51

    1.22 Summary (contd.) 00:15

    1.23 Conclusion 00:10

    Lesson 2 - Querying with Hive and Impala 26:44

    2.1 Querying with Hive and Impala 00:21

    2.2 Objectives 00:34

    2.3 SQL Language Statements 01:13

    2.4 DDL Statements 00:55

    2.5 DML Statements 00:55

    2.6 CREATE DATABASE 00:43

    2.7 CREATE TABLE 00:59

    2.8 CREATE TABLE - Examples 00:59

    2.9 Internal and External Tables 00:53

    2.10 Loading Data into Impala Table 00:35

    2.11 ALTER TABLE 00:34

    2.12 DROP TABLE 00:41

    2.13 DROP DATABASE 00:30

    2.14 DESCRIBE Statement 00:47

    2.15 EXPLAIN Statement 01:07

    2.16 SHOW TABLE Statement 00:29

    2.17 INSERT Statement 00:50

    2.18 INSERT Statement - Examples 00:22

    2.19 SELECT Statement 00:45

    2.20 Data Type 01:17

    2.21 Data Type (contd.) 00:46

    2.22 Operators 01:22

    2.23 Functions 00:39

    2.24 CREATE VIEW in Impala 01:02

    2.25 Hive and Impala Query Syntax 02:09

    2.26 Demo - Using Impala Shell for DDL and DDML SQL Statements 02:09

    2.27 Demo - Using Impala Shell for DDL and DDML SQL Statements (contd.) 02:09

    2.28 Quiz

    2.29 Summary 00:49

    2.30 Conclusion 00:10

    Lesson 3 - Data Storage and File Format 11:47

    3.1 Data Storage and File Format 00:22

    3.2 Objectives 00:27

    3.3 Partitioning Tables 00:43

    3.4 SQL Statements for Partitioned Tables 01:16

    3.5 File Format and Performance Considerations 01:02

    3.6 Choosing File Type and Compression Technique 00:30

    3.7 Demo - File Formats and Compression Techniques 00:15

    3.8 Demo - File Formats and Compression Techniques (contd.) 06:24

    3.9 Quiz

    3.10 Summary 00:39

    3.11 Conclusion 00:09

    Lesson 4 - Working with Impala 12:53

    4.1 Working with Impala 00:18

    4.2 Objectives 00:33

    4.3 Impala Architecture 00:47

    4.4 Impala Daemon 01:08

    4.5 Impala Statestore 00:25

    4.6 Impala Catalog Service 01:40

    4.7 Query Execution Flow in Impala 00:47

    4.8 User - Defined Functions 01:01

    4.9 Hive UDFs with Impala 01:14

    4.10 Demo - UDF in Impala 00:07

    4.11 Demo - UDF in Impala(contd.) 02:31

    4.12 Improving Impala Performance 01:35

    4.13 Quiz

    4.14 Summary 00:40

    4.15 Conclusion 00:07

    That was just a sneak-peak into the lesson.
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Course Fee:
USD 199

Course Type:

Self-Study

Course Status:

Active

Workload:

1 - 4 hours / week

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