Google BigQuery and PostgreSQL: BigQuery for Data Analysis

Video description

Upskill your skillset on SQL and PostgreSQL by taking up this course.

About This Video

Carefully designed curriculum teaching you everything in SQL and Google BigQuery that you will need for data analysis in businesses
Business-related examples and case studies on SQL and Google BigQuery
Ample practice exercises on Google BigQuery because SQL and Google BigQuery require practice

In Detail

SQL is the most universal and …

Google BigQuery and PostgreSQL: BigQuery for Data Analysis

Video description

Upskill your skillset on SQL and PostgreSQL by taking up this course.

About This Video

Carefully designed curriculum teaching you everything in SQL and Google BigQuery that you will need for data analysis in businesses
Business-related examples and case studies on SQL and Google BigQuery
Ample practice exercises on Google BigQuery because SQL and Google BigQuery require practice

In Detail

SQL is the most universal and commonly used database language. It powers the most used database engines such as PostgreSQL, SQL Server, SQLite, and MySQL. It is not difficult to learn SQL. SQL is not a programming language, it's a query language. The primary objective where SQL was created was to give the possibility to common people to get interested data from a database. It is also an English-like language so anyone who can use English at a basic level can write SQL query easily.

SQL is easy but no one can determine the learning time it takes. It totally depends on you. The method we have adopted to help you learn SQL quickly starts from the basics and takes you to an advanced level within hours. You can follow the same but remember, you can learn nothing without practicing it.

PostgreSQL is one of several database systems, or RDMS (Relational Database Management System), of which others are Oracle, Informix, MySQL, and MSQL.

All these RDMSs use SQL as their language. Each of them has minor variations in the "dialect" of SQL that they use, but it's all still SQL.

By the end of this course, your confidence in using Google BigQuery and PostgreSQL will soar. You'll have a thorough understanding of how to use Google BigQuery and PostgreSQL for data analytics as a career opportunity.

Who this book is for

This course can be taken by any working professionals beginning their data journey and for anyone curious to master SQL from beginner to advanced levels in a short span of time.

Publisher resources

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Chapter 1 : Introduction

Welcome to the Course

Course Flow

Chapter 2 : Installation and Getting Started

PostgreSQL and PGAdmin Installation

Setting Up BigQuery on Google Cloud Platform

BigQuery Interface

Chapter 3 : Fundamental SQL Statements

CREATE

CREATE in BigQuery

Exercise 1: Create DB and Table

INSERT

INSERT in BigQuery

Import Data from File

Importing Data from File Using BigQuery Web User Interface

File Upload in Google BigQuery Through Google Cloud sdk

Importing Data from Google Drive

Exercise 2: Inserting and Importing

SELECT

SELECT in BigQuery

SELECT DISTINCT

SELECT DISTINCT in BigQuery

WHERE Function

WHERE in BigQuery

Logical Operators - AND, OR, NOT

Logical Operators in BigQuery

Exercise 3: SELECT and WHERE

UPDATE

UPDATE in BigQuery

DELETE

DELETE in BigQuery

ALTER

ALTER in BigQuery

Exercise 4: Updating Table

Chapter 4 : Restore and Backup

Restore and Backup

Debugging Restoration

Creating DB Using CSV Files

Data Set Creation in BigQuery

Exercise 5: Restore and Backup

Chapter 5 : Selection Commands: Filtering

IN in BigQuery

BETWEEN

BETWEEN in BigQuery

LIKE in BigQuery

Exercise 6: IN, LIKE, and BETWEEN

Chapter 6 : Selection Commands: Ordering

ORDER BY

ORDER BY in BigQuery

LIMIT

LIMIT in BigQuery

ORDER BY

Chapter 7 : Alias

AS in BigQuery

Chapter 8 : Aggregate Commands

COUNT

COUNT in BigQuery

SUM

SUM in BigQuery

AVERAGE

AVERAGE in BigQuery

MIN MAX

MIN MAX in BigQuery

Exercise 8: Aggregate Functions

Chapter 9 : Group By Commands

GROUP BY

GROUP BY in BigQuery

HAVING

HAVING in BigQuery

Exercise 9: Group By

Chapter 10 : Conditional Statement

CASE WHEN

CASE WHEN in BigQuery

Chapter 11 : JOINS

Introduction to Joins

Concepts of Joining and Combining Data

Preparing the Data

Creating Datasets for Joins in BigQuery

Inner Join

INNER JOIN in BigQuery

Left Join

LEFT JOIN in BigQuery

Right Join

RIGHT JOIN in BigQuery

Full Outer Join

FULL OUTER JOIN in BigQuery

Cross Join

CROSS JOIN in BigQuery

Intersect and Intersect ALL

Except

EXCEPT in BigQuery

Union

UNION in BigQuery

Exercise 10: Joins

Chapter 12 : Subqueries

Subqueries

Subqueries in BigQuery

Exercise 11: Subqueries

Chapter 13 : Views and Indexes

Views

Views in BigQuery

Index

Views in BigQuery

Exercise 12: Views

Chapter 14 : String Functions

LENGTH

LENGTH in BigQuery

UPPER LOWER

Changing Case in BigQuery

REPLACE

REPLACE in BigQuery

TRIM, LTRIM, RTRIM

TRIM, LTRIM, RTRIM in BigQuery

CONCATENATION

CONCATENATION in BigQuery

SUBSTRING

SUBSTRING in Google BigQuery

LIST AGGREGATION

LIST AGGREGATION in Google BigQuery

Exercise 13: String Functions

Chapter 15 : Mathematical Functions

CEIL and FLOOR

CEIL and FLOOR in BigQuery

RANDOM

RANDOM in BigQuery

SETSEED

SETSEED in BigQuery

ROUND

POWER

POWER in BigQuery

Exercise 14: Mathematical Functions

Chapter 16 : Date-Time Functions

CURRENT DATE and TIME

CURRENT DATE and TIME in BigQuery

AGE

AGE in BigQuery

EXTRACT

EXTRACT in BigQuery

Exercise 15: Date-Time Functions

Chapter 17 : Pattern (STRING) Matching

PATTERN MATCHING BASICS

ADVANCE PATTERN MATCHING (REGULAR EXPRESSIONS)

PATTERN MATCHING in BigQuery

Exercise 16: Pattern Matching

Chapter 18 : Google Data Studio to Visualize BigQuery Data

Google Data Studio to Visualize BigQuery Data

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