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A Beginner's Guide to Storytelling with Data
Anthony DeBarros
Copyright © 2018 past Anthony DeBarros.
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CONTENTS IN DETAIL
ACKNOWLEDGMENTS
INTRODUCTION
What Is SQL?
Why Use SQL?
About This Book
Using the Book's Code Examples
Using PostgreSQL
Installing PostgreSQL
Working with pgAdmin
Alternatives to pgAdmin
Wrapping Up
1
CREATING YOUR FIRST DATABASE AND TABLE
Creating a Database
Executing SQL in pgAdmin
Connecting to the Assay Database
Creating a Table
The CREATE Table Statement
Making the teachers Table
Inserting Rows into a Table
The INSERT Argument
Viewing the Information
When Lawmaking Goes Bad
Formatting SQL for Readability
Wrapping Upwards
Try It Yourself
2
Beginning DATA EXPLORATION WITH SELECT
Basic SELECT Syntax
Querying a Subset of Columns
Using DISTINCT to Notice Unique Values
Sorting Information with ORDER Past
Filtering Rows with WHERE
Using Similar and ILIKE with WHERE
Combining Operators with AND and OR
Putting Information technology All Together
Wrapping Up
Endeavour Information technology Yourself
3
Agreement DATA TYPES
Characters
Numbers
Integers
Auto-Incrementing Integers
Decimal Numbers
Choosing Your Number Data Type
Dates and Times
Using the interval Data Blazon in Calculations
Miscellaneous Types
Transforming Values from 1 Blazon to Another with Bandage
CAST Shortcut Annotation
Wrapping Up
Try It Yourself
4
IMPORTING AND EXPORTING DATA
Working with Delimited Text Files
Quoting Columns that Comprise Delimiters
Handling Header Rows
Using Re-create to Import Data
Importing Census Data Describing Counties
Creating the us_counties_2010 Table
Demography Columns and Data Types
Performing the Demography Import with Copy
Importing a Subset of Columns with Copy
Adding a Default Value to a Column During Import
Using COPY to Export Data
Exporting All Information
Exporting Detail Columns
Exporting Query Results
Importing and Exporting Through pgAdmin
Wrapping Up
Try Information technology Yourself
5
BASIC MATH AND STATS WITH SQL
Math Operators
Math and Information Types
Adding, Subtracting, and Multiplying
Partition and Modulo
Exponents, Roots, and Factorials
Minding the Order of Operations
Doing Math Across Demography Table Columns
Calculation and Subtracting Columns
Finding Percentages of the Whole
Tracking Pct Alter
Aggregate Functions for Averages and Sums
Finding the Median
Finding the Median with Percentile Functions
Median and Percentiles with Census Data
Finding Other Quantiles with Percentile Functions
Creating a median() Function
Finding the Mode
Wrapping Up
Try It Yourself
6
JOINING TABLES IN A RELATIONAL DATABASE
Linking Tables Using Bring together
Relating Tables with Key Columns
Querying Multiple Tables Using JOIN
JOIN Types
Join
LEFT JOIN and RIGHT JOIN
Full OUTER JOIN
CROSS JOIN
Using Zip to Find Rows with Missing Values
3 Types of Table Relationships
I-to-One Relationship
One-to-Many Human relationship
Many-to-Many Relationship
Selecting Specific Columns in a Bring together
Simplifying Join Syntax with Table Aliases
Joining Multiple Tables
Performing Math on Joined Table Columns
Wrapping Upwards
Effort It Yourself
7
Tabular array Design THAT WORKS FOR YOU
Naming Tables, Columns, and Other Identifiers
Using Quotes Around Identifiers to Enable Mixed Case
Pitfalls with Quoting Identifiers
Guidelines for Naming Identifiers
Controlling Cavalcade Values with Constraints
Master Keys: Natural vs. Surrogate
Strange Keys
Automatically Deleting Related Records with Pour
The Check Constraint
The UNIQUE Constraint
The Not NULL Constraint
Removing Constraints or Adding Them Later
Speeding Upward Queries with Indexes
B-Tree: PostgreSQL'south Default Index
Considerations When Using Indexes
Wrapping Up
Endeavour It Yourself
viii
EXTRACTING Data BY Group AND SUMMARIZING
Creating the Library Survey Tables
Creating the 2014 Library Information Tabular array
Creating the 2009 Library Information Table
Exploring the Library Data Using Aggregate Functions
Counting Rows and Values Using count()
Finding Maximum and Minimum Values Using max() and min()
Aggregating Information Using GROUP BY
Wrapping Up
Try It Yourself
nine
INSPECTING AND MODIFYING Information
Importing Data on Meat, Poultry, and Egg Producers
Interviewing the Data Set
Checking for Missing Values
Checking for Inconsistent Information Values
Checking for Malformed Values Using length()
Modifying Tables, Columns, and Information
Modifying Tables with ALTER TABLE
Modifying Values with UPDATE
Creating Backup Tables
Restoring Missing Column Values
Updating Values for Consistency
Repairing ZIP Codes Using Concatenation
Updating Values Across Tables
Deleting Unnecessary Data
Deleting Rows from a Tabular array
Deleting a Column from a Tabular array
Deleting a Table from a Database
Using Transaction Blocks to Save or Revert Changes
Improving Performance When Updating Big Tables
Wrapping Upwardly
Try It Yourself
ten
STATISTICAL FUNCTIONS IN SQL
Creating a Census Stats Table
Measuring Correlation with corr(Y, X)
Checking Additional Correlations
Predicting Values with Regression Assay
Finding the Issue of an Independent Variable with r-squared
Creating Rankings with SQL
Ranking with rank() and dense_rank()
Ranking Within Subgroups with Sectionalisation BY
Computing Rates for Meaningful Comparisons
Wrapping Up
Attempt It Yourself
xi
WORKING WITH DATES AND TIMES
Data Types and Functions for Dates and Times
Manipulating Dates and Times
Extracting the Components of a timestamp Value
Creating Datetime Values from timestamp Components
Retrieving the Current Date and Time
Working with Time Zones
Finding Your Time Zone Setting
Setting the Time Zone
Calculations with Dates and Times
Finding Patterns in New York City Taxi Data
Finding Patterns in Amtrak Data
Wrapping Up
Effort It Yourself
12
ADVANCED QUERY TECHNIQUES
Using Subqueries
Filtering with Subqueries in a WHERE Clause
Creating Derived Tables with Subqueries
Joining Derived Tables
Generating Columns with Subqueries
Subquery Expressions
Common Table Expressions
Cross Tabulations
Installing the crosstab() Role
Tabulating Survey Results
Tabulating City Temperature Readings
Reclassifying Values with CASE
Using CASE in a Common Table Expression
Wrapping Upwards
Endeavor Information technology Yourself
xiii
MINING TEXT TO Observe MEANINGFUL DATA
Formatting Text Using String Functions
Case Formatting
Character Information
Removing Characters
Extracting and Replacing Characters
Matching Text Patterns with Regular Expressions
Regular Expression Notation
Turning Text to Data with Regular Expression Functions
Using Regular Expressions with WHERE
Additional Regular Expression Functions
Full Text Search in PostgreSQL
Text Search Data Types
Creating a Table for Full Text Search
Searching Speech Text
Ranking Query Matches by Relevance
Wrapping Up
Try It Yourself
14
ANALYZING SPATIAL DATA WITH POSTGIS
Installing PostGIS and Creating a Spatial Database
The Building Blocks of Spatial Data
Ii-Dimensional Geometries
Well-Known Text Formats
A Note on Coordinate Systems
Spatial Reference System Identifier
PostGIS Data Types
Creating Spatial Objects with PostGIS Functions
Creating a Geometry Type from Well-Known Text
Creating a Geography Type from Well-Known Text
Point Functions
LineString Functions
Polygon Functions
Analyzing Farmers' Markets Data
Creating and Filling a Geography Column
Adding a GiST Alphabetize
Finding Geographies Within a Given Distance
Finding the Distance Betwixt Geographies
Working with Census Shapefiles
Contents of a Shapefile
Loading Shapefiles via the GUI Tool
Exploring the Demography 2010 Counties Shapefile
Performing Spatial Joins
Exploring Roads and Waterways Data
Joining the Census Roads and Water Tables
Finding the Location Where Objects Intersect
Wrapping Upward
Effort Information technology Yourself
15
SAVING TIME WITH VIEWS, FUNCTIONS, AND TRIGGERS
Using Views to Simplify Queries
Creating and Querying Views
Inserting, Updating, and Deleting Data Using a View
Programming Your Own Functions
Creating the percent_change() Function
Using the percent_change() Function
Updating Data with a Function
Using the Python Linguistic communication in a Part
Automating Database Actions with Triggers
Logging Grade Updates to a Table
Automatically Classifying Temperatures
Wrapping Up
Try It Yourself
16
USING POSTGRESQL FROM THE COMMAND LINE
Setting Upward the Command Line for psql
Windows psql Setup
macOS psql Setup
Linux psql Setup
Working with psql
Launching psql and Connecting to a Database
Getting Aid
Changing the User and Database Connection
Running SQL Queries on psql
Navigating and Formatting Results
Meta-Commands for Database Information
Importing, Exporting, and Using Files
Additional Command Line Utilities to Expedite Tasks
Adding a Database with createdb
Loading Shapefiles with shp2pgsql
Wrapping Up
Try It Yourself
17
MAINTAINING YOUR DATABASE
Recovering Unused Space with VACUUM
Tracking Tabular array Size
Monitoring the autovacuum Process
Running VACUUM Manually
Reducing Table Size with VACUUM FULL
Changing Server Settings
Locating and Editing postgresql.conf
Reloading Settings with pg_ctl
Backing Upward and Restoring Your Database
Using pg_dump to Support a Database or Table
Restoring a Database Backup with pg_restore
Boosted Fill-in and Restore Options
Wrapping Up
Try Information technology Yourself
18
IDENTIFYING AND TELLING THE STORY Behind YOUR DATA
Start with a Question
Certificate Your Process
Gather Your Data
No Information? Build Your Ain Database
Assess the Data's Origins
Interview the Data with Queries
Consult the Data'due south Owner
Identify Key Indicators and Trends over Time
Ask Why
Communicate Your Findings
Wrapping Up
Try It Yourself
APPENDIX
ADDITIONAL POSTGRESQL Resources
PostgreSQL Evolution Environments
PostgreSQL Utilities, Tools, and Extensions
PostgreSQL News
Documentation
INDEX
FOREWORD
When people inquire which programming linguistic communication I learned start, I often absent-mindedly reply, "Python," forgetting that it was actually with SQL that I get-go learned to write code. This is probably considering learning SQL felt so intuitive afterwards spending years running formulas in Excel spreadsheets. I didn't have a technical groundwork, but I found SQL's syntax, unlike that of many other programming languages, straightforward and easy to implement. For example, you run SELECT * on a SQL table to make every row and column appear. You simply use the Join keyword to return rows of data from different related tables, which you can then further group, sort, and clarify .
I'chiliad a graphics editor, and I've worked as a programmer and journalist at a number of publications, including Politico, Vox, and USA TODAY. My daily responsibilities involve analyzing information and creating visualizations from what I find. I first used SQL when I worked at The Relate of College Education and its sister publication, The Chronicle of Philanthropy. Our team analyzed data ranging from nonprofit financials to kinesthesia salaries at colleges and universities. Many of our projects included every bit much as 20 years' worth of data, and one of my main tasks was to import all that data into a SQL database and clarify it. I had to summate the percent alter in fund raising dollars at a nonprofit or find the median endowment size at a academy to measure out an institution's performance. I discovered SQL to be a powerful linguistic communication, 1 that fundamentally shaped my agreement of what yous tin and can't do with data.
SQL excels at bringing order to messy, large data sets and helps you observe how different information sets are related. Plus, its queries and functions are easy to reuse inside the aforementioned projection or even in a unlike database. This leads me to Practical SQL. Looking back, I wish I'd read Chapter 4 on "Importing and Exporting Data" so I could have understood the power of majority imports instead of writing long, cumbersome INSERT statements when filling a tabular array. The statistical capabilities of PostgreSQL, covered in Chapters 5 and 10 in this volume, are besides something I wish I had grasped before, equally my data analysis often involves computing the percentage modify or finding the average or median values. I'chiliad embarrassed to say that I didn't know how percentile_cont(), covered in Chapter 5, could be used to easily calculate a median in PostgresSQ with the added bonus that it as well finds your information's natural breaks or quantiles. But at that stage in my career, I was merely scratching the surface of SQL's capabilities. It wasn't until 2014, when I became a data developer at Gannett Digital on a team led by Anthony DeBarros, that I learned to use PostgreSQL. I began to empathize just how enormously powerful SQL was for creating a reproducible and sustainable workflow.
When I met Anthony, he had been working at Usa TODAY and other Gannett properties for more than than twenty years, where he had led teams that built databases and published accolade-winning investigations. Anthony was able to show me the ins and outs of our squad's databases in addition to instruction me how to properly build and maintain my own. It was through working with Anthony that I truly learned how to lawmaking.
One of the outset projects Anthony and I collaborated on was the 2014 U.Due south. midterm elections. We helped build an election forecast information visualization to evidence U.s.a. TODAY readers the latest polling averages, campaign finance data, and biographical information for more than 1,300 candidates in more than 500 congressional and gubernatorial races.
Building our information infrastructure was a complex, multistep process powered by a PostgreSQL database at its middle. Anthony taught me how to write code that funneled all the information from our sources into a half-dozen tables in PostgreSQL. From there, we could query the data into a format that would power the maps, charts, and front-stop presentation of our ballot forecast. Around this time, I as well learned one of my favorite things about PostgreSQL—its powerful suite of geographic functions (Chapter 14 in this book). Past adding the PostGIS extension to the database, y'all tin create spatial information that you can then export equally GeoJSON or as a shapefile, a format that is easy to map. You lot tin also perform complex spatial assay, like calculating the distance between two points or finding the density of schools or, as Anthony shows in the chapter, all the farmers' markets in a given radius.
It's a skill I've used repeatedly in my career. For case, I used information technology to build a data set of lead exposure risk at the demography-tract level while at Vox, which I consider one of my crowning PostGIS achievements. Using this database, I was able to create a data gear up of every U.Due south. Census tract and its corresponding pb exposure risk in a spatial format that could exist easily mapped at the national level.
With so many different programming languages available more than 200, if you lot can believe it , it's truly overwhelming to know where to begin. Ane of the best pieces of advice I received when first starting to lawmaking was to find an inefficiency in my workflow that could be improved by coding. In my example, it was edifice a database to easily query a projection'due south data. Maybe you're in a similar boat or mayhap you only want to know how to analyze large data sets.
Regardless, you lot're probably looking for a no-nonsense guide that skips the programming jargon and delves into SQL in an easy-to-understand manner that is both practical and, more importantly, applicable. And that'south exactly what Practical SQL does. It gets away from programming theory and focuses on teaching SQL by instance, using real information sets you'll likely encounter. Information technology besides doesn't shy away from showing you how to deal with annoying messy information pitfalls: misspelled names, missing values, and columns with unsuitable data types. This is important because, as you'll quickly larn, there's no such thing as clean data. Over the years, my office as a data journalist has evolved. I build fewer databases now and build more than maps. I also report more. Just the core requirement of my task, and what I learned when showtime learning SQL, remains the same: know thy data and to thine ain data be true. In other words, the most important attribute of working with information is being able to understand what'south in information technology. Yous can't expect to inquire the right questions of your data or tell a compelling story if yous don't sympathize how to best analyze information technology. Fortunately, that's where Applied SQL comes in. It'll teach y'all the fundamentals of working with data so that you can observe your own stories and insights.
Sarah Frostenson
Graphics Editor at Pol
INTRODUCTION
Presently after joining the staff of USA TODAY I received a data set I would analyze well-nigh every week for the side by side decade. It was the weekly Best-Selling Books list, which ranked the nation's top-selling books based on confidential sales data. The listing non only produced an endless stream of story ideas to pitch, but it also captured the zeitgeist of America in a singular way.
For example, did y'all know that cookbooks sell a bit more during the week of Mother'south Day, or that Oprah Winfrey turned many obscure writers into number one best-selling authors but past having them on her show? Week afterward calendar week, the book list editor and I pored over the sales figures and book genres, ranking the information in search of the next headline. Rarely did we come up upwardly empty: nosotros chronicled everything from the rocketrise of the blockbuster Harry Potter series to the fact that Oh, the Places You'll Go! by Dr. Seuss has get a perennial gift for new graduates. My technical companion during this time was the database programming linguistic communication SQL (for Structured Query Linguistic communication). Early on on, I convinced Us TODAY'south Information technology department to grant me access to the SQLbased database arrangement that powered our book list application. Using SQL, I was able to unlock the stories hidden in the database, which contained titles, authors, genres, and various codes that defined the publishing earth. Analyzing data with SQL to discover interesting stories is exactly what yous'll acquire to exercise using this book.
What Is SQL?
SQL is a widely used programming language that allows you to define and query databases. Whether you're a marketing analyst, a announcer, or a researcher mapping neurons in the brain of a fruit fly, you'll benefit from using SQL to manage database objects as well equally create, modify, explore, and summarize information.
Because SQL is a mature linguistic communication that has been around for decades, it's deeply ingrained in many modern systems. A pair of IBM researchers start outlined the syntax for SQL (then called SEQUEL) in a 1974 paper, building on the theoretical work of the British estimator scientist Edgar F. Codd. In 1979, a forerunner to the database company Oracle (then chosen Relational Software) became the kickoff to use the language in a commercial production. Today, it continues to rank every bit one of the most-used computer languages in the world, and that'southward unlikely to modify soon.
SQL comes in several variants, which are generally tied to specific database systems. The American National Standards Institute (ANSI) and International Organization for Standardization (ISO), which set standards for products and technologies, provide standards for the language and shepherd revisions to it. The skillful news is that the variants don't stray far from the standard, so once yous acquire the SQL conventions for one database, you can transfer that cognition to other systems.
Why Use SQL?
So why should yous use SQL? After all, SQL is non usually the first tool people cull when they're learning to clarify data. In fact, many people start with Microsoft Excel spreadsheets and their assortment of analytic functions. After working with Excel, they might graduate to Access, the database system congenital into Microsoft Office, which has a graphical query interface that makes it easy to get piece of work done, making SQL skills optional. Merely as you lot might know, Excel and Access have their limits. Excel currently allows ane,048,576 rows maximum per worksheet, and Admission limits database size to two gigabytes and limits columns to 255 per table. It'south not uncommon for data sets to surpass those limits, particularly when you lot're working with data dumped from government systems. The concluding obstacle you want to discover while facing a deadline is that your database system doesn't have the chapters to become the job done.
Using a robust SQL database system allows you lot to work with terabytes of data, multiple related tables, and thousands of columns. It gives yous improved programmatic control over the construction of your data, leading to efficiency, speed, and most important accuracy. SQL is also an excellent adjunct to programming languages used in the data sciences, such as R and Python. If yous utilise either language, you tin can connect to SQL databases and, in some cases, even incorporate SQL syntax directly into the language. For people with no background in programming languages, SQL often serves as an easy-to-understand introduction into concepts related to data structures and programming logic.
Additionally, knowing SQL tin can assist you beyond data analysis. If y'all delve into edifice online applications, y'all'll find that databases provide the backend power for many common web frameworks, interactive maps, and content management systems. When you need to dig beneath the surface of these applications, SQL'southward adequacy to manipulate data and databases will come in very handy.
About This Volume
Practical SQL is for people who encounter data in their everyday lives and desire to larn how to analyze and transform it. To this end, I discuss realworld data and scenarios, such every bit U.S. Census demographics, crime statistics, and information about taxi rides in New York City. Along with data about databases and code, you'll also larn tips on how to analyze and acquire information equally well equally other valuable insights I've accumulated throughout my career. I won't focus on setting up servers or other tasks typically handled by a database ambassador, but the SQL and PostgreSQL fundamentals y'all learn in this book will serve y'all well if you intend to become that road.
I've designed the exercises for beginner SQL coders but will assume that you know your fashion around your computer, including how to install programs, navigate your hard drive, and download files from the net.
Although many chapters in this volume can stand alone, yous should piece of work through the book sequentially to build on the fundamentals. Some data sets used in early chapters reappear later in the book, and then post-obit the book in order will help you stay on track. Practical SQL starts with the basics of databases, queries, tables, and information that are common to SQL beyond many database systems. Capacity 13 to 17 encompass topics more specific to PostgreSQL, such as total text search and GIS. The post-obit table of contents provides more detail nigh the topics discussed in each chapter:
Chapter 1: Creating Your First Database and Tabular array introduces PostgreSQL, the pgAdmin user interface, and the code for loading a elementary data set about teachers into a new database.
Chapter ii: Beginning Data Exploration with SELECT explores basic SQL query syntax, including how to sort and filter data.
Chapter 3: Understanding Information Types explains the definitions for setting columns in a table to hold specific types of data, from text to dates to various forms of numbers.
Chapter four: Importing and Exporting Data explains how to utilize SQL commands to load data from external files and then consign it. You'll load a tabular array of U.Southward. Demography population information that you'll use throughout the book.
Chapter five: Basic Math and Stats with SQL covers arithmetic operations and introduces aggregate functions for finding sums, averages, and medians.
Chapter 6: Joining Tables in a Relational Database explains how to query multiple, related tables by joining them on key columns. You'll larn how and when to employ different types of joins.
Chapter seven: Table Blueprint that Works for You covers how to gear up tables to improve the organization and integrity of your data as well as how to speed upwardly queries using indexes.
Chapter 8: Extracting Information past Grouping and Summarizing explains how to use aggregate functions to notice trends in U.S. library apply based on almanac surveys.
Affiliate 9: Inspecting and Modifying Information explores how to observe and fix incomplete or inaccurate data using a collection of records well-nigh meat, egg, and poultry producers equally an case.
Chapter x: Statistical Functions in SQL introduces correlation, regression, and ranking functions in SQL to help you derive more meaning from data sets.
Affiliate 11: Working with Dates and Times explains how to create, dispense, and query dates and times in your database, including working with time zones, using data on New York City taxi trips and Amtrak train schedules.
Chapter 12: Advanced Query Techniques explains how to utilize more complex SQL operations, such every bit subqueries and cross tabulations, and the CASE statement to reclassify values in a data set on temperature readings.
Chapter 13: Mining Text to Discover Meaningful Data covers how to use PostgreSQL's total text search engine and regular expressions to extract information from unstructured text, using a collection of speeches by U.Southward. presidents every bit an example.
Chapter 14: Analyzing Spatial Data with PostGIS introduces data types and queries related to spatial objects, which will permit you analyze geographical features like states, roads, and rivers.
Chapter xv: Saving Time with Views, Functions, and Triggers explains how to automate database tasks and so you can avert repeating routine piece of work.
Chapter 16: Using PostgreSQL from the Command Line covers how to utilise text commands at your figurer's command prompt to connect to your database and run queries.
Chapter 17: Maintaining Your Database provides tips and procedures for tracking the size of your database, customizing settings, and bankroll upwardly data.
Chapter xviii: Identifying and Telling the Story Behind Your Information provides guidelines for generating ideas for analysis, vetting information, drawing sound conclusions, and presenting your findings conspicuously.
Appendix: Additional PostgreSQL Resources lists software and documentation to help yous grow your skills. Each chapter ends with a "Try It Yourself" section that contains exercises to help you lot reinforce the topics you learned.
Using the Book'southward Lawmaking Examples
Each chapter includes code examples, and most use data sets I've already compiled. All the lawmaking and sample data in the book is bachelor to download at nostarch.com/practicalSQL/. Click the Download the lawmaking from GitHub link to go to the GitHub repository that holds this material. At GitHub, you lot should see a "Clone or Download" push button that gives you lot the selection to download a Nix file with all the materials. Save the file to your computer in a location where you lot can easily discover it, such as your desktop.
Inside the ZIP file is a folder for each affiliate. Each folder contains a file named Chapter_XX (Twenty is the chapter number) that ends with a .sql extension. Y'all can open up those files with a text editor or with the PostgreSQL administrative tool you'll install. You lot can copy and paste code when the book instructs you to run it. Note that in the book, several code examples are truncated to salvage space, only you'll need the total listing from the .sql file to consummate the practise. You'll know an example is truncated when you run across snip within the listing.
Also in the .sql files, you'll see lines that brainstorm with two hyphens (--) and a space. These are comments that provide the code'south listing number and additional context, but they're non part of the code. These comments as well note when the file has additional examples that aren't in the volume.
Of course, you can also use another database system, such every bit Microsoft SQL Server or MySQL; many lawmaking examples in this volume translate easily to either SQL implementation. Yet, some examples, especially afterwards in the volume, do not, and you'll demand to search online for equivalent solutions. Where advisable, I'll notation whether an instance lawmaking follows the ANSI SQL standard and may exist portable to other systems or whether it's specific to PostgreSQL.
Using PostgreSQL
In this book, I'll teach y'all SQL using the open source PostgreSQL database arrangement. PostgreSQL, or merely Postgres, is a robust database organisation that can handle very large amounts of data. Here are some reasons PostgreSQL is a great choice to use with this book:
Information technology's free.
It's available for Windows, macOS, and Linux operating systems.
Its SQL implementation closely follows ANSI standards.
Information technology's widely used for analytics and information mining, so finding assistance online from peers is easy.
Its geospatial extension, PostGIS, lets you analyze geometric information and perform mapping functions.
Information technology's available in several variants, such every bit Amazon Redshift and Green - plum, which focus on processing huge data sets.
It's a mutual choice for web applications, including those powered by the pop web frameworks Django and Reddish on Runway.
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