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- Herausgeber: Packt Publishing
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
Data exploration and visualization is vital to Business Intelligence, the backbone of almost every enterprise or organization. Redash is a querying and visualization tool developed to simplify how marketing and business development departments are exposed to data. If you want to learn to create interactive dashboards with Redash, explore different visualizations, and share the insights with your peers, then this is the ideal book for you.
The book starts with essential Business Intelligence concepts that are at the heart of data visualizations. You will learn how to find your way round Redash and its rich array of data visualization options for building interactive dashboards. You will learn how to create data storytelling and share these with peers. You will see how to connect to different data sources to process complex data, and then visualize this data to reveal valuable insights.
By the end of this book, you will be confident with the Redash dashboarding tool to provide insight and communicate data storytelling.
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Seitenzahl: 155
Veröffentlichungsjahr: 2018
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Redash v5 Quick Start Guide
Copyright © 2018 Packt Publishing
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Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.
Commissioning Editor: Sunith ShettyAcquisition Editor:Siddharth MandalContent Development Editor:Kirk DsouzaTechnical Editor: Jinesh TopiwalaCopy Editor: Safis EditingProject Coordinator: Hardik BhindeProofreader: Safis EditingIndexer:Rekha NairGraphics: Jason MonteiroProduction Coordinator:Shantanu Zagade
First published: September 2018
Production reference: 1290918
Published by Packt Publishing Ltd. Livery Place 35 Livery Street Birmingham B3 2PB, UK.
ISBN 978-1-78899-616-7
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Foreword
It's amazing to realize that Redash started five years ago. It started as a hackathon project, which was open sourced two months later. I remember getting excited about the first 5 companies that started using Redash.
Today, Redash is a company on its own, and there are over 4500 teams around the world using Redash, both the open source project and the SaaS service. Reading the first book on Redash is another important milestone in Redash’s maturity.
Alexander and Yael are great candidates to write the book. Alexander contributed code to the project and is very familiar with its internals, while Yael used it in various data projects for multiple companies since the early days of Redash.
I hope you will find this book useful, and it will help you start using Redash. You should also remember that Redash is an active open source project, which means it keeps evolving constantly. I suggest that you familiarize yourself with the knowledge base along with the active community. Happy querying!
Arik Fraimovich,Redash Creator/Founder
Contributors
About the authors
Alexander Leibzon is a software infrastructure consultant and backend software developer with over 15 years' experience in the software development industry.
Alexander is a contributor to Redash and several other open source projects.
Prior to becoming an independent consultant, Alexander was a data infrastructure engineer at EverythingMe, the company where Redash was initially developed during a hackathon.
Alexander holds a BSc degree in physics and computer science.
Yael Leibzon is a data analyst with 8 years' experience in the industry. Yael has been an extensive user of Redash for over 3 years.
Yael holds an MSc in biomedical engineering. During Yael's academic research , Yael developed finite element computational models, which were published in biomedical literature.
About the reviewer
Arik Fraimovich created Redash as a hackathon project while working for EverythingMe in 2013. He founded the Redash company in 2015 to make sure Redash has a sustainable future. Arik is also a developer and entrepreneur, developing software professionally for over 15 years and has passion for solving real users' problems.
Tal Maizels is a chief technology officer with extensive experience in the marketing and advertising industry. Tal has led projects and teams in start-ups for the last 10 years in the fields of EdTech, consumer networks, and finance. An IT professional with a BSc in computer science and mathematics from Bar-Ilan University, Tal has over 20 years' experience in software development, design, and management, and is skilled in mobile applications, Java, Software as a Service (SaaS), Continuous Integration, and Scrum.
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Table of Contents
Title Page
Copyright and Credits
Redash v5 Quick Start Guide
Packt Upsell
Why subscribe?
PacktPub.com
Foreword
Contributors
About the authors
About the reviewer
Packt is searching for authors like you
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Conventions used
Get in touch
Reviews
Introducing Redash
Data challenges experienced by companies on a daily basis
An example dashboard
Ideal tools for targeting challenges
Meeting Redash
What exactly is Redash?
Redash architecture
Summary
Installing Redash
Sizing – choosing the right machine to do the job
Installation options and installation walkthrough
Installation options
AWS-predefined image
Launching an instance using Redash AMI
Launching an instance using Bitnami Redash AMI
Welcome to Redash setup page
GCE-Predefined image
Docker-based installation
Provisioning script installation
Explaining the setup.sh script
Troubleshooting
Configuration and setup
Email configuration
Using Google OAuth to log in to Redash
Redash environment settings
HTTPS (SSL) Setup
Permissions in Redash
Groups
Creating and editing groups
Creating users
Summary
Creating and Visualizing your First Query
Creating and testing the Data Source
Alternative static Data Source definition
Creating your first query
Creating the visualization
Creating the dashboard
Summary
Connecting to Data Sources
Supported Data Sources
Adding a new Redash Data Source
A detailed walk-through of the selected Data Sources
Connecting to PostgreSQL
Connecting to ElasticSearch
Connecting to MongoDB
Connecting to GoogleSpreadsheet
Connecting to Url
Connecting to Query Results (beta)
Connecting to Amazon Athena
Connecting to BigQuery
Connecting to Redshift
Connecting to DynamoDB
Summary
Writing and Executing Queries
Query listing
Query editor overview
Query operations
Creating a query
Editing a query
Forking a query
Archiving a query
Scheduling a query
Query results and filters
Query results
Query filters
Parametrized queries
Parameter settings
Query snippets
Alerts
Alert statuses
Creating Alerts
Alert destinations
Summary
Creating Visualizations
The benefits of visualizations
An overview of visualization types
Boxplot
Chart
Map (Choropleth map)
Cohort
Counter
Funnel
Map (Markers map)
Pivot table
Sankey
Sunburst sequence
Word cloud
Table
Visualizations in action
Creating and editing visualizations
Going over Redash visualizations
Boxplot
Chart
Map (Choropleth)
Cohort
Counter
Funnel
Map (Markers)
Pivot Table
Sankey
Sunburst sequence
Table
Word Cloud
Special actions on visualizations
Summary
Dashboards and Practical Tips
Dashboard how-tos
Creating/editing dashboards
Dashboard query filters, hashtags, and favorite dashboards
Dashboard-level filters
Dashboard hashtags
Favorite Dashboards
Sharing dashboards
Dashboard guidelines
Tips and tricks
Summary
Customizing Redash
Redash API
API authentication
API calls overview
API usage examples
Extending Redash code
Installing Redash for development
Installing a Docker-based developer environment
Initial dev setup
Dev use
Installing a regular developer environment
Installing dependencies
Installing the necessary Python packages
Node.js packages and assets
Redash configuration
Creating Redash operational database tables
Starting Redash's main processes
Running tests
Connecting to the remote server while running the frontend locally
Summary
Other Books You May Enjoy
Leave a review - let other readers know what you think
Preface
Redash is a relatively new player in the data querying and visualization ecosystem, yet it gains solid recognition levels as time passes by.
Redash was initially developed by developers who work with data to serve everyone who works with data. This concept remains the core of Redash, and the book's aims to expose that concept to the readers.
Who this book is for
The Book is for anyone who works with data, but it will best suit mixed data teams. Mixed means you have a developer, an analyst (optionally DBA/IT), and product. Those teams will benefit from all of Redash's features.
This book is intended for novices to intermediate-level Data Analysts and Developers.
Although as prior knowledge – nothing is really required, to get the most of the book you need to be fairly familiar with SQL syntax, as some linux knowledge is a great advantage.
What this book covers
Chapter 1, Introducing Redash, In this chapter you will get an overview of what exactly Redash is and what kind of problems Redash tries to solve.
Chapter 2, Installing Redash, In this chapter you will be walked through the installation process, there are several options to install Redash, all are covered.
Chapter 3,Creating and Visualizing your First Query, chapter for those who want to get right to the point ASAP; a brief overview of everything you need to get started immediately.
Chapter 4, Connecting to Data Sources, chapter that introduces the reader to all the DataSources that Redash can connect to, and their options.
Chapter 5, Writing and Executing Queries, a chapter that gives a walkthrough of Redash's query editor, that covers everything related to creating, editing and executing queries
Chapter 6, Creating Visualizations, chapter that will show all the possible visualization options in Redash, and guidse you on how to use them.
Chapter 7, Dashboards and Practical Tips, chapter that covers actions on Dashboards, and some useful general tips for Redash users.
Chapter 8, Customizing Redash, Chapter that covers the option to extend and customize Redash for your own specific needs.
To get the most out of this book
If you will be the maintainer of a self hosted redash service – you must be proficient with Linux , this will help you to get through
Chapter 2
,
Installing Redash
chapter.
If you will be using Redash to write and visualize queries only – then your minimal requirement will be SQL (no matter which).
If you wish to contribute back to redash, or extend its functionality – then Python and some JavaScript knowledge is required.
In all of the above cases – you can only benefit if you run the examples presented in the book.
from dev side – most of the benefits come from looking at the code!
Download the example code files
You can download the example code files for this book from your account at www.packtpub.com. If you purchased this book elsewhere, you can visit www.packtpub.com/support and register to have the files emailed directly to you.
You can download the code files by following these steps:
Log in or register at
www.packtpub.com
.
Select the
SUPPORT
tab.
Click on
Code Downloads & Errata
.
Enter the name of the book in the
Search
box and follow the onscreen instructions.
Once the file is downloaded, please make sure that you unzip or extract the folder using the latest version of:
WinRAR/7-Zip for Windows
Zipeg/iZip/UnRarX for Mac
7-Zip/PeaZip for Linux
The code bundle for the book is also hosted on GitHub athttps://github.com/PacktPublishing/Redash-v5-Quick-Start-Guide. In case there's an update to the code, it will be updated on the existing GitHub repository.
We also have other code bundles from our rich catalog of books and videos available athttps://github.com/PacktPublishing/. Check them out!
Conventions used
There are a number of text conventions used throughout this book.
CodeInText: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: "In case you prefer to have more control over the installation, you can choose Docker or manually run the bootstrap.shscript"
A block of code is set as follows:
root@ip-10-69-10-45:/home/bitnamiroot@ip-10-69-10-45:/home/bitnamitelnet 54.156.58.190 5432Trying 54.156.58.190...Connected to 54.156.58.190.Escape character is '^]'.^]telnet>
Any command-line input or output is written as follows:
ps -ef | grep redash
Bold: Indicates a new term, an important word, or words that you see onscreen. For example, words in menus or dialog boxes appear in the text like this. Here is an example: "To create a new visualization, press the +New Visualization button"
Get in touch
Feedback from our readers is always welcome.
General feedback: Email [email protected] and mention the book title in the subject of your message. If you have questions about any aspect of this book, please email us at [email protected].
Errata: Although we have taken every care to ensure the accuracy of our content, mistakes do happen. If you have found a mistake in this book, we would be grateful if you would report this to us. Please visit www.packtpub.com/submit-errata, selecting your book, clicking on the Errata Submission Form link, and entering the details.
Piracy: If you come across any illegal copies of our works in any form on the Internet, we would be grateful if you would provide us with the location address or website name. Please contact us at [email protected] with a link to the material.
If you are interested in becoming an author: If there is a topic that you have expertise in and you are interested in either writing or contributing to a book, please visit authors.packtpub.com.
Reviews
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For more information about Packt, please visit packtpub.com.
Introducing Redash
Nowadays, every business creates vast amounts of data. Whether it’s plain logs, usage statistics, or user data, businesses tend to store it.
But without proper analysis and usage, this data just occupies space (S3s, self-hosted Hadoop clusters, regular RDBMS, and so on) and resources (someone must maintain the servers; otherwise, the data is lost).
The ultimate goal is to try to make the data work for the benefit of the company.
Data analysis rapidly expands beyound the domain of enclosed research departments and penetrates almost every department along the company's verticals.
The trend is that data insights move from business-supporting to business-generating roles.
In this chapter, we will cover the following topics:
Data challenges experienced by companies on a daily basis
Ideal tools to target challenges
Meeting Redash
Redash architecture
Data challenges experienced by companies on a daily basis
Let's have a look at an abstract example of a social gaming company and it's use of data:
CEO/SVPs use generic knowledge of company revenues, pre-defined KPIs (new daily users/daily active users/churn rate)
The marketing/business development departments use conversion funnels/campaign traction/pre-defined KPIs/growth rate/revenues (usually also sliced by department/game type/geolocation).
The finance department uses various revenue breakdowns (by department/by external clients, and so on)
The sales department uses revenues by campaigns breakdown (for better campaign evaluation)
The product department uses statistics/growth rate/feature popularity/new daily users (to find out whether a specific feature attracts more users/revenue (with at least the same slicing as marketing)
Customer support/QA/developers deal with bug rates/user reviews/usage statistics
Data analytics/data scientists require data on usage statistics
IT/DBAs/operations/infrastructure need information regarding load statistics/uptime/response SLAs/disk usage/CPU/memory (and other various system stats)
External (contractors/clients/partners) require daily/weekly/monthly reports of various business metrics
As you can see, all the different departments rely on data and have their own specific data needs.
We can also note that if we treat each need as abuilding block, we can reuse them across departments.
But data is not only about numbers. People like to get a real feel, and that's where visualization can come in handy, especially when there is a need to discover trends or spot anomalies. Most of the time, it's much easier to track everything through charts and graphs, even if they represent an abstract trend.
