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Learn Power BI
Learn Power BI

Learn Power BI: A comprehensive, step-by-step guide for beginners to learn real-world business intelligence , Second Edition

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Learn Power BI

Chapter 1: Understanding Business Intelligence and Power BI

Power BI is a powerful ecosystem of business intelligence tools and technologies from Microsoft. But what exactly is business intelligence, anyway? Simply stated, business intelligence is all about leveraging data to make better decisions. This can take many forms and is not necessarily restricted to just business. We use data in our personal lives to make better decisions as well. For example, if we are remodeling a bathroom, we get multiple quotes from different firms. The prices and details in these quotes are pieces of data that allow us to make an informed decision in terms of which company to choose. We may also research these firms online. This is more data that ultimately supports our decision.

In this chapter, we will explore the fundamental concepts of business intelligence, as well as why business intelligence is important to organizations. In addition, we will take a high-level tour of the Power BI ecosystem, licensing, and core tools, such as Power BI Desktop and the Power BI service.

The following topics will be covered in this chapter:

  • Exploring key concepts of business intelligence
  • Discovering the Power BI ecosystem
  • Choosing the right Power BI license
  • Introducing Power BI Desktop and the Power BI service

Exploring key concepts of business intelligence

In the context of organizations, business intelligence is about making better decisions for your business. Unlike the example in the introduction, organizations are not generally concerned with bathrooms but rather with what can make their business more effective, efficient, and profitable. The businesses that provided those quotes on bathroom remodeling need to answer questions such as the following:

  • How can the business attract new customers?
  • How can the business retain more customers?
  • Who are the competitors and how do they compare?
  • What is driving profitability?
  • Where can expenses be diminished?

There are endless questions that businesses need to answer every day, and these businesses need data coupled with business intelligence tools and techniques to answer such questions and make effective operational and strategic decisions.

While business intelligence is a vast subject in and of itself, the key concepts of business intelligence can be broken down into five areas:

  • Domain
  • Data
  • Model
  • Analysis
  • Visualization

Domain

A domain is simply the context where business intelligence is applied. Most businesses are composed of relatively standard business functions or departments, such as the following:

  • Sales
  • Marketing
  • Manufacturing/production
  • Supply chain/operations
  • Research and development
  • Human resources
  • Accounting/finance

Each of these business functions or departments represents a domain within which business intelligence can be used to answer questions that can assist us in making better decisions.

The domain helps in narrowing down the focus regarding which questions can be answered and what decisions need to be made. For example, within the context of sales, a business might want to know which sales personnel are performing better or worse, or which customers are the most profitable. Business intelligence can provide such insights as well as help to determine which activities enable certain sales professionals to outperform others, or why certain customers are more profitable than others. This information can then be used to train and mentor sales personnel who are performing less effectively or to focus sales efforts.

Within the context of marketing, a business can use business intelligence to determine which types of marketing campaigns, such as email, radio, print, TV, and the web, are most effective in attracting new customers. This then informs the business where they should spend their marketing budget.

Within the context of manufacturing, a business can use business intelligence to determine the Mean Time Between Failure (MTBF) for machines that are used in the production of goods. This information can be used by the business to determine whether preventative maintenance would be beneficial and how often such preventative maintenance should occur.

Clearly, there are endless examples of where business intelligence can make an organization more efficient, effective, and profitable. Deciding on a domain in which to employ business intelligence techniques is a key step in enabling business intelligence undertakings within organizations, since the domain dictates which key questions can be answered, the possible benefits, as well as what data is required in order to answer those questions.

Data

Once a domain has been decided upon, the next step is identifying and acquiring the data that's pertinent to that domain. This means identifying the sources of relevant data. These sources may be internal or external to an organization and may be structured, unstructured, or semi-structured in nature.

Internal and external data

Internal data is data that is generated within an organization by its business processes and operations. These business processes can generate large volumes of data that is specific to that organization's operations. This data can take the form of net revenues, sales to customers, new customer acquisitions, employee turnover, units produced, cost of raw materials, and time series or transactional information. This historical and current data is valuable to organizations if they wish to identify patterns and trends, as well as for forecasting and future planning. Importantly, all the relevant data to a domain and question is almost never housed within a single data source; organizations inevitably have multiple sources of relevant data.

In addition to internal data, business intelligence is most effective when internal data is combined with external data. Crucially, external data is data that is generated outside the boundaries of an organization's operations. Such external data includes things such as overall global economic trends, census information, customer demographics, household salaries, and the cost of raw materials. All this data exists irrespective of any single organization.

Each domain and question will have internal and external data that is relevant and irrelevant to answering the question at hand. However, do not be fooled into believing that simply because you have chosen manufacturing/production as the domain, other domains, such as sales and marketing, do not have relevant sources of data. If you are trying to forecast the required production levels, sales data in terms of pipelines can be very relevant. Similarly, external data that points toward overall economic growth may also be extremely relevant, while data such as the cost of raw materials may very well be irrelevant.

Structured, unstructured, and semi-structured data

Structured data is data that conforms to a rather formal specification of tables with rows and columns. Think of a spreadsheet where you might have columns for the transaction ID, customer, units purchased, and price per unit. Each row represents a sales transaction. Structured data sources are the easiest sources for business intelligence tools to consume and analyze. These sources are most often relational databases, which include technologies such as Microsoft SQL Server, Microsoft Access, Azure Table storage, Azure SQL Database, Oracle, MySQL, IBM Db2, Teradata, PostgreSQL, Informix, and Sybase. In addition, this category of data sources includes relational database standards such as Open Database Connectivity (ODBC) and Object Linking and Embedding Database (OLE DB).

Unstructured data is effectively the opposite of structured data. Unstructured data cannot be organized into simple tables with rows and columns. Such data includes things such as video, audio, images, and text. Text documents, social media posts, and online reviews are also examples of largely unstructured data. Unstructured data sources are the most difficult types of sources for business intelligence tools to consume and analyze. This type of data is either stored as Binary Large Objects (BLOBSs), online files or posts, or as files in a filesystem, such as the New Technology File System (NTFS) or the Hadoop Distributed File System (HDFS).

Semi-structured data has a structure but does not conform to the formal definition of structured data, that is, tables with rows and columns. Examples of semi-structured data include tab and delimited text files, XML, other markup languages such as HTML and XSL, JavaScript Object Notation (JSON), and Electronic Data Interchange (EDI). Semi-structured data sources have a self-defining structure that makes them easier to consume and analyze than unstructured data sources but require more work than true, structured data sources.

Semi-structured data also includes so-called NoSQL databases, which include data stores such as document databases, graph databases, and key-value stores. These databases are specifically designed to store structured and unstructured data. Document databases include Microsoft Azure Cosmos DB, MongoDB, Cloudant (IBM), Couchbase, and MarkLogic. Graph databases include Neo4j and HyperGraphDB. Key-value stores include Basho Technologies' Riak, Redis, Aerospike, Amazon Web Services' DynamoDB, Couchbase, DataStax's Cassandra, and MapR Technologies. Wide-column stores include Cassandra and HBase.

Finally, semi-structured data also includes data access protocols, such as Open Data Protocol (OData) and other Representational State Transfer (REST) Application Programming Interfaces (APIs). These protocols provide interfaces to data sources such as Microsoft SharePoint, Microsoft Exchange, Microsoft Active Directory, and Microsoft Dynamics; social media systems such as Twitter and Facebook; as well as other online systems such as Mailchimp, Salesforce, Smartsheet, Twilio, Google Analytics, and GitHub, to name a few. These data protocols abstract how the data is stored, whether that is a relational database, NoSQL database, or simply a bunch of files.

Most business intelligence tools, such as Power BI, are optimized for handling structured and semi-structured data. Structured data sources integrate natively with how business intelligence tools are designed. In addition, business intelligence tools are designed to ingest semi-structured data sources and transform them into structured data. Unstructured data is more difficult but not impossible to analyze with business intelligence tools. In fact, Power BI has some features that are designed to ease the ingestion and analysis of unstructured data sources. However, analyzing such unstructured data has its limitations.

Model

A model, or data model, refers to the way in which one or more data sources are organized to support analysis and visualization. Models are built by transforming and cleansing data, helping to define the types of data within those sources, as well as the definition of data categories for specific data types. Building a model generally involves three elements:

  • Organizing
  • Transforming and cleansing
  • Defining and categorizing

Organizing

Models can be extremely simple, such as a single table with columns and rows. However, business intelligence almost always involves multiple tables of data, and often involves multiple tables of data coming from multiple sources. Thus, the model becomes more complex as the various sources and tables of data must be combined into a cohesive whole. This is done by defining how each of the disparate sources of data relates to one another. As an example, let's say you have one data source that represents a customer's name, contact information, and perhaps the size of the business by revenue and/or the number of employees. This information might come from an organization's Customer Relationship Management (CRM) system. The second source of data might be order information, which includes the customer's name, units purchased, and the price that was paid. This second source of data comes from the organization's Enterprise Resource Planning (ERP) system. These two sources of data can be related to one another based on the unique name or ID of the customer.

Some sources of data have prebuilt models. This includes traditional data warehouse technologies for structured data as well as analogous systems for performing analytics over unstructured data. The traditional data warehouse technology is generally built upon the Online Analytical Processing (OLAP) technology and includes systems such as Microsoft's Analysis Services, Snowflake, Oracle's Essbase, AtScale cubes, SAP HANA and Business Warehouse servers, and Azure Synapse. With respect to unstructured data analysis, technologies such as Apache Spark, Databricks, and Azure Data Lake Storage are used.

Transforming and cleansing

When building a data model, it is often (read: always) necessary to clean and transform the source data. Data is never clean – it must always be massaged for bad data to be removed or resolved. For example, when dealing with customer data from a CRM system, it is not uncommon to have the same customer entered with multiple spellings. The format of data in spreadsheets may make data entry easy for humans but can be unsuitable for business intelligence purposes. In addition, data may have errors, missing data, inconsistent formatting, or even have something as seemingly simple as trailing spaces. These types of situations can cause problems when performing business intelligence analysis. Luckily, business intelligence tools such as Power BI provide mechanisms for cleansing and reshaping the data to support analysis. This might involve replacing or removing errors in the data, pivoting, unpivoting, or transposing rows and columns, removing trailing spaces, or other types of transformation operations.

Transforming and cleansing technologies are often referred to as Extract, Transform, Load (ETL) tools and include products such as Microsoft's SQL Server Integration Services (SSIS), Azure Data Factory, Alteryx, Informatica, Dell Boomi, Salesforce's MuleSoft, Skyvia, IBM's InfoSphere Information Server, Oracle Data Integrator, Talend, Pentaho Data Integration, SAS's Data Integration Studio, Sybase ETL, and QlikView Expressor.

Defining and categorizing

Data models also formally define the types of data within each table. Data types generally include formats such as text, decimal number, whole number, percentage, date, time, date and time, duration, true/false, and binary. The definition of these data types is important as it defines what kind of analysis can be performed on the data. For example, it does not make sense to create a sum or average of text data types; instead, you would use aggregations such as count, first, or last.

Finally, data models also define the data category of data types. While a data type such as a postal code might be numeric or text, it is important for the model to define that the numeric data type represents a postal code. This further defines the type of analysis that can be performed upon this data, such as plotting the data on a map. Similarly, it might be important for the data model to define that a text data type represents a web or image Uniform Resource Locator (URL). Typical data categories include such things as address, city, state, province, continent, country, region, place, county, longitude, latitude, postal code, web URL, image URL, and barcode.

Analysis

Once a domain has been selected and data sources have been combined into a model, the next step is to perform an analysis of the data. This is a key process within business intelligence as this is when you attempt to answer questions that are relevant to the business using internal and external data. Simply having data about sales is not immediately useful to a business. For example, to predict future sales revenue, it is important that such data is aggregated and analyzed. This analysis can determine the average sales for a product, the frequency of purchases, and which customers purchase more frequently than others. Such information allows better decision-making by an organization.

Data analysis can take many forms, such as grouping data, creating simple aggregations such as sums, counts, and averages, as well as creating more complex calculations, identifying trends, correlations, and forecasting. Many times, organizations have, or wish to have, Key Performance Indicators (KPIs), which are tracked by the business to help determine the organization's health or performance. KPIs might include such things as employee retention rate, net promoter score, new customer acquisitions per month, gross margin, and Earnings Before Interest, Tax, Depreciation, and Amortization (EBITDA). Such KPIs generally require that the data is aggregated, has calculations performed on it, or both. These aggregations and calculations are called metrics or measures and are used to identify trends or patterns that can inform business decision-making. In some cases, advanced analysis tools such as programming languages, machine learning and artificial intelligence, data mining, streaming analytics, and unstructured analytics are necessary to gain the proper insights.

There are numerous programming languages that have either been specifically designed from the ground up for data analytics or have developed robust data analytics packages or extensions. Two of the most popular languages in this space include R and Python. Other popular languages include SQL, Multidimensional Expressions (MDX), Julia, SAS, MATLAB, Scala, and F#.

There is also a wide variety of machine learning and data mining tools and platforms for performing predictive analytics around data classification, regression, anomaly detection, clustering, and decision-making. Such systems include TensorFlow, Microsoft's Azure Machine Learning, DataRobot, Alteryx Analytics Hub, H2O.ai, KNIME, Splunk, RapidMiner, and Prevedere.

Streaming analytics becomes important when dealing with Internet of Things (IoT) data. In these situations, tools such as Striim, StreamAnalytix, TIBCO Event Processing, Apache Storm, Azure Stream Analytics, and Oracle Stream Analytics are used.

When dealing with unstructured data, tools such as Pig and Hive are popular, as well as tools such as Apache Spark and Azure Cognitive Services for vision, speech, and sentiment analysis.

Of course, any discussion around data analytics tools would be incomplete without including Microsoft Excel. Spreadsheets have long been the go-to analytics tool for business users, and the most popular spreadsheet today is Microsoft Excel. However, other spreadsheet programs, such as Google Sheets, Smartsheet, Apple Numbers, Zoho Sheet, and LibreOffice Calc, also exist.

Visualization

The final key concept in business intelligence is visualization or the actual presentation of the analysis being performed. Humans are visually oriented and thus it is advantageous to view the results of the analysis in the form of charts, reports, and dashboards. This may take the form of tables, matrices, pie charts, bar graphs, and other visual displays that help provide context and meaning to the analysis. In the same way that a picture is worth a thousand words, visualizations allow thousands, millions, or even trillions of individual data points to be presented in a concise manner that is easily consumed and understandable. Visualization allows the analyst or report author to let the data tell a story. This story answers the questions that are originally posed by the business and thus delivers the insights that allow organizations to make better decisions.

Individual charts or visualizations typically display aggregations, KPIs, and/or other calculations of underlying data that's been summarized by some form of grouping. These charts are designed to present a specific facet or metric of the data within a specific context. For example, one chart may display the number of web sessions by the day of the week, while another chart may display the number of page views by browser.

Business intelligence tools allow multiple individual tables and charts to be combined on a single page or report. Modern business intelligence tools such as Power BI support interactivity between individual visualizations to further aid the discovery and analysis process. This interactivity allows the report consumer to click on portions of individual visualizations, such as bar charts, maps, and tables, in order to drill down, highlight, or filter the information presented or determine the influence of a particular portion of a chart on the rest of the visualizations in a report. This goes beyond typical legacy visualization tools such as SQL Server Reporting Services (SSRS) or Crystal Reports, which only provide minimal user interactivity when it comes to choosing from predefined filters. For example, given the two charts we referenced previously, the report consumer can click on a particular day of the week in the first report to display the page visit breakdown per browser for the chosen day of the week in the second chart:

Figure 1.1 – Two bar charts: (L) Sessions by DayOfWeek; (R) Pageviews by Browser

Figure 1.1 – Two bar charts: (L) Sessions by DayOfWeek; (R) Pageviews by Browser

Finally, dashboards provide easy-to-understand visualizations of KPIs that are important to an organization. For example, the CEO of a corporation may wish to see only certain information from sales, marketing, operations, and human resources. Each of these departments may have its own detailed reports, but the CEO only wishes to track one or two of the individual visualizations within each of those reports. Dashboards enable this functionality.

Visualization software includes venerable tools such as SSRS and Crystal Reports, as well as software such as Birst, Domo, MicroStrategy, Qlik Sense, Tableau CRM, SAS Visual Analytics, Sisense, Tableau, ThoughtSpot, and TIBCO Spotfire.

Now that we have examined the key concepts and overarching themes of business intelligence, it is time to delve a layer deeper and discover the business intelligence-enabling technologies that comprise the Power BI ecosystem.

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Key benefits

  • Learn simple through to advanced Power BI features in a clear, concise way using real-world examples
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Description

To succeed in today's transforming business world, organizations need business intelligence capabilities to make smarter decisions faster than ever before. This updated second edition of Learn Power BI takes you on a journey of data exploration and discovery, using Microsoft Power BI to ingest, cleanse, and organize data in order to unlock key business insights that can then be shared with others. This newly revised and expanded edition of Learn Power BI covers all of the latest features and interface changes and takes you through the fundamentals of business intelligence projects, how to deploy, adopt, and govern Power BI within your organization, and how to leverage your knowledge in the marketplace and broader ecosystem that is Power BI. As you progress, you will learn how to ingest, cleanse, and transform your data into stunning visualizations, reports, and dashboards that speak to business decision-makers. By the end of this Power BI book, you will be fully prepared to be the data analysis hero of your organization – or even start a new career as a business intelligence professional.

Who is this book for?

If you’re an IT manager, data analyst, or BI user new to using Power BI for solving business intelligence problems, this book is for you. You’ll also find this book helpful if you want to migrate from other BI tools to create powerful and interactive dashboards. No experience of working with Power BI is expected.

What you will learn

  • Get up and running quickly with Power BI
  • Understand and plan your business intelligence projects
  • Connect to and transform data using Power Query
  • Create data models optimized for analysis and reporting
  • Perform simple and complex DAX calculations to enhance analysis
  • Discover business insights and create professional reports
  • Collaborate via Power BI dashboards, apps, goals, and scorecards
  • Deploy and govern Power BI, including using deployment pipelines
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Table of Contents

18 Chapters
Section 1:The Basics Chevron down icon Chevron up icon
Chapter 1: Understanding Business Intelligence and Power BI Chevron down icon Chevron up icon
Chapter 2: Planning Projects with Power BI Chevron down icon Chevron up icon
Section 2:The Desktop Chevron down icon Chevron up icon
Chapter 3: Up and Running with Power BI Desktop Chevron down icon Chevron up icon
Chapter 4: Connecting to and Transforming Data Chevron down icon Chevron up icon
Chapter 5: Creating Data Models and Calculations Chevron down icon Chevron up icon
Chapter 6: Unlocking Insights Chevron down icon Chevron up icon
Chapter 7: Creating the Final Report Chevron down icon Chevron up icon
Section 3:The Service Chevron down icon Chevron up icon
Chapter 8: Publishing and Sharing Chevron down icon Chevron up icon
Chapter 9: Using Reports in the Power BI Service Chevron down icon Chevron up icon
Chapter 10: Understanding Dashboards, Apps, Goals, and Security Chevron down icon Chevron up icon
Chapter 11: Refreshing Content Chevron down icon Chevron up icon
Section 4:The Future Chevron down icon Chevron up icon
Chapter 12: Deploying, Governing, and Adopting Power BI Chevron down icon Chevron up icon
Chapter 13: Putting Your Knowledge to Use Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

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Bhavik Merchant Feb 28, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Great piece of work updated with newer content and capabilities. This book definitely hits the mark for its stated target audience who are not BI professionals. Greg does a good job introducing BI and projects, especially the point that the technology can change but most fundamental processes and techniques should not (e.g. Data modelling).I liked that it had a single use case that is built out over the course of the book. The problems chosen were realistic and steps required to solve them were not overly complex. I also liked the order in which the chapters flowed, for example getting a fully working simple report by Chapter 3 without loading any external data.Coverage of Power BI Desktop and Service was detailed and pretty thorough. The reader will definitely get experience with all important areas and more.The final section on taking up a career in BI is very good, complete with interview tips and salary negotiation advice!I only have one suggested improvement - I wish that best practices and other nuggets throughout the book were highlighted more obviously because they are so useful!Disclosure: I received a free copy of the book in return for my review.
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Jason Ramsey Feb 18, 2022
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Excellent read for someone new to Power BI or someone looking for a refresher or reference for specfic topics. The book covers a lot of ground and has a good balance of narrative and code samples. Chapter 12 on deployment models, governance, and adoption were of particular interest to me.
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karl Y. Vieux Feb 28, 2022
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This is definitely the book to have if you want to learn Power BI. As a BI developer I used this book to help enhance my skill in this space. Definitely recommend this one to Anyone looking to enter the BI space.
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Pragati Jain Feb 18, 2022
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Learn Power BI by Greg Deckler is an amazing book for every Power BI enthusiast who want to start their learning journey on this business intelligence tool. The book is a step-by-step guide that helps learning the real-world intelligence.The first key highlight for me in this book is that - Ever chapter ends with 3 concluding sections:Summary, Questions, Further ReadingEvery section has it's importance here and in my suggestion should not to be missed by the reader.Second key highlight for me is the references to the relevant resources used to explain every topic in the book. The reader can basically download sample data and get some practical hands-on while reading the book. So just not theoretical, it is practical hands-on hat makes you learn concepts. Coming from a Maths/Science background, this is something I really look in every book.Overall this is a well-written book with enough examples for one to try. Worth buying for a beginner learner. Great job be Greg on assembling Power BI topics in a single book.
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Laura Mahoney Feb 24, 2022
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This book provides a thorough end-to-end coverage of Power BI that should help anyone get started or get better. Along with core topics like data transformation, modeling, analysis, and visualization, it also covers other important topics like sharing/permissions, licenses, row-level security (RLS), goals, dashboards, and more.
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  17. Venezuela
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Customs duty are charges levied on goods when they cross international borders. It is a tax that is imposed on imported goods. These duties are charged by special authorities and bodies created by local governments and are meant to protect local industries, economies, and businesses.

Do I have to pay customs charges for the print book order? Chevron down icon Chevron up icon

The orders shipped to the countries that are listed under EU27 will not bear custom charges. They are paid by Packt as part of the order.

List of EU27 countries: www.gov.uk/eu-eea:

A custom duty or localized taxes may be applicable on the shipment and would be charged by the recipient country outside of the EU27 which should be paid by the customer and these duties are not included in the shipping charges been charged on the order.

How do I know my custom duty charges? Chevron down icon Chevron up icon

The amount of duty payable varies greatly depending on the imported goods, the country of origin and several other factors like the total invoice amount or dimensions like weight, and other such criteria applicable in your country.

For example:

  • If you live in Mexico, and the declared value of your ordered items is over $ 50, for you to receive a package, you will have to pay additional import tax of 19% which will be $ 9.50 to the courier service.
  • Whereas if you live in Turkey, and the declared value of your ordered items is over € 22, for you to receive a package, you will have to pay additional import tax of 18% which will be € 3.96 to the courier service.
How can I cancel my order? Chevron down icon Chevron up icon

Cancellation Policy for Published Printed Books:

You can cancel any order within 1 hour of placing the order. Simply contact customercare@packt.com with your order details or payment transaction id. If your order has already started the shipment process, we will do our best to stop it. However, if it is already on the way to you then when you receive it, you can contact us at customercare@packt.com using the returns and refund process.

Please understand that Packt Publishing cannot provide refunds or cancel any order except for the cases described in our Return Policy (i.e. Packt Publishing agrees to replace your printed book because it arrives damaged or material defect in book), Packt Publishing will not accept returns.

What is your returns and refunds policy? Chevron down icon Chevron up icon

Return Policy:

We want you to be happy with your purchase from Packtpub.com. We will not hassle you with returning print books to us. If the print book you receive from us is incorrect, damaged, doesn't work or is unacceptably late, please contact Customer Relations Team on customercare@packt.com with the order number and issue details as explained below:

  1. If you ordered (eBook, Video or Print Book) incorrectly or accidentally, please contact Customer Relations Team on customercare@packt.com within one hour of placing the order and we will replace/refund you the item cost.
  2. Sadly, if your eBook or Video file is faulty or a fault occurs during the eBook or Video being made available to you, i.e. during download then you should contact Customer Relations Team within 14 days of purchase on customercare@packt.com who will be able to resolve this issue for you.
  3. You will have a choice of replacement or refund of the problem items.(damaged, defective or incorrect)
  4. Once Customer Care Team confirms that you will be refunded, you should receive the refund within 10 to 12 working days.
  5. If you are only requesting a refund of one book from a multiple order, then we will refund you the appropriate single item.
  6. Where the items were shipped under a free shipping offer, there will be no shipping costs to refund.

On the off chance your printed book arrives damaged, with book material defect, contact our Customer Relation Team on customercare@packt.com within 14 days of receipt of the book with appropriate evidence of damage and we will work with you to secure a replacement copy, if necessary. Please note that each printed book you order from us is individually made by Packt's professional book-printing partner which is on a print-on-demand basis.

What tax is charged? Chevron down icon Chevron up icon

Currently, no tax is charged on the purchase of any print book (subject to change based on the laws and regulations). A localized VAT fee is charged only to our European and UK customers on eBooks, Video and subscriptions that they buy. GST is charged to Indian customers for eBooks and video purchases.

What payment methods can I use? Chevron down icon Chevron up icon

You can pay with the following card types:

  1. Visa Debit
  2. Visa Credit
  3. MasterCard
  4. PayPal
What is the delivery time and cost of print books? Chevron down icon Chevron up icon

Shipping Details

USA:

'

Economy: Delivery to most addresses in the US within 10-15 business days

Premium: Trackable Delivery to most addresses in the US within 3-8 business days

UK:

Economy: Delivery to most addresses in the U.K. within 7-9 business days.
Shipments are not trackable

Premium: Trackable delivery to most addresses in the U.K. within 3-4 business days!
Add one extra business day for deliveries to Northern Ireland and Scottish Highlands and islands

EU:

Premium: Trackable delivery to most EU destinations within 4-9 business days.

Australia:

Economy: Can deliver to P. O. Boxes and private residences.
Trackable service with delivery to addresses in Australia only.
Delivery time ranges from 7-9 business days for VIC and 8-10 business days for Interstate metro
Delivery time is up to 15 business days for remote areas of WA, NT & QLD.

Premium: Delivery to addresses in Australia only
Trackable delivery to most P. O. Boxes and private residences in Australia within 4-5 days based on the distance to a destination following dispatch.

India:

Premium: Delivery to most Indian addresses within 5-6 business days

Rest of the World:

Premium: Countries in the American continent: Trackable delivery to most countries within 4-7 business days

Asia:

Premium: Delivery to most Asian addresses within 5-9 business days

Disclaimer:
All orders received before 5 PM U.K time would start printing from the next business day. So the estimated delivery times start from the next day as well. Orders received after 5 PM U.K time (in our internal systems) on a business day or anytime on the weekend will begin printing the second to next business day. For example, an order placed at 11 AM today will begin printing tomorrow, whereas an order placed at 9 PM tonight will begin printing the day after tomorrow.


Unfortunately, due to several restrictions, we are unable to ship to the following countries:

  1. Afghanistan
  2. American Samoa
  3. Belarus
  4. Brunei Darussalam
  5. Central African Republic
  6. The Democratic Republic of Congo
  7. Eritrea
  8. Guinea-bissau
  9. Iran
  10. Lebanon
  11. Libiya Arab Jamahriya
  12. Somalia
  13. Sudan
  14. Russian Federation
  15. Syrian Arab Republic
  16. Ukraine
  17. Venezuela