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Data Modeling with Tableau
Data Modeling with Tableau

Data Modeling with Tableau: A practical guide to building data models using Tableau Prep and Tableau Desktop

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Data Modeling with Tableau

Introducing Data Modeling in Tableau

Welcome to data modeling in Tableau. You might know Tableau as a great self-service analytics tool that provides both powerful analytics and is also easy to use. You might also think that Tableau is light on the key enterprise analytics requirement of data security, data model robustness, and data maintainability. In this book, you will learn that Tableau has all these key data requirements covered. You will learn how data is best structured for Tableau analysis and performance, and understand the functionality of Tableau Prep Builder and Tableau Desktop and the role each plays in building data models. You’ll then publish these data models to Tableau Server or Online and optimize them for performance, governance, and security.

By the end of this book, you will have all the strategies and techniques needed to enable individuals in your organization to answer their own questions with data, regardless of their level of expertise. You will also drastically reduce the calls you receive from these same individuals about confusing data and dashboards that are slow to load.

Tableau is very different from most other BI tools in that the model can be either implicit or explicit. For instance, many analysts open Tableau Desktop, connect to data, and immediately begin creating visuals. In this instance, Tableau implicitly created a data model (that is, made a connection, executed a query, and created metadata) without an analyst having to do anything to create the model.

This implicit data modeling works well when your data source has already been prepared for analysis and you are the person creating charts and dashboards. Often, our data is not structured this way. It comes from different sources and needs to be combined and defined in meaningful ways. In these instances, Tableau provides the tools for you to create data models that are scalable, secure, and targeted to the different skills of a broad class of developers and consumers.

Tableau uses a data model as the foundation for the creation of all analyses. A Tableau data model contains the following:

  • Connection information to the underlying data source.
  • The queries required to retrieve the data.
  • Additional metadata, or data about the data, added to the underlying data. Metadata can include more readable field names, field types, the grouping of data into hierarchies, and calculations not in the underlying data.

Tableau works best when your data is in a traditional spreadsheet table format – that is, Tableau assumes that the first row of your data consists of column headers and each column header maps 1:1 to a field name, with additional rows of data each containing one record of data. If the underlying data is not formatted in this way, analysis within Tableau becomes very difficult and performance will suffer. To address this, you can model your data in a format that works best with Tableau. The best practices to model data properly are the primary content of this book.

This chapter demonstrates how Tableau automatically creates a data model when you connect to a data source, how it interprets rows and columns in your data, and how you can shape and combine additional data into your data model.

In this chapter, we’re going to cover the following main topics:

  • What happens when you connect to data in Tableau Desktop
  • The ideal data structure for Tableau
  • Shaping data for Tableau
  • Connecting multiple tables

Technical requirements

Tableau Desktop (and Tableau Prep Builder version 2022.2 or higher in future chapters) version 2022.2 or higher is needed to complete the exercises in this chapter.

If you don’t have a licensed version of Tableau Desktop, you can obtain a 14-day free trial from https://www.tableau.com/products/desktop.

Another alternative is Tableau Public. The free Tableau Public version of Desktop contains almost all the same features as the paid version, with the exception of a small number of data source connection options, and output can only be saved to the Tableau Public site. However, it often has enough features to perform visual analysis as long as the data isn’t confidential. The Tableau Public Desktop version can be found at https://public.tableau.com/s/.

The files used in the exercises in this chapter can be found at https://github.com/PacktPublishing/Data-Modeling-with-Tableau/. We recommend downloading all the files before getting started. The quickest way to do this is to click on the green <>Code button and then select Download ZIP. Expand the ZIP file and make note of the directory. We will be referencing it throughout the book.

Note

The aforementioned requirements are applicable to all chapters in this book.

What happens when you connect to data in Tableau Desktop?

When you connect to data in Tableau Desktop, Tableau will begin to interpret your data. First, it will create a field for each column of your data. Second, it will assign a data type to each of the fields. Tableau does this because it is powered by a proprietary query technology, called VizQL. VizQL is the technology that underpins Tableau, enabling a visual analytics experience by automatically creating visualizations for a user. This is very different than most business intelligence tools that rely on the user to tell the tool how they would like the data visualized through the picking of a chart type.

For VizQL to work, Tableau needs to know the type of each field. The two main field types in Tableau are discrete and continuous:

  • Discrete fields: Colored blue in Tableau. By the Oxford Dictionary’s definition, discrete means independent of other things of the same type. When placed on a Tableau visualization, discrete fields usually create a header – similar to a column header in a spreadsheet.
  • Continuous fields: Colored green in Tableau. Again, using the Oxford Dictionary, continuous is defined as happening or existing for a period of time without being interrupted. When placed on a Tableau visualization, continuous fields create an axis – that is, they create a visual display of data.

One way to think about the relationship between discrete and continuous fields is that continuous fields are recording measurements and discrete fields are describing those measurements. In a statement, this can usually be phrased as continuous by discrete – for example, sales (continuous) by region (discrete), as shown in Figure 1.1.

Figure 1.1 – Demonstrating discrete and continuous fields

Figure 1.1 – Demonstrating discrete and continuous fields

Within these two main field types, there are additional field types that inform VizQL how to create a visual display when they are brought onto the Tableau canvas. These can be seen in Figure 1.2 and are as follows:

  • Number (decimal): A number that allows fractions. Represented by a # symbol in the Tableau UI.
  • Number (whole): An integer or a number with no decimals. Also represented by a # symbol.
  • String: A field that contains alphanumeric characters. Represented by abc.
  • Date: Tableau accepts several date formatting options. Represented by a calendar icon.
  • Date & Time: A date field with granularity down to the second of a day. Represented by a calendar icon plus an analog clock.
  • Geographical/Spatial: A field that can be plotted on a map. There are many subtypes of geographical fields, including country, state/province, city, postal/zip code, airport, congressional district, NUTS (Europe), and a latitude or longitude value. Represented by a globe icon.
  • Binary/Boolean: A field that takes a true/false or yes/no condition. Represented by a T/F icon.
Figure 1.2 – Additional field types

Figure 1.2 – Additional field types

Let’s open Tableau Desktop and connect to the Superstore sales 2022.csv file. This file contains the sample data that comes along with the Tableau installation. It is a sample (and fictional) retail dataset that is useful for demonstration and learning purposes. We will use this data throughout the book when we can. This will help you as you increase your Tableau learning journey, as most of Tableau’s training videos use the same data:

  1. Open Tableau Desktop.
  2. Click on the Connect to Data blue hyperlink near the top-left-hand side of the Tableau Desktop UI:
Figure 1.3 – Connect to Data in Tableau Desktop

Figure 1.3 – Connect to Data in Tableau Desktop

  1. You are now presented with many different options for data sources. We will discuss some of these in upcoming chapters. For now, find the To a file section and click on the Text file option. Navigate to the Superstore sales 2022.csv file in the location you saved it on your computer. Click Open.
  2. Tableau will bring the data in and bring focus to the Data Source tab, as follows:
Figure 1.4 – The data pane in Tableau Desktop

Figure 1.4 – The data pane in Tableau Desktop

The top part of the screen acts as a visual canvas where we can bring in additional data through relationships, joins, and unions. For now, we will look at the bottom part of the screen.

This part of the user interface is broken into two sections. The section on the left displays the metadata for the fields in the data source. The metadata list contains the type, field name, physical table from where the data is being queried, and remote field name.

Type is the Tableau field type, which allows VizQL to guide the analyst to the best visual display for the data. Field Name is Tableau’s attempt to take the remote field name and map it to a business-friendly name. In our dataset, the column names already translate easily to business-friendly names. Imagine if our source file had contained postal_code and not Postal Code in the first row. Tableau would automatically transform postal_code to Postal Code in the field name, making our data modeling job easier for us. We can always change the default name Tableau assigns to any field.

The section to the right contains a sample of data that will be queried, called the Table Details pane. By default, Tableau returns the first 100 rows as a sample. The sample size can be changed in terms of the number of rows to return.

Next up, we will look at what a table format looks like and why it is so important for Tableau.

The ideal data format for Tableau – table format

Tableau performs best and is easiest to use when every column in the source data corresponds to a single field and each row represents a record of data at the lowest level required for analysis. As you can see in the following screenshot (Figure 1.5), Tableau will put all the field names in columns, including the type of each field. This is the metadata in your data model. Every row below the row of field headers will contain data, with each row representing one record of data:

Figure 1.5 – The Tableau data format

Figure 1.5 – The Tableau data format

When data is structured in this manner, it allows Tableau to perform optimally based on query performance, the ease of building analyses, and combining data from different sources.

Tableau will automatically assume your data is stored this way when you connect to a new data source. We saw this in the previous exercise. Tableau took the first row from the comma-separated values file and used it to create field names, restarting after the comma separator. Next, Tableau assumed after the first carriage return that the second row would be a row of values, neatly falling into the columns above it. For each additional carriage return, Tableau assumed another data record.

It turned out that Tableau was right in the assumptions it made. Data prepared for analysis is often already stored in this format, with each row being a unique record of data, separated into individual fields based on fields in database tables or headings in flat files and Excel.

What happens when it isn’t? This puts an analyst in the position of creating more complicated analyses (calculations, sheets, dashboards, and stories) and query performance almost always suffers.

In the next section, we will look at one of the most common ways where we see data structured in a manner that is not Tableau-friendly.

Shaping data for Tableau

In the previous section, we looked at the data format that works best in Tableau. We will now look at one of the two main examples where data is shaped ineffectively for Tableau and how we can easily change it to the correct format before beginning our data analysis.

Time-based data, especially financial data, is often stored in Excel with the dates in the column headers, the fields spread across columns in the first row, and values falling in the cells in the intersection. We can see this in Figure 1.6. This is a planning sheet for sales targets for the year 2022 for category sales of our SuperStore data:

Figure 1.6 – SuperStore sales targets format

Figure 1.6 – SuperStore sales targets format

If we connect this data to Tableau, it gives us the following metadata:

Figure 1.7 – SuperStore target default metadata

Figure 1.7 – SuperStore target default metadata

Thinking back to having fields in columns, a field type, and values in rows, we can see that this interpretation of the data is not helpful at all. What we want are three fields of the following types:

  • Date: Type – date. There are 12 values, one for each month in 2022.
  • Category: Type – string. There are three values: Furniture, Office Supplies, and Technology.
  • Revenue target: Type – whole number

In order to get these fields into the right format, we need to pivot those date columns into rows and then rename two fields. Tableau makes this easy for us. Here are the steps:

  1. Open Tableau Desktop.
  2. Click on the Connect to Data blue hyperlink near the top left-hand side of the Tableau Desktop UI.
  3. Choose Microsoft Excel. Navigate to the SuperStore 2022 Budget.xlsx file in the location you saved it on your computer. Click Open.
  4. You will be presented with a screen similar to Figure 1.6.
  5. In the Table Details pane, click on the header of Jan-22, hold down the Shift key, and click on the Dec-22 header to select all the date columns. You might find this easier if you first collapse the metadata pane:
Figure 1.8 – Multiselecting columns in the Table Details pane

Figure 1.8 – Multiselecting columns in the Table Details pane

  1. Hold your cursor over the top-right-hand corner of the Dec-22 header to bring up the down arrow.
  2. Click on the down arrow to bring up the menu of options. Choose Pivot.
Figure 1.9 – Menu options from the Table Details pane

Figure 1.9 – Menu options from the Table Details pane

  1. You will now be presented with three fields. We are almost there!
Figure 1.10 – Table Details after the pivot

Figure 1.10 – Table Details after the pivot

  1. Our last step is to rename our three fields. We can do this by clicking on the field name and typing over the ones that are there, or by clicking on the same down arrow that we used in step 8 and using the Rename option.
  2. Let’s rename our fields as follows: F1 to Category, Pivot Field Names to Date, and Pivot Field Values to Sales Target:
Figure 1.11 – Table details after renaming

Figure 1.11 – Table details after renaming

  1. Our last step is making sure our field types are correct. Category is a string field (alphanumeric) so it should show Abc, which it does. Similarly, Sales Target is a number and Tableau has it correct. Date is showing as a string. We would really like this as a date field because Tableau has special date-handling capabilities to make analysis much easier for us. To change the Date field to a type of date, click on Abc over Date and change the field type to Date:
Figure 1.12 – Table details – changing the data type

Figure 1.12 – Table details – changing the data type

Don’t close this workbook; we will pick up from this point in the exercise in the next section, where we will combine data to our data model from other tables.

In this section, we looked at how we may need to reshape our data to make better data models in Tableau. In the next section, we will look at a use case where we need to add additional columns (fields) of data from other data sources to our model.

Connecting multiple tables to add new columns

Now that we understand the ideal format of data for Tableau, we can look at ways to expand the size of our data model by connecting to multiple tables. We have connected independently to two different tables in this chapter. We will now combine those tables, along with a third table.

There are two ways in which we can add multiple tables to a single data model. The first is by joining data. We join data when we want to add additional columns to our model for additional context. Think of our sales target data. We want to join that data to our SuperStore sales data to add the additional context of sales targets. That will allow analysts to create views to see how the different categories are performing against their targets.

The second way to add additional data to our model is by adding additional rows (with the same columns). The SuperStore data we used earlier in this chapter was for 2022. What if we wanted to add in sales from 2021? That is where a union would be the answer.

For our next exercise, it should be noted that we are creating a relationship to add the new column and not a join. Using a relationship allows Tableau to create the proper join at runtime based on the data being analyzed. You can think of a relationship as a dynamic, smarter join. We will explore the details and differences between relationships and joins in Chapter 7, Chapter 8, and Chapter 9.

To add the additional column of sales target, we will pick up from the end of the previous exercise:

  1. Click on the blue Add hyperlink to the right of Connections:
Figure 1.13 – Clicking the Add hyperlink

Figure 1.13 – Clicking the Add hyperlink

  1. Select To a file | Text file | Superstore Sales 2022.csv | Open.
  2. Under Files, drag Superstore Sales 2022.csv onto the canvas until the orange noodle shows, and then let go.
  3. At this point, Tableau tries to create a relationship between these tables. Sometimes, Tableau can figure out how to create this relationship. In this case, we can see Tableau was unable to do this from the exclamation mark in a red triangle:
Figure 1.14 – Creating a relationship between two tables

Figure 1.14 – Creating a relationship between two tables

  1. We need to tell Tableau that the two relationships it should create are Date with Order Date and Category with Category. Let’s relate the two category fields first. We do this by selecting Category and Category1 and making sure the operator is set to equals (=):
Figure 1.15 – Creating a relationship by linking fields

Figure 1.15 – Creating a relationship by linking fields

  1. To complete the relationship, we will also need to link Date fields to ensure that the sales targets are aligned with the proper dates for analysis. In this specific case, the dates are not at the same grain (one is at the month level and one is at the day level). This will require us to create a relationship calculation, which we will explore in future chapters.
  2. Now that we have joined additional data sources (tables) to add additional columns for analysis, we will explore adding new rows of data through a union.
  3. Hover your cursor on the right side of the Superstore Sales 2022.csv file rectangle on the canvas, and click on the down arrow when it appears. Select Convert to Union…:
Figure 1.16 – Convert to Union…

Figure 1.16 – Convert to Union…

  1. Drag the Superstore Sales 2021.csv file from the files section on the canvas into the union dialog box. After clicking OK, all the rows from both 2021 and 2022 will be available for analysis:
Figure 1.17 – Dragging files to the union dialog box

Figure 1.17 – Dragging files to the union dialog box

We have now covered the two core methods of expanding our data model through relationships and unions.

Summary

Congratulations! We are only one chapter in and you have already created a data model in Tableau. We now know how to structure the ideal data model for Tableau. We looked at connecting to data, exploring how Tableau interprets that data, reshaping data for analysis, and adding additional data. With this learning foundation, we are well-positioned for more advanced topics in the following chapters, where we will build security, maintainability, and robustness into our data models.

In the next chapter, we will look at Tableau licensing models and how licensing impacts data modeling. We will also explore the difference between embedded and published data models and the difference between data models that connect live to data and those that extract data for analysis.

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Key benefits

  • Master best practices in data modeling with Tableau Prep Builder and Tableau Desktop
  • Apply Tableau Server and Tableau Cloud to create and extend data models
  • Build organizational data models based on data and content governance best practices

Description

Tableau is unlike most other BI platforms that have a single data modeling tool and enterprise data model (for example, LookML from Google’s Looker). That doesn’t mean Tableau doesn’t have enterprise data governance; it is both robust and highly flexible. This book will help you effectively use Tableau governance models to build a data-driven organization. Data Modeling with Tableau is an extensive guide, complete with step-by-step explanations of essential concepts, practical examples, and hands-on exercises. As you progress through the chapters, you’ll learn the role that Tableau Prep Builder and Tableau Desktop each play in data modeling. You’ll also explore the components of Tableau Server and Tableau Cloud that make data modeling more robust, secure, and performant. Moreover, by extending data models for Ask and Explain Data, you’ll gain the knowledge required to extend analytics to more people in their organizations, leading to better data-driven decisions. Finally, this book will guide you through the entire Tableau stack and the techniques required to build the right level of governance into Tableau data models for the correct use cases. By the end of this Tableau book, you’ll have a firm understanding of how to leverage data modeling in Tableau to benefit your organization.

Who is this book for?

This book is for data analysts and business analysts who are looking to enhance their data skills, offering a broad foundation to build better data models in Tableau for easier analysis and better query performance. It will also benefit anyone responsible for making secure data available to their organization through Tableau, such as data stewards and others who work to take enterprise data and make it more accessible to business analysts.

What you will learn

  • Showcase Tableau published data sources and embedded connections
  • Apply Ask Data in data cataloging and natural language query
  • Understand the features of Tableau Prep Builder with the help of hands-on exercises
  • Model data with Tableau Desktop using examples
  • Formulate a governed data strategy using Tableau Server and Tableau Cloud
  • Optimize data models for Ask and Explain Data
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Table of Contents

21 Chapters
Part 1: Data Modeling on the Tableau Platform Chevron down icon Chevron up icon
Chapter 1: Introducing Data Modeling in Tableau Chevron down icon Chevron up icon
Chapter 2: Licensing Considerations and Types of Data Models Chevron down icon Chevron up icon
Part 2: Tableau Prep Builder for Data Modeling Chevron down icon Chevron up icon
Chapter 3: Data Preparation with Tableau Prep Builder Chevron down icon Chevron up icon
Chapter 4: Data Modeling Functions with Tableau Prep Builder Chevron down icon Chevron up icon
Chapter 5: Advanced Modeling Functions in Tableau Prep Builder Chevron down icon Chevron up icon
Chapter 6: Data Output from Tableau Prep Builder Chevron down icon Chevron up icon
Part 3: Tableau Desktop for Data Modeling Chevron down icon Chevron up icon
Chapter 7: Connecting to Data in Tableau Desktop Chevron down icon Chevron up icon
Chapter 8: Building Data Models Using Relationships Chevron down icon Chevron up icon
Chapter 9: Building Data Models at the Physical Level Chevron down icon Chevron up icon
Chapter 10: Sharing and Extending Tableau Data Models Chevron down icon Chevron up icon
Part 4: Data Modeling with Tableau Server and Online Chevron down icon Chevron up icon
Chapter 11: Securing Data Chevron down icon Chevron up icon
Chapter 12: Data Modeling Considerations for Ask Data and Explain Data Chevron down icon Chevron up icon
Chapter 13: Data Management with Tableau Prep Conductor Chevron down icon Chevron up icon
Chapter 14: Scheduling Extract Refreshes Chevron down icon Chevron up icon
Chapter 15: Data Modeling Strategies by Audience and Use Case Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

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Vamsi Chakravarthy Jan 30, 2023
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I am a tableau developer myself and have been in the IT industry for almost 10 years now. I read a lot of blogs and journals on data modeling and etl and business analytics and tableau in particular and I can say with confidence that this ebook gives you so many new insights to anyone that's new to Tableau and also to experienced people alike. Data modeling, business intelligence concepts are explained in detail and with practical use cases.A detailed writing, I recommend this book to anyone in IT
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Peter Jonsson Feb 28, 2023
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Just finished ’Data Modeling with Tableau’ by the amazing Kirk Munroe and I’m thrilled by the massive loads of great insight in this book; perfect balance of being comprehensive and easy to follow along. Read it cover to cover, did all exercises & I’m impressed with the attention to details, although very easy to follow along. I recommend it to any aspiring Tableau Data Rockstar analysts out there.
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iris Feb 28, 2023
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"Data Modeling with Tableau" by Kirk Munroe is a comprehensive guide for anyone interested in learning how to create effective data models using Tableau and Tableau Prep. The book provides a step-by-step approach to building data models, starting from understanding the basics of data modeling, to designing and implementing advanced data models.One of the strengths of the book is the way it breaks down the process of data modeling into simple and easy-to-understand steps. The author uses a practical approach to illustrate how to create data models. This helps readers to not only understand the concepts but also see how they can be applied in real-life situations.The book covers a wide range of topics, from designing simple data models to more complex data models with multiple data sources. The author provides a detailed explanation of how to connect to different data sources, and how to combine and transform data to create meaningful insights.Another strength of the book is the clear and concise writing style. The author explains complex concepts in a simple and straightforward manner, making it easy for readers to follow along.Overall, "Data Modeling with Tableau" is an excellent resource for anyone who wants to learn how to create effective data models using Tableau. The book is well-organized, easy to follow, and provides a wealth of practical advice and guidance. I highly recommend this book to anyone who wants to take their Tableau skills to the next level.
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Mark Bradbourne Jan 10, 2023
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There are so many books about Tableau on the market, so I can’t lie and say I wasn’t concerned about overlap in the library, but I was pleasantly surprised.This deep dive in to the why, and how of preparing data for effective use in Tableau was something I wasn’t expecting but after reviewing the book it’s an amazing gap to fill in the Tableau library.This isn’t the sexy part of Tableau, but if you want to be successful, it’s the necessary part. Being able to construct secure, concise, and effective data sources for your organization is critical for success. This book will help you get there.If you are responsible for your data engineering, data source creation, or just want to be able to have a deeper understanding of data sources for your own analysis, this one should be on your shelf.
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Donal O'Beirne Jan 07, 2023
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So many books and articles on Tableau focus on the visualization practice but leave data modelling as a neglected stepchild, secondary to the images and charts being generated.This is a shortcoming for all enterprise and corporate users, who need to first build effective and performative data models in tableau using a variety of data sources and types. Indeed one of the challenges with Tableau is getting the data into it using source data rather than extracts. One other challenge is that it is assumed that users have access to the full stack, including Prep, Catalog, Data Management, and more.This book finally addresses many of these issues. It takes you though all the secrets, tips, and capabilities of Tableau Desktop, Server, and Online in building fast and effective data models. This book is an excellent primer for data analysts & scientists, product owners, and business analysts to be able to build effective corporate dashboards.As a Tableau Product Owner, what I appreciate the most is that regardless of your technology stack that you can get the most of the platform using this book. If you only have Tableau Desktop or Server, and not the add-ons like Prep, you can still build out well performing models. It's not a sales pitch, rather it's a master class for experts and new users alike.I'm going to be getting several copies for my office, and I strongly support the content of this book as part of your enterprise learning plan for Tableau.Donal O'BeirnePrincipal Product OwnerATB Financial
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'

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
What is custom duty/charge? Chevron down icon Chevron up icon

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