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Learning Tableau 10
Learning Tableau 10

Learning Tableau 10: Business Intelligence and data visualization that brings your business into focus , Second Edition

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Learning Tableau 10

Chapter 2. Working with Data in Tableau

Tableau offers us the ability to connect to nearly any data source. It does so using a unique paradigm that allows it to leverage the power and efficiency of existing database engines with an option to extract data locally. This chapter focuses on the foundational concepts of how Tableau works with data; we will also cover the following topics:

  • The Tableau paradigm
  • Connecting to data
  • Managing data source metadata
  • Working with extracts instead of live connections
  • Tableau file types
  • Joins and blends
  • Filtering data

The Tableau paradigm

Tableau connects directly to native data engines and also includes the option to extract data locally. The unique experience of working with data in Tableau is a result of Visual Query Language (VizQL).

VizQL was developed as a Stanford Research Project focusing on the natural ways by which humans visually perceive the world and how those ways can be applied to data visualization. We naturally perceive differences in size, shape, spatial location, and color. VizQL allows Tableau to translate your actions, as you drag and drop fields of data in a visual environment, into a query language that defines how the data encodes those visual elements. You will never need to read, write, or debug VizQL.

Tableau will generate the Visual Query Language behind the scenes as you drag and drop fields onto various shelves defining size, color, shape, and spatial location. This allows you to focus on visualizing data and not on writing code!

One of the benefits of VizQL is that it provides...

Connecting to data

There is virtually no limit to the data Tableau can visualize. Each successive version of Tableau adds new native connections. Tableau continues to add native connectors for cloud-based data and recently included the Web Data Connector, which allows you to custom-build a connector for any online data you wish to retrieve. Additionally, for any database without a native connection, Tableau gives you the ability to use a generic ODBC connection. The Extract API allows you to programmatically extract and combine any data source(s) for use in Tableau.

You may have multiple data source connections to different sources in the same workbook. Each connection will show up under the Data tab on the left sidebar.

This section will focus on a few practical examples of connecting to various data sources. We won't cover every possible connection, but we will cover several that are representative of others. You may or may not have access to some of the data sources in the following...

Managing data source metadata

Data sources in Tableau are a definition of the connection(s). In addition to storing information about the connection (such as database server name, database, and/or file names), the data source also contains information about all the fields available (such as field name, data type, default format, comments, aliases, and so on). Often, this data about the data is referred to as metadata.

Right-clicking on a field in the data pane reveals a menu of metadata options. Some of these options will be demonstrated in the following exercise, others will be explained throughout the book. The following are some of the options available via right-click:

  • Rename the field
  • Hide the field
  • Change aliases for values of a dimension (other than date fields)
  • Create calculated fields, groups, or sets
  • Split the field
  • Change the default use of a date or numeric field to either discrete or continuous
  • Redefine the field as a dimension or a measure
  • Change the data type of the field
  • Assign a...

Working with extracts instead of live connections

Most data sources give the option of either connecting live or extracting the data; however, some cloud-based data sources do require an extract. Conversely, OLAP data sources cannot be extracted and require live connections.

When using a live connection, Tableau issues queries directly to the data source (or uses data in the cache if possible). When you extract the data, Tableau pulls some or all of the data from the original source and stores it in a Tableau Data Extract file (.tde). Extracts extend the way in which Tableau works with data. Consider the following diagram:

Working with extracts instead of live connections

The fundamental paradigm of how Tableau works with data does not change, but you'll notice that Tableau is now querying and getting results from the extract. Data can be retrieved from the source again to refresh the extract. Thus, each extract is a snapshot of the data source at the time of the latest refresh. Extracts offer the benefit of being portable and extremely...

The Tableau paradigm


Tableau connects directly to native data engines and also includes the option to extract data locally. The unique experience of working with data in Tableau is a result of Visual Query Language (VizQL).

VizQL was developed as a Stanford Research Project focusing on the natural ways by which humans visually perceive the world and how those ways can be applied to data visualization. We naturally perceive differences in size, shape, spatial location, and color. VizQL allows Tableau to translate your actions, as you drag and drop fields of data in a visual environment, into a query language that defines how the data encodes those visual elements. You will never need to read, write, or debug VizQL.

Tableau will generate the Visual Query Language behind the scenes as you drag and drop fields onto various shelves defining size, color, shape, and spatial location. This allows you to focus on visualizing data and not on writing code!

One of the benefits of VizQL is that it provides...

Connecting to data


There is virtually no limit to the data Tableau can visualize. Each successive version of Tableau adds new native connections. Tableau continues to add native connectors for cloud-based data and recently included the Web Data Connector, which allows you to custom-build a connector for any online data you wish to retrieve. Additionally, for any database without a native connection, Tableau gives you the ability to use a generic ODBC connection. The Extract API allows you to programmatically extract and combine any data source(s) for use in Tableau.

You may have multiple data source connections to different sources in the same workbook. Each connection will show up under the Data tab on the left sidebar.

This section will focus on a few practical examples of connecting to various data sources. We won't cover every possible connection, but we will cover several that are representative of others. You may or may not have access to some of the data sources in the following examples...

Managing data source metadata


Data sources in Tableau are a definition of the connection(s). In addition to storing information about the connection (such as database server name, database, and/or file names), the data source also contains information about all the fields available (such as field name, data type, default format, comments, aliases, and so on). Often, this data about the data is referred to as metadata.

Right-clicking on a field in the data pane reveals a menu of metadata options. Some of these options will be demonstrated in the following exercise, others will be explained throughout the book. The following are some of the options available via right-click:

  • Rename the field

  • Hide the field

  • Change aliases for values of a dimension (other than date fields)

  • Create calculated fields, groups, or sets

  • Split the field

  • Change the default use of a date or numeric field to either discrete or continuous

  • Redefine the field as a dimension or a measure

  • Change the data type of the field

  • Assign a geographic...

Working with extracts instead of live connections


Most data sources give the option of either connecting live or extracting the data; however, some cloud-based data sources do require an extract. Conversely, OLAP data sources cannot be extracted and require live connections.

When using a live connection, Tableau issues queries directly to the data source (or uses data in the cache if possible). When you extract the data, Tableau pulls some or all of the data from the original source and stores it in a Tableau Data Extract file (.tde). Extracts extend the way in which Tableau works with data. Consider the following diagram:

The fundamental paradigm of how Tableau works with data does not change, but you'll notice that Tableau is now querying and getting results from the extract. Data can be retrieved from the source again to refresh the extract. Thus, each extract is a snapshot of the data source at the time of the latest refresh. Extracts offer the benefit of being portable and extremely...

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Key benefits

  • Create stylish visualizations and dashboards that explain complexity with clarity
  • Learn effective data storytelling to transform how your business uses ideas and makes decisions
  • Explore all the new features in Tableau 10 and start to redefine what business analytics means to your organization

Description

Tableau has for some time been one of the most popular Business Intelligence and data visualization tools available. Why? Because, quite simply, it’s a tool that’s responsive to the needs of modern businesses. But it’s most effective when you know how to get what you want from it – it might make your business intelligent, but it isn’t going to make you intelligent… We’ll make sure you’re well prepared to take full advantage of Tableau 10’s new features. Whether you’re an experienced data analyst that wants to explore 2016’s new Tableau, or you’re a beginner that wants to expand their skillset and bring a more professional and sharper approach to their organization, we’ve got you covered. Beginning with the fundamentals, such as data preparation, you’ll soon learn how to build and customize your own data visualizations and dashboards, essential for high-level visibility and effective data storytelling. You’ll also find out how to so trend analysis and forecasting using clustering and distribution models to inform your analytics. But it’s not just about you – when it comes to data it’s all about availability and access. That’s why we’ll show you how to share your Tableau visualizations. It’s only once insights are shared and communicated that you – and your organization – will start making smarter and informed decisions. And really, that’s exactly what this guide is for.

Who is this book for?

Got data? Not sure what to make of it? This is the guide for you – whether you’ve been working with Tableau for years or are just beginning your adventure into business analytics.

What you will learn

  • Find out how to build effective visualizations and dashboards
  • Prepare and clean your data so you can be sure Tableau is finding answers to your questions – not raising more problems
  • Discover how to create advanced visualizations that explain complexity with clarity and style
  • Dig deeper into your data with clustering and distribution models that allow you to analyze trends and make forecasts
  • Learn how to use data storytelling to aid decision-making and strategy
  • Share dashboards and visualizations to cultivate a culture where data is available and valued
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Publication date : Sep 30, 2016
Length: 432 pages
Edition : 2nd
Language : English
ISBN-13 : 9781786466358
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Product Details

Publication date : Sep 30, 2016
Length: 432 pages
Edition : 2nd
Language : English
ISBN-13 : 9781786466358
Vendor :
Tableau
Category :
Languages :
Tools :

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Table of Contents

11 Chapters
1. Creating Your First Visualizations and Dashboard Chevron down icon Chevron up icon
2. Working with Data in Tableau Chevron down icon Chevron up icon
3. Moving from Foundational to More Advanced Visualizations Chevron down icon Chevron up icon
4. Using Row-Level, Aggregate, and Level of Detail Calculations Chevron down icon Chevron up icon
5. Table Calculations Chevron down icon Chevron up icon
6. Formatting a Visualization to Look Great and Work Well Chevron down icon Chevron up icon
7. Telling a Data Story with Dashboards Chevron down icon Chevron up icon
8. Deeper Analysis – Trends, Clustering, Distributions, and Forecasting Chevron down icon Chevron up icon
9. Making Data Work for You Chevron down icon Chevron up icon
10. Advanced Visualizations, Techniques, Tips, and Tricks Chevron down icon Chevron up icon
11. Sharing Your Data Story Chevron down icon Chevron up icon

Customer reviews

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Donald Q Mar 18, 2018
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Very good information in this book. Well written. Assumes some experience with computers and programming.
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Not Hidden Dec 20, 2018
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Learning on the job just wasn't cutting it at all as everyone here seems to fumble thru development. I felt much more confident after reading chapter 1.
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Well done. Using it to teach Tableau around the country.
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MN mom May 27, 2019
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Purchased for online course. Pleased with the book.
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S. Tescione Jun 15, 2017
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This book made my job so much easier! We are implementing three new software applications: Tableau, Webi, and Alteryx. These books have been enormously helpful in figuring out what the lessons and job aids should be for new users. This book is easy to understand and I can use the practice files to set up the lessons and figure out content and then use my company's data sources for the actual course materials.This book has been a lifesaver on a project with a short timeline. My only complaint is that entirely too much space is spent on how Tableau works with lots of explanation. Frankly my dear, my users don't give a hoot. They don't want to know what it is and how it works. They want to know how to use it. So it could use a lot less on technical explanation and a lot more "cookbook".
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