Tableau 10: Practical and Concise Part 2 | Junaid Athar | Skillshare

Tableau 10: Practical and Concise Part 2

Junaid Athar

Tableau 10: Practical and Concise Part 2

Junaid Athar

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8 Lessons (33m)
    • 1. Tableau Paradigm

    • 2. Data Source Type

    • 3. Server and Cloud Connections

    • 4. Connecting to Live Data Example

    • 5. Using Extracts

    • 6. Joins and Blends

    • 7. Blends and Filters

    • 8. Part 2 Summary

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About This Class

So, you've heard a lot about Tableau 10, but you don't know how to get started?  This Udemy course is exactly what you need! This course will teach you all the fundamentals you need. Trust me, you won't need any other course to reach the intermediate/advanced level after this course.

In this course, you'll learn how to make the data work for you so that you can aggregate, analyze and visualize your data. 

The course's curriculum goes as such: 

  • You'll get started with line and bar charts
  • You'll learn to use extracts, joins and blends 
  • You'll learn to advanced techniques such as Gantt charts, Treemaps, circle charts..
  • You'll master row and aggregate calculations 
  • You'll learn table calculations
  • You'll learn to format your data for visual impact
  • You'll know how to tell a story with Tableau

The course is aimed to be as complete as possible. It will include a lot of practice so that nothing stays theoretical, and the quality is in full HD, so that you can see everything on-screen.

NOTICE: I'll keep adding more and more content to the course to make it the best Tableau course on Udemy.

So, what are you waiting for?! Click on BUY NOW and LET'S GET STARTED!

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Junaid Athar


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1. Tableau Paradigm: welcome to Part two of tableaux for beginners. In Part two, we will work with data and review all the data types. Tableaux offers the ability to connect to virtually any data it does. It does so using a unique paradigm, which we will discuss further. It allows you to leverage the power and efficiency of the existing database engine with an option to extract that on locally. This part of the course focuses on the foundational concepts of how tableau works, with data including the following the tableau paradigm connecting to data, working with extracts, meta data joins and blends and filtering data. So first, let's review the tableau. Paradigm tableaux connects directly to Native Data engines and also include the option to extract data locally. The unique experience of working with the data in tableau is a result of viz que el visual query language vis que el was developed at a stand for as a Stanford research project, focusing on the natural ways humans visual life, which visually perceive the world, and how that could be applied to dead of visit visuals. We naturally see differences in size, shape, spatial location and color vis que el allows tableau to translate your actions as you drag and drop fields of data in a visual environment into a query in language that defines how the data encodes those visual elements you will never need to read, write or debug biz que el As long as you drag a drop fields onto various shelves defining size, color, shape and spatial location, tableaux will generate the visual query language behind the scenes. This allows you to focus on visualising the data. One of the benefits of SQL is that provides a common way of describing how the arrangements of various fields and the view defining Cleary of the data. This common baseline can then be translated into a numerous flavors of sequel and E x t equal, which is what stands for a tableau. Query language, which is used for extracting data tableau, automatically performs the translation of SQL and optimized Query to run natively by source data engine. This summarizes the data at the layer is needed and its simplest, the tableau paradigm of working with data is summarizing dead data from the data source 2. Data Source Type: Welcome back to the next section of tableaux for beginners. Here we will talk about connecting to data tableaux has a few different ways To connect to your data, you can connect to a tableau data extract, which is a dot T d E file containing data that was extracted from an original source. When you connect to adopt TD file, the connection retains information about the DOT TV file, but not about the original source. Microsoft access is the next debt type. This is a dot mdb or dot a C C D B database file created in access after Microsoft access. We have another Microsoft source called Microsoft Excel. I'm sure a lot of you're familiar with this data source. This is a dot XLs or dot x L s x or dot x l S M spread. She created an Excel. We also have text files, which generally are a delimited text file, Most commonly a dot txt dot CSB or dot tabs. Multiple files in a single directory can maybe joined together in the connection window. Um, these text files can be delimited with either a tab, a comma or any other character that you choose after the texts. Well, we have statistical file. This is and dot s a V. That s a S seven, db 80 dot org or dot Our data these files are generated by SAS or are have low also has the option to select other files. So you may choose at that T w b or dot tw B X, which are tableau workbooks, and you can extend these data sources within your next workbook. 3. Server and Cloud Connections: in the previous section, we reviewed how to connect to data sources, which are files now We can use Week in Review how to connect a data on a server database servers, fish such as sequel server, Oracle 11 db two Vertical host data on one or more server machines and use powerful database engines to store aggregate sort and serve data based on queries from client applications. Tableaux can leverage the capabilities of these servers to retrieve data. Four. Visualizations and analysis. Alternately, Dennett can be extracted from these sources of stores stored and tableau Data extract dot TV files An example of connecting to the server Source. Data Consider connected to sequel server as Microsoft as soon as a Microsoft sequel Connection is selected, the interface that displays the options for multiple initial configurations. The sequel Server Connection requires a server name as well as authentication information. A database administrator can configure sequel server to use Windows authentication or a sequel, server, user name and password. With sequel server. You will also optionally allow reading of uncommitted data. This could potentially improve performance but may also lead to unpredictable results if the data is being inserted, Update or deleted at the same time tableau is querying in order to maintain high standards of security. Tableau will not save a password as part of the data source conduction. This means that if you share a workbook using a live connection with somebody else, they will need to have the credentials to access the data. This also means that when you've first opened the workbook, you will need to re enter your password for any connection requiring a password. Once you click on Connect, you will see that screen there is very similar to the connection you saw for Excel or Tableau. So the main difference is between the database and a flat file is that you can use a live connection. Next, we have data in the cloud. Certain data connections are made to data that is hosted in the cloud. These include Amazon, Google, Google Bakery, Salesforce and others. In many cases, you will want to read the documentation provided by the service provider toe, understand the connection and structure of the data. It is beyond the course of this beyond the scope of this course to cover such connections in depth. But as an example of a cloud data source. We can explore connecting to Google Data Analytics. Google Analytics allows you due to analyze data about website traffic and Web page visits. Google provides online dashboards, but tableau can connect directly to Google Analytics to allow you to build your own custom dashboards. The Google be aware that some dimension the measures and Google analytics are not compatible with each other. Selective certain dimensions or certain measures may cause others to be disabled. This is expected based on the structure of the data within Google analytics. Google Analytics is one of several connections that did not allow for live connections. Instead, the data is pulled from Google Analytics into a local extracts. You can refresh the extract at any time by right clicking on the data source in the data window or selecting it from the data menu and selecting extract and refresh any changes to the connection. Properties will cause the editor be re extracted and extracts are explained in detailed in the following section. You can also make short cuts for connecting to data. This allows you to connect very quickly so you can paste data from the clipboard. If you have copy data from a spreadsheet on a table or Web or a text file, you can often pasted right data directly into tableau. This is done using control V or data pace option from the menu. The data will be stored as a file, and you will be a learned to its location when you have saved the worksheet, in some cases pasting the data intact, Excel first and then copy from Excel and paste it into tableau can yield more consistent results. Alternatively, you might also try to first pasting it to any note pad and then pasting it into tableaux. So that's the overview of the different server and cloud connection you can create. 4. Connecting to Live Data Example: Welcome back to tableau for beginners. In this section, we will connect to a data source a sample data source and walked through all the options available in the data window similar to how we reviewed the first chapter. So will we will connect. First, we will create a new tableau canvas. And from here we will connect to a data source by clicking on connect to data within your tab. My tableau repository folder. We'll choose an Excel file within your my tableau repository folder. There's a file called sample superstore dot XLs X. So I got my tablet repository data sources 10.4 joined the US version English and sample superstore data set, click open. And this would bring you to your data source window. You There are multiple sheets within this Excel file. You can choose to rename the file, so we will just call it superstore data. We can choose what kind of data source it is. Weaken. Um, change the connection name, too. Oh, and weaken, rename. The file will leave that At this we condemn drag the sheets and that we want weaken select the tables, everyone's and the ranges that we choose to pull in. So in this section, we're going to pull in orders and returns. So this brings all the order data and it will also choose their returns. So tableaux automatically creates a joint between the two tables. Based on any matching columns, you can choose to make this a left, join and outer left. Join a right Jonah Flatter joint. I will leave this as an inner joy, the section below to the left. So we'll leave that as an inter join. And we don't want to create a new joint clause. So we compose that the section on the left list, the different tables and use available for this connection. So now we've created a new data source. We've joined two tables within that data source. We've identified the connection to be live. This means that every time you open tableau, this will connect rig will update the data available in the data source. If you choose an extract, that means it will not refresh the data. Next time you open up tableau. If you need to edit the connection any time, select data from the menu and then click on edit data Sources. Data tabal Server data and choose to edit the data source. I'll turn it. You may right, click on any data source under the data view and click on edit data. So then you can go to she won and start creating your application. So that's the very first introduction to connect to a brand new data source. 5. Using Extracts: in the In the last video, we review how to connect to a data source and pull in the diet data from exile in tree to join in this video, we will use that same data source and create an extract. So under the data source, um, under the data source Tabb in the connections, you can use a lot of connection or an extract. So once you choose an extract, what this does is it creates a tableau specific compressed extraction file. So then you can save it as sample data extract. Save that don't automatically get saved, and then you can create your visual in the front end. Let's look at this same data set by country by subcategory. Now you can go and shoes to filter on the extracts under the data tab. So you go to data, go to your orders for six sample superstore data and choose the extract option and use the extract data option. Over here, you can add a filter so we can choose to only select the east and the west regions, and this automatically filters the entire data set. It compresses the data set and makes it much significantly more efficient. So some of the benefits of an extract are the performance. Extracts are Callum are and very efficient to query. Extracts are structured so that they can be looked loaded quickly into memory without additional processing and move between memory and disk storage. Hence the size not limit to the amount of RAM available. Many calculated fields are materialized in the extract. That means they're pre calculated values are stored in the extract and can often be read faster than executing the calculation. Every time the query is executed prior to create the extract hide unused field so that you know it's it's Summarize it, if possible, uses subset of data. For example, if you have a historical data for the last 10 years, but you only two years, you can put a filter on the day field, optimizing extract after creating or editing calculated fields by deleting the height of the hidden fields. Lastly, store extracts on solid state disks or drives our D fragmented regularly. Extracts are great for portability and security. Imagine that you are working with a database on a server on the local network at the office , but you have a long flight that evening. I would like to wrap up some analysis on the plane. Normally, you'd have to be on site to work with the day or get you'd have to be PN into your network and then connect while you're flying with an extract. You can take the data with you without needing an Internet connection to use the data. Ah dot TV file contains all the data extracted from the source. When you save a workbook, you may save it as a tableau workbook, T w b or a tableau. Package workbook T W b X, ah workbook dot tw b Contains definitions for all the connections, field visuals and dashboards, but does not contain any or any data or actual files such as images. When you save a package workbook, the TT, WB X, any extracts and actual files are packaged together in a single file for the workbook. Ah, package workbook. Using extracts can be open with tableau, desktop or tableaux reader and published a tableau, public and tableau. I'm line. There are a couple of security concerns when you use an extract, the extracts is made using a single set of credentials, so any security layers that you have inserted into the data are very limited and really aren't applied. An extract does not require a user name or password. All did, and the extract can be read by a new one because it's an extract at nine. A life connection. Remember in the live connection. Every time you open the file, you'll have to enter your credentials to connect to the data source. Any data for visible fields contain an extract dot TV or in an extract containing a package workbook dot TV Baby X can be accessed even if the data is not shown in the visuals. Be very careful when distributing extracts or package workbooks containing sensitive or proprietary data. The story is told of an employer who sent a package workbook containing HR data toe. Others in the company. Even though none the dashboards displayed the data. The extracts contained it and wasn't long before everyone. The company knew everyone else's salary, and the original individual was no longer when working with a company. So a couple, um, ways. You can use an extractor when you should use the extract. You should consider various factors when determining whether or not to use an extract. In some cases, you won't have an option. For example, Old Lap requires a live connection, and some cloud based attic sources require an extract. In other cases you'll wanted. Evaluate the options in general. Use an extract when you need better performance than you can get with a live connection. You need the data to a portable. Use an extract. If you are using a legacy driver or Alexey Connection, you want Teoh. Use extract when you want to share a package workbook. This is especially true if you want to share a package of workbook. There was someone who uses the free tableau reader, which, which which can only read packaged workbooks with extracts in general, did not use an extract. When you have sensitive data, do not use an extract when you need to manage security based on credentials. Don't use an extract when you see changes in the data source updating a real time and lastly, don't use an extract when the volume of data makes the time required to build the extracts and impractical the number of records that could be extracted in a reasonable amount of time will depend on many factors such as a data type, the type of feels the number of fields, the speed of the data source and a network bandwidth. So those are some of the things you have to keep in mind when working with data extracts. Another thing you want to review is a made it metadata and data sharing into the connections. Metadata refers to information about the data itself. Nearly every database contains some metadata. This includes lists of the table's fields for the data source and what type of data there is. For example, numeric string or date tableaux provides an additional layer of a metadata that makes it very easy. Customized connections or change a data source attributes of the data. So this is good to use when you're migrating applications from Deb to test or test production. So those are the items you want to keep in mind when using a extract 6. Joins and Blends: all right now I want to talk about joins and blends. Joining tables and blending data sources are two different ways. Toe Link related data together in Tableau joins and perform two linked data of tables together within a single data source. While blends air perform toe linked together multiple data sources. That's the main difference between joints. Bloods joining tables. Most tables have multiple table. Most databases have multiple tables off data. They're related in some way. Imagine you have been asked to analyse data in a simple database at a hospital with four main tables. The primary table is a visit table that has a record for every visit of a patient to the hospital and includes details such as a start, date and date and type of visit. It also contains Key feels that Lincoln visit to a patient. When you connect to the database and tableau, you'll have seal. You'll see multiple tables. You have to keep in mind off referential integrity. That's referent Rachel it integrity. Tell the database how to join the tables together. So now let's review the types of joints so the inner joint is the upper left hand corner and only records that matched the joint condition from both the table on the left and the table on the right will be kept in this example. On Lee. Only three matching records will be shown, so we'll review the examples. Ah, left joined All records from the table and left will be kept while Onley matching records. The right table will show up in the results. That, and any unmatched records from the right table will be completely removed from the results that right join is the opposite of a left. Join all records from the left table on the on the right will be kept matching. Records from the table on the left will result in values while unmatched records will contain don't values. Lastly, the full our joint and that that creates, ah, a data set that has results from both tables. And if there's no matching data and the other table, it'll create Mel values For those. Now, let's talk about blending data. Blood is a powerful, innovative feature tableau. It allows you to use data from multiple data sources in the same view. Often these sources, maybe of different types, for example, you could blend data from Oracle. With data from Excel, you can blend Google Analytics data with Dave that from access data blending also allows you to compare data at different levels of details. Some advanced uses of data blended will be covered later this in this tutorial in Section eight. For now, let's consider the basics and simple example. Data blending is done at an aggregate level and involves different query sent to each data source. Unlike joining, which is done at the role level and involved a single quick Richard to get a single data source, a simple data blending process involves several steps we can. We understand that the different steps include tableau issues. A query to the primary day resource. The underlying data engine returns, aggregate results, tableau issues. Another query to the secondary data source. This query is filtered based on the set of values. Return from the primary data source for Dimension that link the two tables. The online data engine returns aggregate results from the secondary data source, so those are the steps to get blended. Data Tableau blends the results of the two Coreys together in the cash. It is important to remember to note how data blending is different from joining. Joined Our accomplished in a single query and results are matched row by row data blending occurs by issuing two separate queries and then blending them to get together to the aggregate results. So we've talked about joins and blending data. Let's talk a little bit about linking fields. Linking field are dimensions that are used to match data blending between primary and secondary data. Sources linking fields defined the level of details for the secondary source linking field are automatically assigned a field match my name and type between data sources. Otherwise, you could manually assign relationships between fields by selecting data at it relationships from the data venue. So in the next video, we'll show an example of a joint and an example of a data blood. 7. Blends and Filters: So now we want to review an example of data blending. In this example. We have a data source from superstore sales, and we have coffee chain sales. We've always set the blend at the regional level. Thus you see, the two sources have matching regions. Now, let's say every polling that we want to see the same relationship at the state level. Now both these data sources have state in them. But the blend is not set at the state level. So when I pull in states the superstore sales data source, you can see the detail. By state. However, the coffee chain sales are the same for each state for each region because we've only set the blend at the regional level. So to resolve this will need to set the blend at the region and state level. To do that, we can go to data added relationships, and here we see it's on Lee. At the regional level, we add a new blend at the state level. Click OK, click OK, and you can see now in our coffee chain sales example, we have proper sales for each state. That's an example of using blends from two different data sources. Now let's talk about filters. Often you will want to filter data and tableau and order to perform analysis on a substantive dared data. Narrow your focus or drill into detail. Tableau Offers multiple ways of Filtered Data If you want to limit the scope of our analysis to a substantive data, you will filter the data at the source data. Source. Filters are applied before all other filters are useful. When you want to limit your analysis to a subset of data, these filters are applied before any other filters. So when your first important the data from the data source, you can apply filters to limit the data set next, our extract filters. These limit the data in the dot T D e file data. Source. Filters are often converted into extract filters if they're present when you extract the data. So we showed that in the couple sections ago, where we reviewed extracts last that we have custom sequel filters. These come here accomplished using a live connection with custom sequel that has a tableau parameter in the wear cloth. Well, look at that in the next section are actually two sections from now in part for additionally, you can apply filters on one or more views using one of the following techniques Dragon drop fields from the data window into the filter shock. So if you want to filter on a certain region, you can just drag it into their choose. I want to see only east or west. Click. OK, and now you only have east to west, north and south has been filtered out. You may also select an individual item keep only and now you see that stage filter hasn't applied it for California. You can very easily remove that filter by clicking on it and removing, and you'll remove that set. So those are ways of filtered data directly live in view. So we looked at filtering discrete data, so we showed that region example. So if you come in here select region, you have a discreet number, discreet number of values. You have four values. You can also filter on the measures or the green items. So here we're looking at sales, so weaken filter on sales to and we can choose the sum of sales. How do we want to filter it? And we can say we only want see sales drayd er than 250,000. So coming here click OK, and now this application has been filter on Lee states with greater than 250,000 sales, the volume of sales and you could see that right here. So that's how you That's the difference between filtering on discrete data, which is the Blue Data, and the continuous data, which is the green data that summarizes Chapter two, the second section, Part two, and we'll review do a quick review and move on to Part three. 8. Part 2 Summary: so we reviewed how to use blends and filters in the last video. Let's summarize Part two in this part recovered foundational concepts of how tableau works for data. Although you will not usually be concerned with what QUERIES tableau generates, it's good to understand the underlying data. Engines have a salad foundation of understanding tableaus paradigm that will greatly aid you in your data analysis. Having a basic understanding of connecting to various data sources, working with extracts, customizing metadata and the difference between joins and blends will be keep as you begin deeper your deeper analysis and more advanced visuals, such as those that really that we will review in the next parts so you're now ready to move to set out on a journey of building advanced visuals advanced does not necessarily mean difficult tableau makes many of these visuals easy to create. Advance also does not necessarily mean complex. The goal is to communicate the data, not obscure it and needless complexity. Instead, these vigils are advanced in the sense that you will need to understand what they should be used when they should be used and why they're useful. Additionally, many of the examples introduced summit advanced techniques such as calculations to extend the usefulness of the financial year visuals. Many of these techniques will be developed more fully and future parts, so don't worry if you don't absorb every detail in this part. Most of the examples in the next part are designed so that you can follow along. However, don't simply just memorize a set of instructions. Take time to understand how the combinations of different field types you place on different shelves change the way headers, axes and mark surrendered, experiment and even deviate from the instructions from time to time just to see what else is possible. You can always use tableaus back button to return to your example, so let's jump into the next section.