Master Tableau: Comprehensive Guide to Data Visualization & Dashboards

hello and welcome to this very unique course to master Tableau my name is bar zini and I'm currently leading Big Data

projects at mercedesbenz with over a decade of experience in Big Data data visualizations and business intelligence

projects and I'm very excited to be your instructor for this course in this 21-hour course I'm going to be sharing

everything that I know about one of the most in demand skill in data science and data visualizations Tableau so that by

the end of the course you're going to be able to create amazing dashboard and visualizations in Tableau like I do in

the real projects and I designed this course to take you from Zero to Hero so if you are a beginner don't worry about

it I'm going to explain everything from the scratch step by step so that means this course assumes that you don't have

any skills in data visualizations and as well all the skills that you going to learn in this Tableau course like data

moding and so on could be used in any other tools like powerbi and click and now of course you might ask yourself

what makes this Tableau course different and unique from all other online courses this is the only course that breaks down

the complex concepts of Tableau into animated visuals because visuals are very powerful to make complex Concepts

easy to understand and to follow and in this Tableau course we're going to present over 250 animated sketch notes

of Tableau Concepts understanding the concepts and how Tableau work can make you a professional and expert in data

visualizations and in Tableau and in this course I'm going to provide you with tons of free materials like for

example I have prepared three different data sources for this course that we going to use in all our tasks and

examples through the course and as well I'm going to provide you with three Tableau sheet sheets one sheet sheet for

all Tableau Concepts another one for all Tableau calculations and we have one more sheet sheet for all the visuals to

help you choosing the right charts so having those three sheet sheets you don't have to memorize everything you

have a quick reference and access to Tableau Concepts and as well you have access to all Tableau files and

dashboards that is created during the course and as well all the sketch notes of each section are available to you to

download so you can use it later as a reference so now let's have a sneak peek about the Tableau course we will start

with the basics what is business intelligence data visualizations what is Tableau and then you're going to learn

the Tableau product Suites and after that we're going to do Deep dive into different Tableau Concepts like the

Tableau architecture Dimensions measures discret and continuous data after that we're going to Deep dive in tblo

calculations and fun functions you're going to learn more than 60 different functions in Tableau to manipulate your

data and after that we're going to go and cover more than 63 different types of charts in Tableau and then at the end

we're going to go and Implement Tableau projects similar to the one that I do in real life projects so now the question

is who is this course for if you are someone that has never built any data visualizations using tools like Tableau

or power pii I will be with you in this course in each step starting from the fundamentals and we're going to end up

having the advanced topics and this course is as well for you if you are already a tableau developer so I would

suggest for you that to take a look to the course curriculum and start at the level that suits you I have covered a

lot of advanced topics and you're going to have a lot of best practices in this course and this course is suitable for

you if you have experience in any other tools like in powerp and you would like to pick up a new skill in Tableau so

let's jump in and get started now we're going to have a quick overview of the tblo course I have

splitted this course into 15 different sections for example we're going to learn what is business intelligence what

is data visualizations what is Tableau and the history of Tableau and why Tableau is very powerful tool for data

visualizations after that we're going to go and deep dive into the Tableau product suit we don't have in Tableau

only one product we have eight different products so I'm going to go and introduce you to those products and

we're going to go and compare them side by side for you to understand the differences between them and I'm going

to help you to choose the right products for your project moving on we going go and deep dive into the Tableau

architecture here we're going to learn many different concepts like what is live and extract connections what are

the different types of Tableau files and then we're going to Deep dive into the Tableau architecture in order for you to

understand the main components of the architecture and how Tableau internally Works after all the theory we're going

to start preparing your environment in order for you to practice with me in this course so we will go and download

and install Tableau for free of course at your PC we're going to go and create free Public Accounts we're going to

download the training data sets and we going to publish our first visualization and at the end I'm going to take you in

a tour in order to make you familiar with the Tableau interface and after we have repaired your environment we're

going to start with the first topic how to create a data source in Tableau and here you're going to gain skills about

the data modeling so we're going to go through the basics of data modelling and as well how to do modeling in Tableau

and then we're going to go and learn four different methods on how to combine tables in Tableau using joints

relationships and data blending and of course we're going to go and compare them side by side for you in order to

understand the differences between them and when to use which method and at the end of this section we're going to go

and create two data sources moving on we're going to start talking about the Tableau metadata here you going to learn

very important Concepts in Tableau the data types dimensions and measures discrete and continuous values once you

understand those Concepts you can understand how to create visualizations in Tableau after this section we have a

small section about renaming so here we're going to talk about the naming conventions that each developer should

know then we're going to learn the different techniques on how to rename columns and tables in Tableau and at the

end we're going to learn how to give aliases to the values moving on to the next section you're going to learn how

to organize your data in Tableau and here we have different methods like grouping up the dimensions using

hierarchies grouping up the values using groups and clusters and then after that we're going to learn sets in Tableau and

at the end we're going to learn how to create pens in Tableau in order to create histograms and now in the next

section we're going to learn how to filter our data in Tableau and here you're going to learn the different

types and concepts of filters in Tableau how to create them and how to customize them and I'm going to give you 10 tips

and tricks about filters in Tableau and we will learn as well in this section how to sort our data after that we're

going to learn very important Concept in Tableau which is the Tableau parameters Tableau parameters are great in order to

add Dynamic to your visualizations so you're going to learn the concepts of parameters and then you're going to

learn different use cases for that how to make Dynamic calculations Dynamic reference line filters how to swap

measures and dimensions and to make as well Dynamic pens moving on to the next section we're going to learn as well

something about Dynamic so we're going to learn the Tableau actions in order to make your dashboards interactive so as

usual first you're going to understand the concepts of Tableau actions and then we're going to go through all Tableau

actions types for example how to go to URL how to go to Sheets how to filter data using actions and then how to make

highlights using actions and how to change the values of sets and parameters and after this section we're going to

have the Tableau calculations this section is very huge you're going to learn how to transform and manipulate

your data using four different Tableau calculations types so we have the RO level calculations aggregate calculation

table calculation and the LOD Expressions so in this section you you can learn more than 60 different Tableau

functions in order to manipulate your data moving on to the next section we have another big one we have the Tableau

charts here we're going to go and build together more than 63 different charts in Tableau so we will start with the

basic charts like the bar charts and we're going to end up building very Advanced charts in Tableau and at the

end I'm going to help you to choose the right charts for your requirements moving on to the next one we're going to

learn the Tableau dashboards we're going to go step by step on how to create clean dashboards in Tableau using

containers and now in the last section we have a tableau projects here in this section we're going to go together and

Implement a projects exactly like I do it in my real life projects so first we're going to learn the different faces

of each Tableau project then we're going to start with the requirements so you're going to learn how I analyze the

requirements of Tableau and then we start with the implementations of the projects so we're going to go and build

the data sources the charts and two different dashboards so with that you're going to get familiar on how to

implement projects and companies using Tableau so once you go through all those sections you're going to have a solid

knowledge about Tableau if you are new to the world of data you must start hearing a lot of

buzzwords from Big Data to iot data science data engineering and phrase it like data is the new oil in this

tutorial I will be covering some important buzzwords about the data and what they really mean so let's Dive In

we are living now in the data driven age and data is generated everywhere we people we generate massive amount of

data as we speak each click on the internet each search email or even if you are ordering something online we

generate data we spend hours every day on the social media liking commenting searching our smartphone is just all

time uploading data about where you are how fast you are moving and everything we do online is now stored and tracked

as data not only our smartphones and computers are connected to the internet and generates data but also we have

something called smart home we can connect any device at our home to the internet just put the word smart before

it we have Smart M smart lightning smart Fitness voice devices security systems all those devices could be connected to

the internet and start generating massive amount of data and this is what we call Internet of Things iot iot is

the concept of of connecting any device anything to the internet in order to generate and exchange data not only we

have iot at our home but also everywhere we are living in the digital transformation in the industry and

Manufacturing you might heard of the concept industry 4.0 the first Industrial Revolution introduced in

Germany it's all about smart factories connecting machines and devices to the internet in order to exchange data and

now we can find iots in the cities we are trying to implement those smart cities where we're going to connect

everything in order to reduce waste saving money improving quality we have as well iots in our cars our cars are

loaded with sensors and devices that are connected to exchange data for many reasons like driver assistance object

recognitions self-driving systems the list is just so long in 2022 we have around 14 billions of physical devices

things from small household cooking devices to the sophisticated industrial machines that are connected to the

internet generating and exchanging data the amount of generated data everyday is from iot social media

websites machines is truly mind-blowing there are currently over 44 zettabytes of data in the entire Digital Universe

that is 21 zeros so that means we are no longer dealing with normal traditional data we are dealing now with the big

data so what Big Data means there is three indicators that help us to understand whether our data is big and

are defined by the three V's the first V is volume well big data is Big with the growth of the internet mobile devices

social media iots the amount of generated data from those sources has grown dramatically the second V is

velocity in normal data processing we use to process slow data or we call it patch data once a day or something and

then we store it in the disk but in Big Data WS the sources are generating streams of data with very high speeds

that means we have to process and analyze the data in in realtime fashion and then we store it in memory instead

of disk and the third V is Variety in traditional systems most data types could be captured and Raw on structured

tables like database or excels but in the Big Data WS data often comes in semi-structured format for example

server logs in XML or websites or the data comes in unstructured format like videos audios images free text so in Big

Data we have not only to deal with structured data but also with semi-structured and unstructured data so

the Big Data terms means how we can efficiently store process and analyze our data when it has huge volume high

speed and different types in order to reveal significant values for the business but we still have a problem

with that all those generated data are raow data raow data are just unprocessed rows and rows of numbers that are really

hard to understand hard to read badly structured and almost has no value to the business almost 70% of the W's data

are unused raw data if left without processing and refining is just worthless waste of money waste of space

and it generate digital waste stores in very expensive data centers and that's why we have the very famous phrase of

the famous British mathematician Clive Hy data is the new oil well it means that we have to extract the row data

like we are extracting oil we have to refine it process it transform it into something useful and has value to the

business well what this really means is that most of the companies are sitting on very big field of a new oil raw data

and most of them understood that data is their most valuable asset they have to extract it they have to analyze it in

order to reveal Insight that could help them in order to make faster and better decisions and that's why most of the

companies are hiring Army of data workers as we know the demand for data scientist is increasing rapidly and the

supply is low so now what we can do with all those chaos all those generated unprocessed raw data well we can do the

following stuff so what we can do we can design or build a data architecture data

architecture is the process of creating blueprint on how we organize process and store our data into different layers for

different purposes so that architecture make it easier to manage protect and access our

data another thing that we can do with the row data is data engineering data engineering is very complex process of

Designing and building data pipelines and data storages in data engineerings we usually build ETL processes to

extract the row data from multiple sources then transform it and then load it to the Target storage in order to

make it highly available and usable for the data scientist or any other end user another thing that we can do is

data modeling so data modeling is the process of connecting the dots so what we're going to do is we're going to put

all the data into entities and objects then we describe the relationship between those entities in order to help

us and help the programs to understand how the data are related to each other another thing that we can do with

the row data is we can do data mining data mining is the process of analyzing massive amount of row data in order to

discover knowledge to discover business intelligence like patterns and Trends to solve problems and to mitigate

risks another use of the row data is that we can use it in machine learning in machine learning we are providing the

computers with two things first the RW and historical data together with the mathematical models and algorithms so

once the computer has those two things it's going to start training and practicing in order to perform tasks

like predictions so it's like human the more the machine practice and train the better and accurate the results going to

be and next we can do data science data science is the scientific study of data and it combines three major Powers the

power of programming languages together with the mathematics and statistics and the knowledge of specific domain in

order to uncover valuable knowledge and insights from our row data one more thing that we can use on

the row data and my favorite one is that we can use data visualizations so data visualizations is the process of

converting numbers and raw data which is normally hard to understand and to read into visuals and charts like Bars by

tree plots in order to make it easier to understand and easier to read which really helps in the decision making

there are many other things and processes that we can apply on the row data but these are the major fields of

work that we can use in order to convert the useless row data into knowledge that has significant impact of value to the

business [Music] all right let me tell you this story we

have shops in three different cities in Germany in stutgart we have shop Berlin and Hamburg and our three shops are

generating every business day a lot of raw data on Sales Inventory levels products staff cost and so on and now we

have group of people that are the decision makers like managers HR finance and they have many questions and

decisions to make so they might have questions for example what happened and another questions about what will happen

now if the managers try to find the answers from the row data they might find nothing and no answers because the

row data usually very complex and badly structures and they are really hard to understand and that's why they're going

to go and hire some data analyst for example in order to help them finding the answers from the row data so the

data analyst going to go and start analyzing the row data by doing some magic for example cleaning up the data

connecting objects together and aggregating the data in different levels and at the ends the result will be

communicated as for example spreadsheet to the decision makers and in the other hands the managers can hire data

scientist in order to help them finding answers about what going to happen or uncover unknown facts and insides so the

data science can as well go and start analyzing the raw data but this time using different methods like for example

data mining machine learning or train model in order to find new insights new knowledge and answers the question

question S at the end the output going to be communicated as well to the managers as numbers and spreadsheets now

both of the data scientist and the data analyst did amazing job working on the raw data and analyzing those stuff but

the problem here is that the output might be hard to understand and read Because those managers are usually

people that don't work directly with the data every day so this could lead to a big gap between those managers and the

results and now in order to bridge this Gap and make everything easier we can use the power of data visualizations and

the result presented from the data scientist and the data analyst should be converted from this boring numbers and

spreadsheets to visuals graphs and charts the visual representations of the data will just do the Magic by making

everything clear and easy and it's going to bring very easily the wow effect once you are presenting your results so it's

going to help the managers to immediately find their answers and they going to start making decisions using

the data this process we call it a business intelligence or as a shortcut bi so now the question is why

visualizations is so powerful with the simple Visual Communications you can make a huge difference since the start

of the humanity thousands years ago an early human use visuals in order to tell a story and until now in the modern age

the human still uses visuals in order to tell any story because we humans we are visual creatures we think in pictures

and in visuals if we see a tree our brain going to store it as a visual as an image in our brain studies say that

90% of the information transmitted to our brain is visual but if we read the word tree our brain has fair to

transform it to a visual before storing it which is way slower in fact the human brain processes visuals 60,000 times

faster than it takes more fact about our brain is that we remember most of what we see and interact with it's proven

then the human remember only 10% of things we hear and 20% about what we read and it's also proven that we

remember about 80% of what we see and interact with that's why we have the famous phrases of a picture is worth a

thousands words and seeing is believing having all those facts no wonder that in digital channels the visual content is

taking taking over posts tweets articles news presentations dashboards you can find visuals

everywhere so now the question is what is data visualizations or sometimes we call it data Vis data visualizations is

the process of converting boring numbers and raw data into interesting graphical elements like Bars by three blots and so

on so data visualizations brings the data to life makes you the master of Storytelling of the insights hidden

within your numbers so it's like an art of converting highly complex massive amount of data sets into something very

simple something very easy to understand and to interact with imagine yourself to be one of the managers and you have two

data analysts one of them is presenting the result in spreadsheet filled with numbers and the other data analyst is

presenting the result with visuals filled with graphical representations of the data and both are presenting the

same facts which report you will prefer I would go with the right one because the left one is just dry numbers pouring

and unlikely you will be able to spot any Trends and patterns so the main benefit of data

visualizations is telling a story which arms you with tools in order to make the right decision at the right time there

are many other benefits like seeing the big picture tracking Trends making smarter and faster decisions discovering

unknown facts acts patterns Trends and getting as well more engagement from the end users by asking more and better

questions all right so with that we have learned what is data visualizations and why it is very powerful and important

and next we will compare Excel to bi tools like Tableau and why you need to use Tableau instead of

excel over and over again I'm asked the same question why I should bother learning and using Tableau or power bi

for data visualizations if we have excel in this video I'm going to explain for you my six reasons why we should use a

more than bi tool like Tableau and Barbi and not use Excel for data visualizations and we start right now

there is around 1 billion users globally are using Microsoft Excel I worked in many companies and I can tell you people

are just addicted to excel they love it they use it for everything as planning tool data entry data analyzis and data

visualizations but the main problem here that the more a company grows the more it generates data and because everyone

is familiar with excels they going to keep using them in big data use cases and they're going to face really hard

time managing those spreadsheets and dealing with the limitations in Excel in these situations it's really time to

switch to a modern bi tool or data visualizations tool like Tableau or Barbi now let me show you how bi is done

with Excel we usually have different Source systems and data analyst that's going to go and start exporting manually

the data from those systems and import them in Excel and then some calculations going to be done and at the end a report

will be generated the Excel files then will be accessed from different business users in the other hand we can do bi

with a modern tool like Tableau so what we're going to do we're going to connect Tableau directly to those Source systems

and the data analyst can start developing a Rebo or dashboards in Tableau and at the end the business

users will access Tableau in order to see those dashboards so far you can say okay both look really similar so now

let's dive in in order to show you what is the real benefit of having a modern bi tool like tblo or Barbi and the

limitations that we have in spreadsheets like Excel the first benefit is automation if

you are using Excel and we made some nice reports it's time now to update the data and how we do that in Excel we

update data manually so some employee have to sit down every day and go through the process of extracting data

from those Source systems importing them in Excel do calculations and at the end prepare the reports over and over again

which is very time consuming but if you are working with the modern bi2 like Tableau we can automate this boring task

by creating schedule to refresh the data for example we can create a schedule in Tableau every day at 7:00 morning

Tableau should automatically connect to the data sources pulse the data and prepare the reports there is two

benefits of doing that first we eliminate the human errors which is very common thing in Excel and sometimes

those mistakes can lead to wrong decisions and to finance loss and the second benefit of course we no longer

needs employees that is dedicated only for this boring task of exporting and importing data manually to

excel another benefit here is the capacity if you are working with Excel and one of our source systems start

producing and generating massive amount of data here we have problem in Excel because we can handle around only 1

million records so our Excel file going to breaks and we're going to start getting error messages likes the data

set is too large so what we usually do in Excel we're going to go and start splitting the main file into small

multiple files in order to manage the huge volumes of data which is really hard to manage in the other hand if you

are working with Tableau we don't have to worry about all those stuff we have no problem in Tableau because Tableau is

made for big data use cases and can very easily handle massive amount of data so we might just change the connection type

from extract to live in order to handle it another benefit is security if you are working with Excel it's really hard

to hack into Excel even if you are using password protected spreadsheets it still can easily hacked nowadays and the users

are really used to share their excels in emails copy it USB or store it locally at their computers which is not secure

at all so all those stuffs could cost the companies a lot if sensitive and confidential data is accessed by

competitors but if you are working with modern bi2 like Tableau it going to provide us with Superior security

features like Advanced Access Control Data security network security and plus if you are working with Tableau we don't

have to export the data we can just share the dashboards and reports between employees and only if we grant them

access rights they can see the data another benefit is their role level security in many companies they have a

lot of confidential sources and they start to understand how important is to apply the principal need to know the

principles needs to know says a user shall only have access to the informations that's their job functions

requires that means we cannot go and share all data to all users we have to have some data restrictions for for

examples a sales employee should not see all data like manager and finance employee should not see all personal

informations like HR and so on that means if you are working with excels we have here again to split the main files

into specific reports for specific rule but in the other hands most of the modern bi tools they offer a feature

called Rowl security RLS row level security refers to restricting the roles of data as certain users can see based

on the policies that we Define using this technique going to enforce the need to know principle and going to make our

life easier by just having one dashboard accessed by different types of users and then based on their rule they going to

see the data and the informations that their job requires another benefit is reducing

chaos let me tell you how we usually work with Excel a data science will start exporting data from one source

system and he going to make a report called version one report and then for other requirements he going to make a

version two reports and eventually we're going to have a final reports and we have another data analyst working in

different Source system and the same thing going to keep happening few times back and forth and eventually we're

going to end up having different six versions of the reports and if we scale this impact you will notice that you are

slowly poisoning your business and the end user is going to have to access different versions of the reports and

now if we ask how old is the data in our reports we will get different answers when one version going to be 10 days ago

another one eight 4 and 3 days that's mean we don't have single point of Truth for our data and that's why having

modern bi tools going to help us to eliminate such a chaos and going to help us building a single point of Truth for

our data one last benefit that I would like to talk about is visuals although excels

offers visualizations but it is sometimes very limited when we are producing complex visuals in Excel is as

well creating visualiz Iz ation is very time consuming including a lot of manual steps and as well those visuals going to

be static and not interactive but in the other hand if we are using Tableau everything going to be automated and

super fast we can create new reports and Views very quickly by just drag and drop and they offer way more interactive and

cooler visuals than Excel all right the main reasons why I prefer working with Mod bi tools like

tblo and powerbi and not for data analyzes and data visualizations are automations security big data use cases

and interactive visuals it's not about Excel versus Tableau it's all about using the right tool for The Right Use

cases and not to misuse a tool Excel is a great tool that is used by billions of people because it's very easy to use

sheep professional spreadsheet for data entry and complex calculations but when it comes to data analyzis and data

visualizations we have way better tool than Excel like powerbi and Tableau and you can still use them together for

example you can do your complex calculations in Excel and the final result going to be imported in Tableau

in order to do better visualizations and to get more insight about the results the thing is the world is changing very

fast and the companies are generating massive amount of data so instead of using traditional spreadsheets like

Excel we have to use more powerful Tools in business intelligence to help us quickly find insights Trends patterns in

order to make faster and better decision so now the question is what are the best tools for data visualizations a

leading research company called Gartner publish every year the Gartner magic quadrants to show who are the leading

product in specific domain and if you check the magic quad for analytics and business intelligence platforms for the

last 10 years you can almost see always the same leaders we have Tau power pi and click view since 2003 12 and I'm

working with a lot of data visualizations tools and I can say that all those three tools are really great

tools they have their advantages and disadvantages but by just checking the data visualizations aspects I can say

that Tableau is here a winner because data visualizations in Tableau is a core concept and really the best tool for

data scientists and for Big Data all right the first question is what is tableau a quick answer could be Tableau

help us to convert this to this without any technical or programming skills so Tableau converts

complex and boring grow numbers into beautiful visuals and charts which is really easy to understand and the key

features in Tableau is interactivity easy to build and to use and fast performance we can call Tableau with

many names like a data visualization tool a business intelligence or bi tool or sometimes we call it a reporting tool

well Tableau is all of them but I choose to call Tableau a data visualization tool because data visualizations is the

core concept of Tableau now let's have a quick history about Tableau in 2003 tblo was founded

by three guys Pat Christian and Chris as a result of computer science project at Stanford University they focused in

visualizations technique to analyze data inside databases and then in 2019 Tableau was acquired by Salesforce in a

deal worth over 15 billion and for the last 10 years Tableau was named as a leader in Gartner magic cordance for

business intelligence Tableau has a clear mission to help people to see and understand

their data they really focus on keeping Tableau intuitive and easy to use that's why Tableau does not requires any

technical or programming skills in order to build amazing dashboards and insights that means the target audience of

Tableau is not only for technical users like it data analyst data scientists but also for all other non-technical users

like a business user an end user a teacher and so on this aspect is a GameChanger of changing the old mindset

of having only it and Technical people working with data and building visualizations but now we have modern

data visualizations tools like Tableau which opens the door for everybody to start start working with data that's why

tools like Tableau helps organizations to be data driven and now Tableau is widely used you can find Tableau almost

in all organizations Industries sectors in all departments because most of those organizations want to empower their

employees with tools like Tableau in order to make better faster and smarter decisions using data all

right tblo is not the only leader in business intelligence and data visualization Market there are many

other tools that are available like powerp click View and so on but now if you ask me what makes tblo so special

why Tableau is so widely used I would give you four reasons the first reason is performance

the sources now are generating massive amount of data and Tableau is designed and optimized to handle huge volumes of

data without impacting the performance in the dashboards and that's because is using high performance inmemory data

engine to help analyze large data sets where the data going to be stored inside columns instead of rows which can boost

the performance in dashboards tblo has no limitations or whatever to the number of data points in the visualization for

example on this view we have over 1 million data points without any problem this allows us to analyze large data

sets in order to find Trends patterns with a great performance and all other tools still inforce through size data

point limitations which is not really helpful for data analyzes the second reason is quick and

interactive visualizations compared to the other tools with Tableau we can create rich and beautiful visualizations

in just few seconds I'm going to show you now quick example how to Cluster my data and how to calculate the forecast

in order to do such a complex job in Tableau we will just use drag and drop so let's see how simple it is all right

so we're going to go to the order take the sales put it in the columns profit and the rows and take the order

IDs in the details and I want to see all my members over here and now we go to the analytics pan and then double click

on the Clusters so with that I have very nice four clusters of my data The Next Step I will create a forecast of my data

so I'm going to take the order ID put it in the columns and then we're going to take the sales I would like to change

the visual to bars so I have now here around five years what we're going to do we're going to go to analytics and just

click on the forecast and that's it so I have a forecast of two years of my sales and now I'm just going to go and put

them together in one dashboard so I'm going to create a new dashboard drag and drop the Clusters drag and drop the

forecasts and going to link them together with the filter and that's it so now we have both of them and if I

click around I will have an interactive dashboard for the forecast and for the Clusters

the third reason tblo is userfriendly as you can see we have done very complex analysis with just drag and drop without

writing any code and this is exactly what Tableau wants it's very intuitive and userfriendly and this is the major

strings of tblo it just opens the door for all nontechnical users to have a chance to work and play with data to

solve their daily problems without the need of it but in the other hand Tableau is integrated with programming languages

like python and R which opens another door for Advanced Data visualizations which might be used from data

scientists and the last reason is community if you are working with Tau well you are not alone you have a huge

Tableau community in the community we have around 2 million students and teachers and in Tableau public we have

around 5 million data visualizations that are published and there's around 200,000 questions and ideas that are

shared in Tableau forums having such a huge Community is a big Bloss for any tool it's very important because while

you are working with data you might face some problems or you have questions it's very important that you have a place

where you can go and ask your questions and get advices from other developers all over the world and not only that you

can as well get inspired from the shared visualizations from other developers you can find the important links about the

Tableau community in the video description below all right so my four reasons why Tableau

is one of the best tools for data visualizations are Tableau can handle massive amount of data very suitable for

big data use cases it offers beautiful quick interactive visualizations Tableau is intuitive and userfriendly no coding

or technical skills are required and the last reason table Community is very huge one more thing that I would like to add

that data visualizations is really one skill that you have to master as a data scientist or data analyst and Tableau is

an amazing tool for data visualizations that's why I highly recommend to learn or to get familiar with Tableau it's

going to be like a huge Advantage for your career all right guys so with that you know my reasons why I think Tableau

is a leader in data visualization and with that we have finished the first chapter of Tableau where we have covered

a lot of important terms of data and Tableau and in the next chapter we will have an overview of the Tableau product

Suite where I will introduce you to eight different Tableau products Tableau products in Tableau we have

eight different products and it's really important to understand them and understand the differences between them

so that's why I'm going to go and give you a quick overview of all eight table products and then we're going to go and

compare them side by side in order to understand the differences between them and at the end you're going to learn the

decision making process that I usually follow to choose the right product for your requirements so now let's start

with the first topic where we're going to have an overview of the the development process and products so now

let's go all right if you think Tableau is only one software then you are wrong if

you visit the homepage of Tableau tableau.com you will find many different Tableau products like Tableau desktop

public server cloud prep reader I can say other starts it might be confusing having all those Tableau products but

don't worry about it I'm going to explain them one by one so you can chose the right combinations of Tableau

products for you or for your organizations it's really important to understand the differences between them

the functionalities and the limitations of each Tableau products and let's dive in so Tableau product which contains

eight different products we have Tableau desktop Tableau public desktop prep server cloud public Cloud Reader and

Tableau mobile all right the first thing to understand is that we can split those products into two main categories

developer tools and sharing tools Tableau developers tools as the name implies they are tools that's going to

help you to build data visualizations by creating and designing dashboards charts reports or to do data preparations or

data engineering by preparing the data for data analyzis under this category we can find three Tableau products Tableau

desktop public desktop and Tableau prep and now in the other category we have the sharing tools those tools can help

you to share and collaborate your work work that you have done and created using the developer tools under this

category we can find five Tableau products Tableau server Tableau Cloud public Cloud Reader and tblo mobile all

right so now first let's focus on the Tableau products under the category developer tools and now we can go and as

well split the developers tools into two groups based on their purposes we have data visualizations and data engineering

underneath data visualizations we find two Tableau products Tableau desktop and Tableau public desktop and underneath

that engineering we have only one Tableau product and that's Tableau prep all right so now after we understood the

main categories and the main purposes of Tableau products we will go now and talk about the development process in

Tableau all right so basically we have three very simple steps in the development process in Tableau the first

step we connect our data to Tableau then in the next step we start building our data visualizations to do data anal izes

by creating report chart and dashboards and in the third step we share our work by publishing it the two products to do

these three steps are Tableau desktop and Tableau public desktop in many cases the quality of our data is bad and not

ready for analyzes that's why we add one more pre-processing step to prepare our data before we start building our

visuals and we can use for this step the product Tableau prep all right so now let's do deep Dives in into to tblo

developers products one by one in order to understand the key features and as well the limitations for each one of

them tblo disktop is a software you download and install at your PC with Tableau desktop you can connect to many

different Source types there are over 90 data connectors you can connect to Tableau server or to connect to files

like Excel text Json or to on Prem servers like my L and Oracle or to cloud like Amazon Google and Microsoft Azure

once you connect Tableau to your data you can start building your data visualizations in Tableau desktop you

will find many tools and functions to help you creating charts reports with just drag and drop and then you can

combine those different reports into interactive dashboards and after you're done building your views and dashboards

then you have three options to share your data by either publishing them to Tableau server Tableau cloud or to

Tableau public cloud or even you can store your workbooks locally at your PC all right so Tableau desktop is the

backbone product of Tableau as tblo developer you're going to spend 90% of your time using this tool tblo desktop

is a developer tool to build data visualizations where you connect your data build dashboards and then publish

them sadly tblo desktop is not a free tool like powerbi desktop in order to work with d desktop you have to buy a

license I think they offer some kind of trial phase or if you are a student you get get like one free year don't take my

words it's better to check the current offering from tblo in their homepage with tblo desktop you can connect over

90 different data sources you can publish as well your work everywhere to tblo server tblo cloud and tblo public

and since tblo discop requires a license you don't have any limitations or whatever on how many roads and data you

can store and process T desktop is meant for data analyst data scientist bi developers who work professionally in

companies in data anal iCal projects all right so that was a quick overview of the Tableau disktop next we will check

the Tableau public disktop so tblo public is the free version of tblo desktop it is very

similar to it it's a developer tool in order to build and publish data visualizations and since it's free and

requires no license it comes with few limitations in Tapo public we have a r 10 data connectors you can connect only

to local files at your PC another limitation of that you can store and process only 15 million rows of your

data and you can publish only to Tableau public Cloud so that means you cannot publish your work in Tableau server or

Tableau private cloud and the last limitation is that you cannot store your workbooks at your local PC but here I

have to be fair is that the most important part is that all functions and tools in order to build visuals and

dashboards are completely available in Tableau public like in Tableau desktop which makes really Tableau public as a

great alternative and tool for beginners in order to practice and to learn Tableau before they go and buy licenses

and to be honest that's why I decided to go with Tableau public in all my tutorials so that anyone can follow and

practice with me without having you buying any licenses tblo prep Builder is a software

you download and install at your PC and you can use it to prepare your data before you start analyzing it same as

tblo desktop you can connect to many different Source types there are over 90 data connectors like Tableau server

files on Prem cloud and so on once you connect Tableau to your data you can start building data flows where you have

access to tools and functions to help you to transform your data for example combining data cleaning filtering

aggregating and all other art of data engineering tasks to prepare your data for data visualizations and at the end

of your data flow you can store the new prepared data in three different places either as a file at your local PC or

publish it as a data source in Tableau server or cloud and the last option you can write the output directly in

databases and after you are done building the data flows then you can publish them in Tableau server or

Tableau online for automations and in Tableau prep you have the option to store your data flows locally at your PC

all right so TBB is a data engineering tool to prepare our data to get ready for analyzes sometimes the data that we

are connecting to Tableau desktop has bad quality and we cannot use it immediately in our dashboard that's why

we spend like hours and hours of cleaning up organizing combining preparing our data and that could be

really time consuming so for this situation we could use tblb to help us with this process so tblb is a developer

tool for data engineering where we connect to our data build data flows and then publish them and it's not free tool

it requires a license in t r we have over 90 different data connectors the output of the data flows could be stored

locally at your PC or as a Tableau Data Source or directly in the databases and we can publish the data flow either to

Tableau server or to Tableau Cloud tblo prep is not like tblo desktop we don't have any free version of Tableau prep so

there is no Tableau public prep all right so now let's go and have a summary of the three products where we

going to compare them side by side the main purpose of tblo desktop and public is to generate data visualizations but

the main task of tblo prep is for data engineering now if you are talking about the costs both desktop and prep requires

licenses but Tapo public is free to use and now about the security aspect of the data tblo desktop and prep are secure

since you can publish them to private servers but tblo public you have to publish your work to public platforms

where everyone can see your data so you cannot secure your data in Tableau public and the next Point data limits

since public is free it comes with the limitations of 15 Millions row but desktop and prep you will get no

limitations the next point is connectors in both desktop and prep you have over 90 different data connectors like files

API servers cloud and so on where in tblo public you can connect only to files and if we talk about the live

connections aspect the only tool offers live connections to your data sources is Tableau desktop you cannot make Live

Connections in Tableau public and in Tableau prep you have always to work with extracted data the next point is

about storing your files locally both tblo desktop and Bre allows you to do that by storing your work locally at

your PC but in Tableau public you cannot do that instead you have always to publish your work to tblo public Cloud

the last aspect is about the target audience tblo desktop is made for data scientist and data analysts but tblo

public is made for anybody wants to work with data visualizations and tblo prep is made for data

Engineers all right so now with this we have good overview of the three Tableau products for developments and now comes

at the question when to use which product so now let me guide you in my decision making process using the

following flu charts first we ask the question for which purpose if we need a product for data engineering then it's

easy we have only one Tableau product and that is Tableau prep now if we need a product for data visualizations then

we can ask more questions the next question do we need to connect to server API databases or to Cloud if the answer

is yes then we have to use Tableau desktop and if the answer is no then we ask the next question can our data be

public if the answer is no our data is confidential then we have to use Tableau desktop but if the answer is yes our

data can be public then we jump to the next question do our data sources contain more than 15 million rows if yes

then we have to choose Tableau disktop but if the answer is no our data sources have less than 50 million rows then we

jump to the last question do we need to have live connections to our data sources if the answer is yes then we

have again to choose tblo desktop but if the answer is no then finally we can go and use tblo public all right so if you

follow those questions and this chart you can easily decide when to use which Tableau

product all right guys so in the previous tutorial we splitted Tableau products into two main categories

developers tools and sharing tools now we're going to focus on the second category the sharing tools where we have

Tableau server cloud public Cloud Reader and tblo mobile and as the name implies those products can help us to share our

reports and dashboards with others and in the last last tutorial we have talked about the four steps of Tableau

development process now we're going to do Deep dive in the step number four where we're going to talk about the

different options that we have in order to share our reports and dashboards with others if you want to share your visuals

with your colleagues in your organization then we have here few options first you can install Tableau

server product on servers using the infrastructure of your organization and then you can start publishing and

sharing your dashboard there then your colleagues can either use their web browser or they can use tblo mobile app

on their smartphone or tablets to view and interact with your dashboards directly from the server the second

option we have we can install Tableau server products on cloud service providers like Amazon AWS Microsoft

Azure or Google cloud and then you can publish your dashboard there and the same thing here users can use web

browsers or Tableau Mobile in order to access your work the third option we have you can use Tableau private cloud

service here you don't have to install any Tableau server or anything you will get everything prepared from tblo Team

you can start immediately publishing your dashboard there and your users can consume it from tblo cloud and now let's

say you want to share your dashboards with everyone in the world and make it public then you can use tblo public

Cloud you don't have to install anything you can immediately publish your dashboard there and users all around the

world word can use their web browser to access your dashboards and data but they cannot use mobile app in order to access

Tableau public and now to the last option that I really don't like to use if you want to share your reports to

individual users you can send them a tableau file with the format twbx Tableau packaged workbook which

contains your data plus your reports and dashboards and then the users can view this file using Tableau reader software

installed at their p [Music] all right everyone so now in order to

understand the real differences between tblo server and tblo Cloud we have to understand the backend details and some

basic concepts about hosting servers let's go let's say we are startup company and we want to host our own

table application and build the entire infrastructure for that reason there is a long list of tasks that should be done

of course the first thing that you need to do is to go and buy some Hardwares and configure them like servers that

will run the applications and each servers need as well storage so we have to provide additionally storage

infrastructure like some hard disk driver and ssds servers needs to be as well connected to the internet therefore

we have to provide as well all the networking infrastructure once we have all those stuffs then we have all

Hardwares needed the next thing that we need to do is that we going to go and start installing and configuring some

softwares like we can install an operating system for example windows or Linux and many other middlewares once

the operating system is in place then we have to install and configure tblo server application once we have all

software and Hardware ready and running it's finally now the time to set up our Tableau project and we have to manage

the following tasks we have to start adding users to the Tableau server and map them to the correct licenses we have

as well to create schedules and tasks to refresh our data inside tableau server and then we have to start monitoring the

Tableau jobs all right so now we come to the big question that we have to answer who will manage what the first option

you have if you decide to manage all these layers that means we are talking about the on premises model so it's

clear ownership you manage everything from top to bottom Hardware the software and the project itself but now if you

say you know what this is too much to manage we don't have the money to buy all those stuff and hard Hardwares at

the start and we don't have the time to take care of them and maintain them then you will start thinking about

Outsourcing the Hardwares where you're going to buy a service from cloud providers like Microsoft Azure Amazon

AWS or Google Cloud so that they manage the hardware and you manage both software and projects and this is what

we call infrastructure as a service I the first letter of each word but now if you say you know what our it team is

very small we don't even have the time to keep those softwares updated each time Tableau makes a new release we have

to install a new version of Tableau server which is really wasting our time and we are not able to focus on our Core

Business project we don't have the resources to manage our own software then you start thinking about

Outsourcing the software layer to do that you can buy a service from Tableau it's called Tableau clouds where tblo

team going to manage everything for you both Hardwares and software and this is what we call software as a service

SAS okay guys so now let's summarize and compare the three hosting options the first point is about hosting setup on

premises you need Tableau server installed in your organization servers in as you need as well tblo server

installed in cloud service provider for example Microsoft Azure and in SAS you just buy tblo Cloud product and now for

the question who man what in on premises you manage everything the hardware software and your project and there is

no Outsourcing in is you manage both software and your project and the cloud service provider going to manage only

the hardware in SAS you manage only your business projects and Tablo going to manage both hardware and software so now

let's check the advantages and disadvantages of each service model for the on premises the good thing here is

that you have full control of everything the hard hardware and software and your data remains behind your firewalls this

is very important if you have critical or sensitive informations that should not stor outside of the company's

firewall but the drawbacks here you need a dedicated hardware and software administrators to deal with the

maintenance patching and many other tasks it is very costly at the start of the project you have to pay a lot for

the Hardwares and the softwares and it's not flexible it's really hard to scale up or skill down your Hardwares as

needed having all those stuff generally you have less time for your business projects all right so now let's move to

the is the First Advantage it gives you flexibility you can scale up scale down the Hardwares as the business needs and

there is no upfront cost for buying Hardwares but the downside of is is that you still need administrators to manage

your softwares to do installations patchings of your softwares and if you don't pay attentions for the cost you

might end up paying big bills now let's move to SAS the main advantage in SAS is that it allows your it team to focus

only on the Core Business projects and allows you to implement projects in very short time and the other good thing is

that your software will be always up to date Tableau team going to deal with that but the downside of SAS is loss of

control you will be at the mercy of Tableau team if anything bad happen like security problems all your

organization's data might be compromised and the other disadvantage is that you might have bad performance or networking

issues connecting Tableau to your Source systems and my advice here that you should avoid Reinventing the wheel

always take advantage of services that do things not part of your core business every hour you spend patching an OS or

installing updates for your software or replacing Hardwares is an hour not spent enhancing and refining your dashboards

in tableau [Music] all right everyone so now we're going to

do deep dives into Tableau sharing products one by one in order to understand their key features and as

well their limitations for each one of them and we start with tblo server and Tableau Cloud as Tableau developers in

organizations we need to share our reports and dashboards with the other colleagues in our organization so we

need to put those dashboards in a trusted environment or platform in our organizations and we usually have four

requirements the first requirement it should be safe and secure we want to control who is accessing our data and

dashboard second it should be easy to scale third it should be robust that can handles huge amount of users and data

and the last requirement it should be powerful and deliver high performance no one wants slow dashboards and reports

and now in order to build this trusted environment with these requirements we have two Tableau products Tableau server

and Tableau cloud and we have three hosting options on premises ASAS and SAS don't worry about the terms I'm going to

explain them Tableau server and Cloud they are very similar at the user interface level you will not notice any

differences but if you are checking the backend level there is a big differences between them so now first let's talk

about the user interface level of Tableau server and Tableau Cloud once you publish your dashboard to Tableau

server or Cloud you can share them by Prov find ing links to the users across all departments in your organization and

then the users they can access your dashboard using their web browser without installing any software at their

end and if you give them access they can start exploring your data in Tableau server or Cloud you can manage your

users by adding and removing them give them specific rules like admin creators viewers or Explorer you can manage your

users as well by adding them to groups another important task you can do in Tableau server or cloud is that so you

can automate your tasks for example you can create a refresh schedule to refresh your data sources on regular basis like

once a day in TBL server and Cloud you can monitor the tasks and schedules to check the status if the job failed or

succeeded and you can find many other statistics about the runtime the average queue and error messages and so on not

only the users can view the dashboards in Tableau server or Cloud but also they can create a new one if you give the

users enough rights they can even start creating their own insights and Views directly on their web browser without

having them to install any tblo desktop it's something we call self-service Pi all right everybody so now with this

we have clear picture about tblo server and Tableau Cloud so now let's talk about the other sharing Tableau products

Tableau public cloud is a free cloud service managed by Tableau team everyone in the world can share visualizations in

this platform so if you publish your dashboards in Tableau public everyone can access it interact with it and even

download it Tableau public is like social media you can edit your profile and add your personal informations in

Tableau public you have a huge gallery of visit built by people all around the world it hosts currently over 5 million

visualizations in Tableau public if you are browsing and you found some interesting dashboard like this amazing

dashboard from AAS you can add it to your favorites and then you can check what other visit did I just created and

published to public and like any other social media if you like her content you can go and follow her to see her new

updates and if you're inspired of one of her dashboards you can go and install the whole workbook to see how she did

build these amazing dashboards and see all details with that you are expanding the knowledge in Tableau development so

using Tableau public you can get inspired from others and you can get connected to other Tableau Developers

from all around the world and one more cool thing about Tableau public if you are searching for new job and you want

to flex your data visualization skills you can publish a lot of work in Tableau public and Link it in your CV so that

the companies can see how skilled are you in Tao so all these nice features makes Tableau public Cloud a very

attractive platform for sharing visualizations but now if you are talking about the security aspect it is

very limited the only thing that you can control is not allowed to download your visualizations or you can completely

hide it from others but you don't have any user access control like we have in tblo server or Cloud so Tableau public

cloud is a free cloud service from Tableau which host a lot of reports and dashboards built by people all around

the world it's a great platform to get inspired by Tableau Community build connections to other Tableau developers

and share your skills but since it's free it comes with few limitations the total size available for each account is

only GB your dashboard and reports are not connected to the source systems that means you cannot automatically refresh

your data in tblo public always you have to do it manually so you're going to open the reports refresh the data and

again publish it to tblo cloud and the third limitation of Tableau public is that as the name implies everyone in the

world can see and share your data that means you cannot use it in organizations since you cannot protect your data

[Music] Tableau reader is a software you download and install at your BC you can

use it only to view reports and dashboards but you cannot use tblo reader to create any data visualizations

or even edit it as you can see we don't have any tools or functions to create charts you can't even connect any data

sources or refresh your data Tableau reader is very old tool from Tableau it was created in the early days of Tableau

in order to share content build using Tableau desktop this was before even Tableau server and Tableau Cloud made

available at that time Tableau reader was the only option you have in order to share dashboard and Report with other

users so how it works you build data visualizations using Tableau desktop and then you send a file to someone else

then they going to use Tableau reader in order to view and interact with the dashboard that you built so to summarize

Tableau reader is a free tool it is just to view and interact with report and dashboard bu using Tableau desktop you

cannot create or edit anything in Tableau reader you cannot refresh the data inside your dashboard using table

reader each time you have to ask for a new copy if you want to have fresh data and there is no security features

password protections or login option this is big problem if the files lands on the wrong hand your organization data

could be exposed well I don't recommend at all using this tool in organizations the risk is just too big but if you want

to take take the risk and to share your visuals with one two three persons then use it but try to avoid

it tblo mobile is a free mobile app that you can download at your smartphone or your tablet you can use it to view and

interact with Tableau reports and dashboards published to Tableau server and clouds so you can use it only to

view the reports you cannot use it to create new reports or to edit the reports while tblo mobile is free to

download it requires a license to use and it can only access Tableau server and Tableau Cloud so you cannot use it

in order to access Tableau public and tblo Mobile going to automatically cash your reports and dashboards in memory

that means you can access them even if you are offline all right everybody so now let's

summarize and compare all Tableau sharing products side by side the first point about hosting Tableau server can

be hosted in your organizations or in cloud service providers like Azure or Amazon both tblo cloud and tblo public

Cloud are hosted by tblo team Tableau reader will just be software installed at your PC you can't even host it now if

you are talking about the cost for Tableau server you have to pay for licenses Hardwares and maintenance but

in Tableau Cloud you have only to pay for the licenses Tableau public and Tableau reader are free to use now if

you check the data security aspects both tblo server and tblo Cloud are highly secure Tableau public and reader they

are not next point is about the storage limitations in tblo server it really depends on the server dis space in tblo

cloud and reader there is no limitations but in tblo public Cloud the total size available for each account is only 10 GB

the next point about the connectors Tableau server and Cloud can be connected to different types of sources

like Cloud API Services files databases and so on but tblo public cloud and Tableau readers they cannot be connected

directly to any of your Source systems let's jump to the next Point Automation in Tableau server and Cloud you can

schedule tasks to refresh your data inside your dashboards automatically from The Source systems but the data

inside tblo public cloud and reader cannot be refreshed you have to do it manually you have to republish it or to

resend the file the next point about tblo mobile you can connect your smartphones or tablets only to Tableau

server or Tableau cloud and now to the last point we can use Tableau server and Cloud to share dashboards inside

organizations tblo public is used to share dashboards to the whole word and TBL reader is used to share dashboards

directly to individuals all right so now with this we have an overview of all Tableau

sharing products so now the question is when to use which product so let me guide you in my decision making process

following this chart all right first we ask all questions about the limitations inside tblo public Cloud the first

question can data be public if the answer is yes then we ask the next question should the data be frequently

refreshed in the reports and dashboards if the answer is no then you can go and use tblo public clouds but if the data

should not be public and should be refreshed automatically then we have to think about private hosting for the

question now do you want to manage the hardware if yes then you can use Tableau server on on premises as your

organization but if you don't want to do that and you want to Outsource it then you ask the next question do you want to

manage the software on your own but if the answer is yes then you can use again tblo server but this time it's going to

be hosted in cloud service provider like Microsoft Azure in is service model but if the answer is no you don't want to

manage the software by yourself and you want to Outsource it then you can go and use tblo cloud as a SAS Service as you

can see TBL reader is not in my decision making process since I don't recommend it at all so now if you combine this

flowchart with the one that we built previously for developers tools you will get my whole decision making process

that I usually use when I start a new Tableau projects so if somebody asked you when to use which tblo product you

can go through it and find the right combinations for you or for your company all those materials you can find it in

my website all right everyone so with that we have covered all eight Tableau products and we understood the

differences between between them in the next chapter we will learn the Tableau architecture to understand how Tableau

internally works and what are the main components of Tableau Tableau architecture now we're

going to go and understand how Tableau internally Works its components and its limitations so now we're going to go and

cover many important Tableau Concepts like what is live and extract connections what are the different file

types in Tableau and then we going to start drawing the Tableau desktop architecture and then we're going to

jump to Tableau server in order to understand different scenarios like the publish process authentication process

and accessing view process and after that we're going to go and complete the big picture by drawing the server

architecture and its components and at the end we're going to cover as well the architecture of the Tableau public so

now let's start with the first concept the live and extract data connections so now let's

go how we come to the most important decision or questions that's we're going to make make inside data source do you

want to store an extra copy of your data inside Tableau so here we have two designs for the data source either

you're going to say no we don't need to copy inside Tableau the data should stay where it is in the source systems then

what going to happens each time your visualizations needs data it going to sends quaries directly to the external

database and then the database going to send the results back to your visualizations so the data comes always

fresh from the s sources directly to your dashboards this type of the connections we call it a live connection

or you're going to say yes let's have a copy of our data inside Tableau so a snapshot or subset of the data going to

be copied from the external database to Tableau this copy we call it an extract and now each time our visualizations

needs data it going to send queries this time to the extract instead of the external database and then the extract

going to return the results back to your visualizations and since the extract is inside Tableau and very close to the

visualizations we will get great respond time and very fast performance this type of connection we call it an extract

connection all right so now the question is which connection type should I use in my data sources the typical answer for

this question is well it depends because here we have a tradeoff between performance and data freshness for

example if for you the performance is way more important than the data freshness then you have to go with the

extract since the dataa going to be stored inside Tableau in memory using the column store technique you will get

just great performance but if you say you know what the data freshness for me is more important than the performance

then you have to go with the Live Connections in your data sources because you will always get fresh data directly

from the sources in your dashboards all right so now if you want to send Tableau files directly to the

users we have to ask ask the question which type of files we're going to send because in tblo desktop we can generate

not only one file we can generate five different types of files in Tableau so now we're going to have like quick

overview of those types of files to understand them and to know when to use them all right so as we learned the

Tableau workbook contains three things the extract the data source and the visualizations there is a file type for

each combinations depend on your requirements for example if you want to share only your data without anything

else no data source no visualizations then you can send an extract as a hyper format but now if you say you know what

I've done a lot of work in the data source I built a data model I renamed stuff I did aggregations I created a lot

of new columns so I would like to share that with my team with my colleagues and I'm not allowed to share my data with

them so in this situation you say okay I'm going to share the data source with my colleagues and we call it Tableau

Data Source TDS without data or you might be in other situation where you say you know what my colleagues don't

have an access to the source systems we cannot use the live connection and you don't mind sharing your data as well so

now you can send them a package of an extract and a data source so the file type here called tblo package data

source DDS X so this type of file contains both of your data and your data source and we might be in another

situation where our colleagues or users they are interested as well in the visualizations so we can send them a

file with the visualizations and the data source and here again we have the same situation you decide whether you

going to send with it a data or not so if you don't want to send the data inside it you can send a file called

Tableau workbook TW WB and the last scenario I think you already guessed it if you want to send everything the whole

package the extract the data source and your visualizations then you can go and send your colleagues a tableau format

code tblo packaged workbook twbx all right so as you can see Tableau

did design different types of files for different purposes so depend on the situation or the scenario that you have

you can share your work with your colleagues all right so now generally speaking we have two different types of

workbooks a workbook with data using extract connection and another book without data using live Connection in

one hand in the workbook with data you can send three different types of files you can send only the data using hyper

format or send the whole data set with the data using TDS X format or send the whole package with the format TW wbx and

in the other hand with the workbook without data you can send only two files the data set without data TDS or the

workbook wbx and now you might have the question and you say okay which Tableau products should I use in order to open

these Tableau files well we have three Tableau products tableau Des toop Tableau public and Tableau reader with

the Tableau desktop you can open everything you can open all these different Tableau formats and files but

with the Tableau reader and public you can open only the Tableau packaged workbook TW wpx since Tableau reader and

Tableau public cannot connect directly to the data sources and they cannot use the live

connections all right one more thing to understand about Tableau workbooks is that Tableau uses two different types of

data to store the workbook the first one is the metadata information it will be stored in XML files metadata is data

about your data it describes your data it contains all informations on what have you done in the workbooks anything

you click drag and drop or do while working with dblo desktop will be reflected in some way in the metadata

you can find informations for example like column names data type data model and so one and the second type is the

data itself the actual data if you load data inside Tableau Tableau going to store it in format of hyber file where

the data going to be stored in column store methods in the memory of Tableau it is like special formats for fast data

retrieval all right if you understand the Tableau architectures and how the components are connected to each others

everything going to make sense for you as you are working with t and as well it going to makes you a

better Tableau developer so I will be sketching the concepts in order to make it easier for you to understand so let's

go the TBL architectures contains four different layers The Source layer the disktop layer server layer and the

consumer layer we will start unboxing each layer one by one to understand their components and we're going to work

with this architecture from left to right so we will start by The Source layer and we're going to end up by the

consumer layer all right so now we have the source layer The Source layer is outside of

Tableau and it contains the source of our data so our data could be in databases like MySQL or Oracle or the

data could be in files like Excel and Json or even in the cloud like Amazon AWS or Microsoft Azure or even in epis

so our data could be everywhere all right so now back to the big picture let's jump to the next layer

we're going to unbox the desktop layer the first component in tblo desktop is the data source before you start

building your visualizations you must set up the data source the first thing that we're going to do inside the data

source is to connect Tableau to our data Tableau offers around 90 different data connectors so we can connect Tableau

almost to anything once you build the connection between Tableau and your source of data the access information is

going to be stored inside the data source for example the path of the file location of servers username passwords

or access tokens and so on so all these informations going to be stored inside the data source all right so the two

types of data Connections in data sources are extract and live connections so now we connected to data we decided

which type of the connection the next thing that we have to do in the data source is to start building our data

model and we can do that by combining tables together using relationships joins and Union and you can do many

other stuffs like setting the right data types doing aggregations renaming stables and columns creating new

calculations and filters and so on all right so now to summarize the data source component in Tableau contains the

following informations we have the data connectors to connect Tableau to our data we have the access informations

where the locations of our sources going to be stored and as well we can decide whether we're going to load an extra

copy of our data inside Tableau we call it an extract connection or we're going to leave it as Live Connections in the

data sources and the last thing we have the data model inside data sources where we can combine tables together and do

aggregations or we can do some other custom stuff all right so once we are done with the setup of the data source

we have the connection whether it's extract or live we have our data model and everything is ready now we're going

to go and start building our visualizations and Tableau organizes the visualizations in three levels the first

one is the worksheets so we can use the data available in our data sources to build a single view only one visual it

could be a bar charts a pie charts or a table View and as you can see each worksheet is connected directly to a

data source but in Tableau you can build a worksheet from two different data sources by using very powerful combining

methods called Data blending and this is very unique feature in Tableau you cannot find it in any other bi tools

where the data in one visual can come from different sources and once we have the these different worksheets we can go

to the next level where we start combining these worksheets into One dashboard to show the different visuals

in only one view but keep in mind if you want to do any changes in the visuals you have to go back to the worksheets

and do the adjustment there and now we come to the last level we have the stories as you know the main goal of

doing data visualizations is to tell a story so you can build like a sequence of worksheets or dashboards that works

together in order to tell the users story based on your data all right so now you might ask me which visualization

level is the right one for you well if you have only one visual then go with the worksheet but if you want to build

some kind of qbi to monitor process then build a dashboard and if you want to present your data and tell a story from

it then go and build a story all right so now we have in Tableau desktop both of the data sources and the

visualizations and these two components are contained in something called a tableau workbook so now the question is

after you're done building your data sources and visualizations what can you do with this workbook well you can share

it with your colleagues in your team or department and there is two ways to do that either you're going to go and send

a tableau file directly to the users or you're going to go and publish the workbook to a tableau server or cloud

and from there your users and your team can access your workbook all right so now back to the big picture

the Tableau architecture let's talk about the ler on the right side the consumer layer there is different ways

to consume Tableau visualizations depends on the users's clients and on the tasks the users do so we start with

very small group of users that they might use Tableau reader to view and interact with Tableau visualizations and

they usually don't want to edit or create something new for this group of users we're going to send them a TBL

file as we learned they're going to need the Tableau packaged workbook twbx and we might have another group of

users usually they are your team colleagues they want to build analyzes on top of your work they're going to use

dblo disktop to do that for them we can send any kind of Tableau files depends on their requirements and their tasks

and now we have a big group of users or consumers that they can access Tableau server or Cloud to view and interact

with Tableau visuals they can use their web browsers like Google Chrome and Firefox to access the content of tblo

server and from there they can view interact and even edit the visualizations if they have enough

permissions or they can use Tableau mobile app on their smartphones or tablets to view and interact with your

workbooks but they cannot use it in order to edit the table visualizations so for this group of users you will not

send them any files first you have to publish your work to the server and here we have two options either you're going

to publish only the data source or you're going to publish the whole workbook to the Tableau server or cloud

and after that you're going to share the link of your workbooks to the users and now to the last group of users that's

worth mentioning they are the static users you can always export your data and visuals from Tableau desktop and

send it directly to the users as a BDF or Excel so of course it's static and they cannot interact with it all right

so so far in the Tableau architecture we talked about the source layer we did Deep dive in the Tableau desktop and its

components and we understood the different type of consumers and the clients all right so previously we start

sketching the Tableau architecture where we learned about the source layer the desktop layer and the consumer layer now

we going to unbox the server layer in Tableau architecture and in order to better understand Tableau server

components I'm going to walk you through three three scenarios from the user point of view what can to happen exactly

in tblo server once we publish a workbook or when we log into the server and access a workbook so let's

go so let's say that you want to publish a tableau workbook with an extract what going to happen tblo desktop going to

request the server to upload the workbook twbx and the first component in TBL server that going to receive the

request is the Gateway the Gateway knows how to forward the request the right server components and in this situation

the right component to process the publishing is the application server so the Gateway going to forward the request

to it and as we learned the Tableau workbook holds two different types of informations the metadata stored in the

XML files and the data itself stored in hyper files and in Tableau server those two different types of files going to be

stored in two different places the application server going to send the XML file to be stored in the server

component called repository and the Hy file going to be stored in another component called the file store so what

we have learned so far the Gateway is responsible to forward the request to the right component the application

server is the one that going to handle the publish process the reposter going to store the XML files the metadata of

the workbook and the actual data the hyber going to be stored inside the file store all right so now our workbook and

our data are published to Tableau server it's it's time now for our users to log the Tableau server and start interacting

with our dashboards so let's see how this going to work let's say your manager is Michael Scott and Michael

wants to check your sales dashboards in Tableau server and I am going to do it I need a

username and I have a great one and once Michael give these informations a request going to be sent

to the server as HTTP request the first thing that's going to hit is the Gateway the gateways notes that the application

saver is the right component to handle the authentication process so the Gateway going to forward it to it and

then the application server going to ask the reposter to check if the credentials username and password are correct and if