Do you want to evaluate/learn Tableau Prep, the new ETL/Data Prep Tool from Tableau?

Just 2 weeks back, I got started with Maestro (Update: now called Tableau Prep after the release in April 2018), the ETL/Data Preparation tool from Tableau – I did not realize that the product would have such an impact on me. For the last 4 months, I have taken time out from regular professional work (analytics consulting) to dedicate to creating a curriculum for teaching Tableau based on the lines of Montessori education system, where the emphasis is on learning the core fundamentals by small and gradual experiments – eventually, the student can discover many of the insights that surprises many!

During the last month I was preparing for 2 topics – Combining data from multiple sources and calculations. I have done a lot of work in both areas in preparing and organizing my thought process. Little did I realize that Maestro would come and sweep all of that work.

In a flash of inspiration, I have spent a few intense days, creating over 10 videos over the weekend to explain the different features of Maestro, but I have also been able to cover in depth the 2 topics at a level that I have not done before – Joins and Calculations.

In these videos, I have taken the liberty to compare Maestro with the tools that I know – Alteryx, Wrangler, and in some cases even Power Query. And I also criticize Maestro liberally – my aim was to help the Maestro team to see it through the eyes of a typical user so that they can work on improving the usability/stability of the product in the upcoming releases.

Here is the Youtube playlist that contains 12 videos:


  1. In the first video, I go over a complete case study in which almost all the features of Maestro are covered, although at a fast pace. If you want to get a first impression of Maestro and to understand what it is capable of, this video might be useful. For a lot of Alteryx users who are curious about this new boy in the town, this video might be of interest.
  2. In the second video, I have tried to summarize all the different tasks involved in data preparation into a coherent framework so that it can serve as an easy way to visualize all the different details – If you are a beginner in this area and/or wondering why would we even need a tool for preparing data in addition to Tableau, then this video might provide some answers. If you are experienced in this area, I invite you to reflect on my framework and share your feedback.
  3. Videos 3 – 11 (9 videos) cover each topic in depth. The 2 topics that I felt very satisfied in producing are Joins and Calculations – I have received feedback on my Youtube channel to cover these 2 topics in depth and I hope this is a starting point. I still plan to cover calculations in a lot more depth, focused on Tableau.
  4. The last video is my conclusion on the strengths and weaknesses of Maestro as of now in Beta v4. If you have different opinions, please share with me so that I can learn and enrich myself.

Before I conclude, I want to dedicate this series to 3 individuals who  have influenced me in this area:

  1. Hadley Wickham  – He is a professor and contributor to many great packages in the language ‘R’. I talk about him in detail in my second video. When I have read his paper on tidy data, It had such an impact on me that it made me even learn the language of R and got me interested in data preparation once more (as I have moved up to other areas of value addition, leaving my team to handle this technical task).
  2. Joe Mako – For Tableau community, Joe is such an inspiration. I got to know Joe when I was trying to solve a complex problem which was beyond my reach in my early days of Tableau. Post that session, whenever I had something that I felt was beyond my reach, the person to call was Joe Mako – He was always available, despite the fact he was up in the night. Through him, I have learnt about Alteryx and got interested in that as tool – “if Joe likes a product, there must be something to it” I thought.
  3. Ken Black – I never thought that we could build friendships over blog. It is Ken whose sheer passion for Tableau and Alteryx got me finally started with Alteryx. I love his articles and the common thing between us that we love teaching. His focus is on data blending and I really enjoy reading his articles.

Any day, I would love to be a student of each of these 3 great teachers – I have been inspired by them in ways that I can’t easily put into words.

Before I close, I want to acknowledge the One who is the mastermind behind all of this – the Almighty, the All-wise – if not for the people I have met, if not for the projects/situations I have been given, if not for the great teachers I have learnt from, and if not for the inspirations that I have received during these last 4 months when I am trying to capture the fundamentals of Analytics, if not for the guidance to follow my heart, this playlist and none of the other videos about which people send me generous praise would not have come into being. All praise belongs to Him alone! And I submit this work to Him with all humility and gratefulness!

First Impression of Tableau’s Data Prep Tool – Project Maestro – What a blessing!

The invitation for beta testing of the new ETL/Data Preparation tool from Tableau has been sitting in my inbox for a few months. For the past 3 – 4  months, I was quite busy in creating my professional dream – a montessori-style curriculum for teaching Tableau. Yesterday, I went to a client site where we have been struggling with cleaning up customer master from 2 different systems. We have been doing a lot of work manually and we have tried a number of ETL tools over the past 1 year. Yesterday, I wanted to try Maestro to see whether it would help – in about 10 hours from downloading the product, what a progress we made! What a blessing – I am so grateful for having access to such a product! A lot of ideas I had in my mind about how an ETL tool should behave were implemented there. When we were wrapping up, the client, who is a family business owner, commented that it is such an useful product.

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Dates Magic – Analyzing Trends & Seasonality in Tableau

One of the first things that I was very impressed with Tableau when I tried it in early 2013  ago was the ease with which dates analysis can be done in Tableau. It felt like sheer magic to me then (and even now) – I came to Tableau from Powerpivot and Qlikview background. In those tools, you have to do a lot of work to enable just basic quarterly, monthly analysis. In Tableau, everything was just built-in. I just couldn’t believe that it was so easy.

In my opinion, this is one of the most valuable functionality for a typical business user. I have seen a client of mine, who is a owner of business, create many intricate graphs to analyze trends & seasonality with just an hour of training in Tableau  – in this video, I will show you the different options for understanding trend & seasonality in Tableau in 13 minutes.


Caveat: Now that I am a Tableau power user, there are a few quirks with how Tableau handles dates that can frustrate a new user – especially the discrete vs continuous part. But hey, we live in a world, where everything created by humans is imperfect in some way – so, let us enjoy what is available and be grateful 🙂


Golden Rule of Aggregation in Tableau

In this video, I talk about a simple rule that can be of immense help in understanding how advanced calculations work in Tableau – if you properly understand the Golden Rule, you are well on your own way to figuring out how any advanced table calculation or LOD calculation actually produces the result.

Here is the gist of the video:

Before you understand the Golden Rule, you need to understand one more fundamental idea that is crucial in the reporting world: Level of Detail or granularity.

We can understand Level of Detail of a report by comparing it to the level of detail we see on a Google Map as we zoom in and out. As we zoom in, we see more detail and as we zoom out, we see less detail – the same thing applies to a report/chart. As we add more dimensions, we see more detail and as we remove dimension, we see less detail. Hence, Level of Detail is nothing but the combination of dimensions on the report.

But what does a Dimension mean for the database? What does the database do when you add one more Dimension to your report?

For the database, Dimension are equivalent to Filters. As you add a Dimension, the database adds a Filter. If you have 3 dimensions on your report, then the database has to add  filters on each of the 3 dimensions to calculate the value of a cell in your report.

Hence, here is the Golden Rule: For each cell/mark,

  1. Find the Filters i.e. Level of Detail
  2. Apply the Filters
  3. Then do the Calculation e.g. SUM

In one-line, it can be written as “Filters THEN Calculation


From this video onwards, I am not going to be producing a text version immediately. It is difficult for me to find large chunks of time to produce the videos and to write the text version. But as I find time, I will come back and update the post to have the text version – for now it is only going to be videos. I hope you find them to be of value.


Basics of Aggregation Part 2 – How do we make sense of Text?

It has been a long time since I have posted here – 4+ months. Number of external events happened that really threw my schedule into a disarray – the first and the biggest one of them was a cyclone that shook our city, Chennai (India), on 12 December 2016. This resulted in internet getting disconnected for a number of weeks and even when the connection came back, it was quite unstable. Then, we had a number of different political events happen which disrupted normal life. Hence, the flow I had in creating this series was disrupted. Once I have lost the flow, it was hard to come back. And project deadlines and international travel also made it difficult for me to get back to doing what I love.

Now, I had to spend a number of days trying to read/watch all that I have created to reorient myself. I really struggled to get started with the same energy and the ideas I had before. If any of you had read the book – Big Magic, you can understand the phenomenon I am going through better – once you start any creative endeavour, it is hard to resume if you take a break. Quite literally, the ideas go somewhere else and it is hard to bring them back. If you have not read the book, I suggest that you at least take a look at Ken Black’s short post where he talks about this book. By the way, Ken was the one who introduced this book to me and it helped me in so many ways to reflect consciously on the process of creating something – Thank you so much Ken.

Enough Stories – let us get back to the subject matter at hand. In this post, let us answer the 2nd of the following 2 questions that I introduced in the last post:

  1. How do we make sense of Numbers?
  2. How do we make sense of Text?

The last post answered the first question and this post will answer the second question.

Here is the gist of the video:

There are 3 ways to make sense of Text:

  1. List the unique values
  2. Count the number of unique values
  3. Count the number of records.

When we are generating a report using any text value (which are called Dimensions in Tableau), then the default behaviour is to list the unique values. Counting the number of unique values (technically called as Distinct Count) is another thing we do often.

Here is a text-version of the video for those of you who prefer to read than to watch – please note that this is NOT a transcript of the video.

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Basics of Aggregation Part 1 – How do we make sense of Numbers?

In this post, My aim is to answer the question ‘What really is an aggregation and why do we need it?’. The way I am going to explain aggregation is by helping you answer the following 2 questions:

  1. How do we make sense of Numbers?
  2. How do we make sense of Text?

Here is the gist of the video:

Aggregation is nothing a but a technical name to a highly intutive process that we all undertake when we are presented with a lot of information – when we have too much information, we tend to summarize it. So, that is what we are doing with numbers here – we are summarizing them to 4 typical summary values (Total, Min, Max & Average) to make sense of them without going through every single value.

Here is a text-version of the video for those of you who prefer to read than to watch – please note that this is NOT a transcript of the video.

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Dimensions & Measures – Simplest explanation you have ever heard

It has been a while since I have posted on Table Calculations. After finishing the Paradoxical Problem 2 video, I knew that I had a lot of work to do on Paradoxical Problem 3 – Though I have presented the Paradoxical Problem 3 number of times, I felt that I still need to simplify the presentation more so that everyone can relate to my explanation easily. It is a challenge as the Paradoxical Problem 3 deals with a problem that is complex in itself. So, I decided to create a few videos on the ‘Basics of Data Analysis’ that would set up the stage for me to explain the Paradoxical Problem 3 effectively.

Thanks for all of you who have reached out to me asking when I would post the Paradoxical Problem. I was quite surprised to see a number of people who are not Tableau users are watching these videos. And I was also surprised to see that some very senior people in my network (Director and CXO-level resources) have watched the videos. This confirms my feeling that the problems I described are not limited to Tableau.

So, here is the first video in the ‘Basics of Data Analysis’ areas. Here my aim is to explain Dimensions and Measures in a simple way. I have heard a lot of different explanations of these 2 terms that are filled with jargons. I wanted to create an explanation that a normal business user can easily understand and I hope I have succeeded.

Here is the gist of the video:

Measures are nothing but Numbers

Dimensions are nothing but Text (+ Date)