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How to speed up Tableau by using Performance Recordings?

Tableau Performance Recording Timeline
Tableau Performance Recording Timeline

Getting your dashboards up to speed can be quite difficult if you don’t know where the latency is situated. The first and most important rule about making workbooks more efficient is to understand that if it loads slowly in Desktop on your computer, then it will be slow on the server too once it is published. Tableau Desktop and Tableau Server each have their own way to enable, record, and analyze performance.

A must have for performance tuning your workbooks. All you have to do is start the Tableau Performance Recording, make your workbook action and stop the Performance Recording. A few seconds later, Tableau opens a new workbook with the Performance Summary dashboard in it.

Create a performance recording in Tableau Desktop

  1. To start recording performance, follow this step: Help > Settings and Performance > Start Performance Recording
  2. Make some dashboard operations and/or refresh your data source(s).
  3. To stop recording, and then view a temporary workbook containing results from the recording session, follow this step: Help > Settings and Performance > Stop Performance Recording
  4. You can now view the Performance Summary dashboard and begin your analysis.

Create a performance recording on Tableau Server

  1. Administrators must enable the feature. This is located under settings, for each site.
  2. Check the box and save for Workbook Performance Metrics.
  3. Navigate to a view on the server.
  4. Remove the iid=xx from the URL.
  5. Enter in its place record_performance=yes. Your full URL should now look something like this: https://data.alexloth.com/#/site/AA/views/Superstore/Summary?:record_performance=yes
  6. After the page reloads, you’ll notice the ID is added automatically back to the URL and that a performance button appears within the View’s toolbar. Don’t click on the performance button yet.
  7. Do some filtering and some clicking within the workbook such as applying filters, selecting marks/rows, and clicks that cause actions to other elements of the visualization.
  8. Then click the performance button.
  9. Now you’re ready to click on the Performance button which will launch a new window with the Performance Summary dashboard.
  10. Don’t forget to disable the performance recording in the admin settings when you are finished.

Understand the Performance Summery dashboard

The Performance Summery dashboard contains three views:

  • Timeline: a Gantt chart displaying event start time and duration.
  • Events sorted by time: a bar chart showing event duration by type.
  • Query text: It optionally appears when clicking-on an executing query event in the bar chart.

Time line Gantt chart

The uppermost view in a performance recording dashboard shows the events that occurred during the recording, arranged chronologically from left to right. The bottom axis shows elapsed time since Tableau started, in seconds.

In the Timeline view, the WorkbookDashboard, and Worksheet columns identify the context for the events. The Event column identifies the nature of the event, and the final column show each event’s duration and how it compares chronologically to other recorded events.

The events sorted by time

This section of the workbook shows the duration of recorded events in descending order. This is useful for observing the execution time of each event that occurs during the performance recording. This will help you identify any lengthy events that may be the cause of performance problems.
Events with longer durations can help you identify where to look first if you want to speed up your workbook.

Different colors indicate different types of events. The range of events that can be recorded is:

  • Computing layouts: If layouts are taking too long, consider simplifying your workbook.
  • Connecting to a data source: Slow connections could be due to network issues or issues with the database server.
  • Executing query: If queries are taking too long, consult your database server’s documentation.
  • Generating extract: To speed up extract generation, consider only importing some data from the original data source. For example, you can filter on specific data fields, or create a sample based on a specified number of rows or percentage of the data.
  • Geocoding: To speed up geocoding performance, try using less data or filtering out data.
  • Blending data: To speed up data blending, try using less data or filtering out data.
  • Server rendering: You can speed up server, rendering by running additional VizQL Server processes on additional machines.

Query text

Alternatively, the workbook also displays the query text for any specific event that you want to examine in detail. You can access the detail by clicking on any of the green executing query events in the bar chart. This is a handy feature which allows you to review any query text that may be of interest without having to leave the tableau performance summary dashboard.

If you click on an Executing Query event in either the Timeline or Events section of a performance recording dashboard, the text for that query is displayed in the Query section.

Quantitative Finance Applications in R

Do you want to do some quick, in depth technical analysis of stock prices?

After I left CERN to work as consultant and to earn an MBA, I was engaged in many exciting projects in the finance sector, analyzing financial data, such as stock prices, exchange rates and so on. Obviously there are a lot of available models to fit, analyze and predict these types of data. For instance, basic time series model arima(p,d,q), Garch model, and multivariate time series model such as VARX model, state space models.

Although it is a little hard to propose a new and effective model in a short time, I believe that it is also meaningful to apply the existing models and methods to play the financial data. Probably some valuable conclusions will be found. For those of you who wish to have data to experiment with financial models, I put together a web application written in R:

TSLA
Quantitative Finance Analysis in R (click image to open application)

How to Log your Twitter Follower Stats with IFTTT to a Google Spreadsheet

tstats GitHub repository
The tstats script (on GitHub) logs your Twitter Follower Stats with IFTTT to a Google Spreadsheet

How can we log the follower statistics for a Twitter account?

In order to store these stats, I’d like to use IFTTT’s new Maker channel that was introduced last month. I have created a simple Bash script (tstats.sh) to log this data to a spreadsheet in my Google Drive. I run this as a cron job every 24 hours.

Prerequisites

Ruby:

sudo apt-get install ruby-dev

Twitter CLI:

gem install t

Authorize your Twitter account:

t authorize

A Google account, as the log is saved to a spreadsheet in your Google Drive.

An IFTTT account.

Connect the Maker and Google Drive channels to your IFTTT account.

Usage

cd into the tstats directory and edit the script with your IFTTT secret key, your IFTTT trigger event name and your Twitter screen name. Make the script executable with:

chmod +x tstats.sh

Then simply run it with:

./tstats.sh

If you receive a “Congratulations” message and an entry is added to your spread sheet, you can go ahead and add it to your cron to run at a predetermined time.

To have this script run every 24 hours, add this to your crontab (you may need to change the path):

42,09 * * * * /home/user/tstats/tstats.sh >/dev/null 2>&1

[Update 26 Jul 2018] Now on GitHub: Yes, three years later this script is still hot! However, WordPress is not the perfect place to host code. As part of my preparation for my TC18 session on Social Media in New Orleans, I moved the code to a GitHub repositroy: https://github.com/aloth/tstats

How to unleash Data Science with an MBA?

Servers record a copy of LHC data and distribute it around the world for Analytics

My Data Science journey starts at CERN where I finished my master thesis in 2009. CERN, the European Organization for Nuclear Research, is the home of the Large Hadron Collider (LHC) and has some questions to answer: like how the universe works and what is it made of. CERN collects nearly unbelievable amounts of data – 35 petabytes of data per year that needs analysis. After submitted my thesis, I continued my Data Science research at CERN.

I began to wonder: Which insights are to be discovered beyond Particle Physics? How can traditional companies benefit from Data Science? After almost four exciting years at CERN with plenty of Hadoop and Map/Reduce, I decided to join Capgemini to develop business in Big Data Analysics, and to boost their engagements in Business Intelligence. In order to leverage my data-driven background I enrolled for the Executive MBA program at Frankfurt School of Finance & Management including an Emerging Markets module at CEIBS in Shanghai.

Today companies have realized that Business Analytics needs to be an essential part of their competitive strategy. The demand on Data Scientists grows exponentially. To me, Data Science is more about the right questions being asked than the actual data. The MBA enabled me to understand that data does not provide insights unless appropriately questioned. Delivering excellent Big Data projects requires a full understanding of the business, developing the questions, distilling the adequate amount of data to answer those questions and communicating the proposed solution to the target audience.

“The task of leaders is to simplify. You should be able to explain where you have to go in two minutes.” – Jeroen van der Veer, former CEO of Royal Dutch Shell

IMF Global Data Explorer

How about some visual takeaways from the IMF’s World Economic Outlook? Recently I prepared two nifty data visualizations with Tableau that I like to share with you.

These visualizations allow you to explore plenty of economical data, including IMF staff estimates until 2020. Don’t forget to choose “Units” after switching “Subject” on the right-side bar. A detailed description on each subject is displayed below.

Tableau