At this year’s Tableau Conference, I tried to stay on the safe side and show mostly Tableau supported solutions: visualizations on real-time data, Jupyer Notebook like experiences and so on. I had only one exception, my “Tableau Extensions Add-ons” microframework that allowed me to show use cases like synchronized scrolling of dashboard elements, getting and validating user names and sessions and building user interaction heatmaps. People loved all of these, so let me show you the synchronized scrolling magic.
We all have our own Tableau Conference habits. Mine is to participate in the first-day hackathon, build something super cool that nobody understands and in the end win nothing. Tableau 2019 was no exception. Earlier this year, our team won the NYC Tableau Hackathon — and I had almost nothing to do with it. My tasks were limited to keep our squad hydrated, caffeinated and fed, so for TC19 I had
Tableau Hyper Database (“Extract”) is a great engine; it’s one of the reasons people obsessed with Tableau analytics. However, being a single node database server, it has its limits (performance and data size) – that we are going to break in this blog post. With adding multi-node capacity, partitioning and distributed query processing Hyper will act like a real enterprise MPP data warehouse – running on modern infrastructure like kubernetes.
In a new series of blog posts, what I creatively named as “Python Experiments in Tableau”, I will share a few projects that we are doing in-house to play with Python and Tableau. In the first example we will do something special with the “External Services” functionality: add live data from web services to implement up to date currency conversion calculations in Tableau. All this with only a few lines of code using thanks to TabPy and the Forex Python library. 
We are all excited about the great news that 
Somewhere around the end of July I was invited to show some magic from our consultancy portfolio in the Server Admin User Group’s virtual meetup. I choose (no, actually Mark choose) two topics: the first was automated query banding (practically server side search and replace to programmatically fill initial sqls for workbooks and data sources) while the second was this consistency checker tool. Why should you care about your repository consistency? Read more, I’ll explain.
Today I had the honour to present at the Tableau On Tour London conference. The main focus was the “web data connector trick” that allows one to implement reusable server plugins, explained 
SAP ERP systems are probably the best when it comes to robustness, flexibility and extensibility. However, two major issues almost always come up with SAP customers: the lack of operational analytics and the user interface which is… well… not the pinnacle of UI design. SAP constantly promises that they will replace the UI with something new but the reality is that 90% of SAP users still use the traditional SAP GUI. Operational analytics are mostly restricted to simple lists and so called “drilldown reports”.
It started with one of my colleague who managed a Tableau case from our end. His client was constantly dissatisfied: her workbooks were significantly faster with embedded data sources compared to published ones. Even if we told her that this is normal, this is the way how life works, so better accept it and let it go. But no, she wanted an explanation. Well, here it is.
I was casually sitting at my desk writing 
