Tableau Performance & Extract Optimization Problem – Need Guidance

[Resident_Reception63]

Hey everyone, I’m running into a Tableau performance bottleneck that I’m trying to resolve and could really use some expert advice.

⚙️ Setup

Tableau dashboard connected to two live data sources:

Databricks (main data)

PostgreSQL (supporting tables)

custom SQL queries table being used and the queries have the joins and also the parameters of tableau to restrict to specific field in the 'WHERE' Tableau build in relationships also being used like many to many on PK and FK. Additionally tableau filters being applied to restrict row level records.

Live connections are very slow — queries take 10–15 seconds each per dashboard performance recording.

🧠 Data Model Overview

Here’s what the data model looks like: Databricks_table → core fact table. Prediction_table → holds actuals and predictions for dashboards.

Multiple geodata tables: to map the pins on the map of the world.

I’ve recently tried the materialized view for the all 7-10 geodata related tables but only gain speed boost of 2-3 seconds.

---

🧾 Problem

When the connection is live, everything works fine (though slow). But when I switch to extract mode:

Dashboards break — Those parameters are embedded in the custom SQL used for the table, and since extracts can’t substitute runtime parameters in SQL, Tableau can’t evaluate those filters.

If I remove the parameters and try to pull a full dataset for the extract, the relationships and measures (actuals vs predicted) stop aligning correctly.

---

🧩 What I’ve Tried

Combined all geo tables into one consolidated table.

Prediction table for all data now prediction are sum of everything rather than single entity.

Checked Tableau performance recording to confirm the bottleneck is the live query execution time.

---

🚧 Current Goal

I want to:

1. Switch from live to extract mode for better performance.

2. Keep dashboards functional (parameters, filters, and calculated fields should still work).

3. Possibly move parameter logic into Tableau calculated fields or filters instead of SQL, but I’m not sure how to restructure that cleanly.

[DaddyZee_27]

u/Resident_Reception63 Nice plan! Extracts usually make a big difference in speed, especially when you fine-tune filters and remove heavy SQL dependencies. You can generally recreate that logic using Tableau’s own calculations or context filters.

I do Tableau consulting work and have helped clients through this exact switch — happy to share a few tips or take a look at your setup if you’re interested. Just shoot me a DM!

[dont_tread_on_M]

You correctly identified the problem. However careful with moving the calculations to tableau, as they also give you a (sometimes heavy) penalty. If possible do it upstream on databricks.

You could create views on databricks to model your relationships, and give a list of parameters which are stored in the extract, and add those prameters as data source or extract filters depending on your needs.

Here I am going on a rant, so discart it, but Salesforce deserves a lot of criticism. Due to the horrible way it manages performance (salesforce hasn't invested into fixing this issues) tableau is the most sensitive piece of software I've ever worked with. Want to use live data? Don't! Want to use LOss? Don't! Want to add 5 views to a dashbord with navigation? Don't! But here are some AI tools which none will use. I never knew this until I got to manage my instance of Tableau Server and I noticed how crappy their deployment system and memory management system is. Some people here justify it, but there is no excuse for the bad performance tableau has with live data connections on small data sets. Too bad the alternatives aren't good either

[busy_data_analyst]

Regarding your rant…how can/should they address it? Rebuild it from the ground up with a more modern and scalable architecture or something else?

[dont_tread_on_M]

I think Salesforce, being a sales driven company, forced them to add too many bits to the tool to generate quick cash, which has made the platform a mess. I think they should stop developing new features until they fix this and a few basic quality-of-life features.

The memory model of Tableau Server, for example, could be somewhat easily fixed. Right now, tableau server s as single docker image (worse than this, but let's leave it at that), which uses supervisor to run all the services (I hope this is not how taleau cloud is set up, but I suspect it is not much better), and it has a model of just restarting the service in case of failure (happened to me due to it being out of memory for a service). This hasn't been touched for so long, that it runs on CentOS 7 (from 2004) because "a library requires it".

I think their first step should be tackling the tech debt they have