All in Data
Sick of dealing with poorly formatted data in spreadsheets? No more! Check out this webinar to learn the fundamental techniques you need to use Tableau Prep to transform your data and life. We cover:
Pivoting
Transposing
Filtering
Dealing with Nulls
Grouping Messy Values
Removing Duplicates
Working with Tableau Published Data Sources (PDS) can be fantastic for data governance, integrity, performance, and privacy. It can also be frustrating when trying to combine them with other data sources.
A Published Data Source is a finalized version of data source - similar to a PDF. All the editing you want to make to your data needs to happen before the PDS is completed.
One of the biggest areas of technical advancement in Tableau Desktop over the last several years is the ability to model complex data sources. Relationships first debuted in 2020 but were only usable for simple data models because you could only use a single fact table as a base table.
In 2024, Tableau updated the Relationship builder to allow for multiple-fact tables and shared dimension tables. This ability is invaluable in complex analyses that leverage numerous tables.
When you union tables in Tableau, you append two tables of data vertically. That means if you have two tables that are 1,000 rows each, the result of the union will be a single table that is 2,000 rows. Shared columns will align automatically, and unique columns will not.
Unions provide the ability to relate multiple tables that contain different data types of structures without worrying about granularity. Many times, fact tables that primarily consist of measures cannot be joined together because the join would lead to undesired row duplication.
If you want to create the best user experience in Tableau, odds are you want to convert your data connections into extracts. Tableau Data Extracts (.hyper) provide the most efficient way to query data in Tableau. Extracts are local snapshots of your data which have been optimized for usage in Tableau.
Extracts can be customized to filter data, aggregate data, and even hide unused fields. Server and cloud-based data sources can support automated extract refreshes on Tableau Server and Tableau Cloud.
Effective data dashboard design is an art. It requires meticulous attention to detail. It includes visualizing the right data, displaying the appropriate level of granularity, designing an efficient layout, and making a number of minute design decisions so the data hits your user "just right".
If you're looking for a checklist of design elements to review before your next dashboard rollout, you've came to the right place! In this video, we discuss the following 15 design best practices:
Do you want to use Tableau to look at running headcount over time? For example, you work with employee level data which has Start Dates and End Dates and you'd like to be able to see how the total active headcount at the company has changed over the last 8 quarters.
That's something Tableau can do, but it isn't going to work out of the box. It's going to take some creative data structuring and calculations to get things working.
In 2020, Tableau unveiled a new way to combine multiple tables of data in Tableau Desktop: a Relationship. Billed as "smart joins", relationships provide the capability to merge multiple tables of data horizontally on shared dimensions (like a join). However, they don't duplicate data when the tables being combined have different levels of granularity.
So, are they better than joins? Not always! Relationships can reduce data duplication and improve performance, but a data set that is generated from a relationship might present blind spots if the data isn't perfectly clean.
Unions are an incredibly powerful tool in our data prep stage when working in Tableau Desktop. But how do you know when to use a Union and when to use a Join?
The general rule of thumb is: when you want to add rows of data to your data source, use a union!
Joins in Tableau Desktop are one of the primary methods for combining multiple tables of data horizontally. One of the great things about joins are their flexibility! You can choose if you'd like to keep all data from both tables in your result set, or if you prefer to keep only matching records.
How do you join two tables together in Tableau when they don’t share the same common field? Or what if that common field is slightly different in both tables? A Join Calculation can help solve a lot of those problems! In this video, we take a look at how to use a join calculation to join tables with mismatched fields.
Are you having trouble getting your data to work in Tableau the way you want? Join us to learn how to prepare data for analysis in Tableau!
You can find a thousand tutorials on how to use Tableau, but they're all worthless until your data is structured for analysis in the format that Tableau likes. Don't get stuck in your analysis journey because Tableau doesn't like your data.
6 data formats to avoid, 8 key rules for structuring data, and 1 video you can't miss. Check out the recording to learn about the key concepts you can utilize to prepare for your data for Tableau.
Blending is nearly as old as Tableau Desktop itself. Executing a blend in Tableau is a method for relating data from multiple different tables so it can be analyzed together. For instance, a sales value and a quota.
The superstore data that Tableau provides is a helpful tool for practice, mockups and trying out concepts. However, one of the frustrations is that it can get “out of date” so to speak so the latest date in the data set might be December 31, 2021 even though today’s date is month’s later.
When joining two tables in Tableau, have you ever wondered any of the following questions?
Is the join working?
Is the join resulting in data duplication?
Am I losing rows of data due to the type or argument of the join?
In its natural state, survey data is rarely in a format where it is optimized for analysis. If you need to visualize survey data, it's likely going to require some preparation and reshaping.
