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Bharti Goel
- April 24, 2023
Are you looking to enhance your data visualization skills and wondering how Data Modelling in Tableau can transform your analytics? Data Relationship, Data Joining, and Data Blending are three important concepts in Data Modelling in Tableau that are essential for effective data analysis and visualization. In this blog, we will explore these concepts and how & when they are used in Tableau.
Basics of Data Modelling in Tableau
Data Relationship
Data Relationship in Tableau refers to the way multiple tables or data sources are connected to create a single view. This connection is based on a common field or key in both tables, which is used to join them together. Tableau allows users to create a data relationship between tables, without the need to join them. We can use Data relationships to build more complex data models, especially when working with large data sets.
Data Joining
This refers to the process of combining two or more tables based on a common field or key to create a single, unified table. The purpose of joining data is to create a more comprehensive view of the data that includes information from both tables. Tableau supports different types of joins, including inner join, left join, right join, and full outer join, which allow users to customize how the tables are combined.
Data Blending
Data Blending is the process of combining data from multiple sources that are stored in different databases or file formats. In Tableau, data blending is used when a user wants to analyze data that is stored in different data sources or when a user wants to combine data from different levels of aggregation. The blending process involves creating a data connection between the data sources and then combining the data by matching the fields that are used to blend the data.
How Data Modeling in Tableau evolved
It’s worth noting that while Data Joining has been a part of Tableau since the beginning, it has evolved to include more advanced join types and options for customizing joins. However, Data Blending was a major addition to Tableau in version 6.0. It has continued to evolve and improve with subsequent releases.
Data Relationships are a more recent addition to Tableau. However, they have quickly become an important feature for working with multiple data sources and creating more complex data models.
It seems like every time we turn around, there’s a new concept to learn. As soon as we think we’ve got it, another one pops up to confuse us more. For example, think about Data Relationship, Data Joining, and Data Blending.
About Data Modeling in Tableau
These three concepts are all about bringing different data sources together to create a more comprehensive view of the data. But let’s be real – who can keep them straight?
Data Joining is like trying to merge two puzzle pieces – sometimes it works seamlessly, and other times you’re left with a big mess. Data Blending, on the other hand, is like trying to mix two different types of liquor – it might seem like a good idea at first, but the result is often a headache.
And as for Data Relationships, well, that’s like trying to navigate a complicated relationship with your ex. Sometimes it’s smooth sailing, other times it’s a total disaster, and most of the time you’re just left scratching your head and wondering why you ever got involved in the first place.
So if you’re still confused about Data Relationship, Data Joining, and Data Blending, don’t worry – you’re not alone.
Compare: Data Modeling in Tableau
Let’s compare Data Relationship vs Data Joining and Data Blending in Tableau
| S. No. | Aspect | a) Data Relationship | b) Data Joining | c) Data Blending |
| 1 |
Definition |
Helps to connect two or more independent tables (from the same or different data connections or sources), but does not provide the merged tables | It helps to merge the data from two or more tables into a single table (from the same or different data sources or connections) & provides the merged table also. | Helps to combine the data from different sources or connections without physically joining them. |
| 2 |
Use Case |
Relationships are the default method, and we can use them in most instances, including across tables with different levels of detail. | Joins combine tables by adding more columns of data across similar row structures. | Blends, unlike relationships or joins, never combine the data directly. Instead, blends queries from each data source independently, aggregates the results to the appropriate level, and then presents the results together visually in the view. Because of this, blends can handle different levels of detail and also work with published data sources. |
| 3 |
Concept Launch Year |
2020 | 2004 | 2011 |
| 4 |
Flexibility |
Relationships are a dynamic, flexible way to combine data from multiple tables for analysis. | Joins are a more static way to combine data so not very flexible. | Related fields vary sheet by sheet, so they can be customised on a sheet-by-sheet basis only. Hence, it is not flexible at all. |
| 5 |
Shortcoming |
You can’t create relationships between tables from published data sources. | You can’t use a published data source in a join. Joins can cause data loss or duplication if tables are at different levels of detail, and joins must be established before analysis can begin. | Blends don’t create a new, blended data source. Instead, they are simply creating blended visuals per sheet. |
| 6 |
Join Type Planning Required |
No | Yes | No |
| 7 |
Matching Fields |
Requires matching fields | Requires common fields | N/A |
| 8 |
Join Types Supported |
It is based on Cardinality (Joins are NA) | Left, Right, Inner, Full Outer Joins | Left Join |
| 9 |
Effect on Calculated Fields |
Calculations are easy & straightforward with Relationships. | Easy & straightforward Calculations with Joins. | Calculations are different and difficult with Blending. |
| 10 |
Effect on Tableau Functioning |
Relationships are not combining the data at all. They are just connecting the data. | A join combines the data and then aggregates. | A blend aggregates and then combines the data. |
Conclusion: Data Modelling in Tableau
To sum up, we hope we were able to give you better clarity regarding when & how to use which Data Modeling in Tableau that can transform your analytics. Give it a practical try & if unable to do then connect with us. We would be happy to assist you. Since you are interested in Tableau, you must explore a very comprehensive blog on interview preparation for a business analyst role.
FAQ: Data Modelling in Tableau
1. How do I decide whether to use Data Relationship, Data Joining, or Data Blending in Tableau?
It depends on your data structure. Use Relationships when working with multiple tables at different levels of detail, Joins when you need a single unified dataset, and Blending when data resides in different sources.
2. Does using multiple joins affect Tableau performance?
Yes, especially with large datasets. Complex joins increase processing time and memory use. Relationships are more optimized because Tableau queries data only when needed.
3. Is Data Blending outdated after the introduction of Relationships in Tableau?
Not really. Blending still matters when you’re combining data from multiple databases or sources that can’t be joined directly. Relationships didn’t replace blending; they just expanded the flexibility.
4. Why do my numbers differ when using Join vs Relationship on the same tables?
That’s a common issue. Joins merge data at the row level, which can cause duplication or data inflation. Relationships query tables separately and aggregate correctly, which often produces cleaner results.
5. Can I use Relationships and Blending in the same Tableau workbook?
Yes, but carefully. You can model some tables with relationships and still blend results from another data source; it’s about knowing which connection suits your analysis.
6. What happens if my data has different levels of detail?
Relationships handle it best. Tableau automatically adjusts aggregation depending on the visualization. Joins or blending may require manual tweaking to avoid mismatched results.
7. Are there limitations with Relationships in Tableau?
Yes, relationships don’t support published data sources or certain calculated joins. If you need deeper row-level control, traditional joins still hold an edge.
8. Is Data Blending slower than Data Joining or Relationships?
Yes, because Tableau runs separate queries for each data source before merging aggregated results. It’s accurate but more resource-intensive than relationships.
9. How can I learn practical Data Modeling in Tableau with real projects?
You can enroll in Edu4Sure’s Tableau Training Program, where concepts like Data Relationship, Joining, and Blending are taught using real business datasets, guided dashboards, and case-based projects to build mastery from scratch.
10. Which Data Modeling technique do professionals use most in real-world analytics?
Modern analysts increasingly prefer Data Relationships for flexibility and scalability, especially in enterprise reporting. However, blending and joining are still widely used depending on data architecture and business needs.
Do you need professional training on any Business Intelligence tool? Kindly contact us via partner@edu4sure.com or WhatsApp at +91-9555115533.
