“How much data can Tableau handle?” is one of the most frequently asked questions we hear from new Tableau users. Furthermore, we frequently undertake Tableau engagements solely with the purpose of helping a workbook and a dashboard operate more effectively.
Tableau is capable of managing extraordinarily massive data sets, and with each new edition, the program grows even more powerful.
That being said, determining how much data Tableau can manage is a difficult issue to answer because “big data” is a relative phrase. Tableau explores the underlying data to show a visual response whenever a new field is added to a view.
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This operates similarly to how a database query language, such as SQL, is used to ask queries of a database. As a result, efficiency can be affected by a variety of factors, including not only the number of records, but also the processing capacity of the hardware, the complexity of computations, the kind of data, and so on.
This post will discuss five strategies for making your Tableau worksheets run more effectively.
5 Tips for Creating Efficient Tableau Workbooks
1. Consider the data that is absolutely necessary from a strategic standpoint.
The most significant efficiency increase that I’ve noticed is when the size of the data set is lowered by deleting unnecessary material from the file. This may sound apparent, but I can’t tell you how many times I’ve seen authors try to show more data than they actually need.
For example, if your company’s criterion for analyzing data is year over year, don’t even bring in data from three years ago. In this situation, you delete at least one-third of the data before you even begin!
The accumulation of dates is another piece of low-hanging fruit that I frequently observe.
For example, if your organization collects data at the timestamp level, but your analytics do not need hour/minute / second-level views, you may aggregate the dates by day. The number of records will be greatly reduced as a result of this.
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These are just two popular examples, but bringing in only the data you need is the first step toward generating effective Tableau worksheets.
Prepare your data before importing it into Tableau.
While Tableau includes many excellent data preparation functions, they are not the software’s core value, and there are superior options for this role.
I recommend utilizing a tool other than Tableau to prepare the data collection so that when you use it in Tableau, Tableau can do what it does best.
Utilize Context Filters
I divided the “first” advice into sections since data preparation and data size limitation are crucial to the productivity of your Tableau workbooks.
Another method for limiting the data that Tableau visualizes is to use “context filters.” Context filters are processed first and may be regarded as temporary tables for your display.
When you apply a context filter, Tableau produces a subset of the data set that is confined to the filter selection; then, all subsequent filters only touch the subset of data. Any dimension filter on the Filters Shelf may be used as a context filter by right-clicking on it and selecting “Add to context.”
2. Use the Apply Button to limit the filters.
When you consider filters as queries on data, it stands to reason that each incremental filter on a view would increase processing time. While there is no hard and fast rule for how many filters are appropriate, selecting which fields should be provided as filters may significantly increase the performance of your views.
Consider the first recommendation above and either filter the data before it arrives in Tableau or add the filter to context to speed up processing if fields need to be filtered more permanently.
Using dashboard actions instead of filters is one technique to decrease the number of filters on a dashboard.
Filtering the remainder of the dashboard is more efficient when you use a sheet as a filter or add a filter dashboard action that executes on hover or choose.
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If you’re using filters, a useful Tableau feature is that dimension filters that are displayed to end-users on the view may be set to process only after all modifications to the filter have been chosen.
For example, if you display a dimension filter with ten dimension members, Tableau will apply the filter every time a dimension element is selected or unchecked on the filter. So, unchecking one dimension element causes the view to be reprocessed; unchecking a second dimension member causes the view to be reprocessed again.
In this instance, click the dropdown arrow on the filter, hover over “Customize,” and pick “Show Apply button” if you prefer that the view only proceed once both dimension members have been unchecked.
Only when the end-user has made their filter selections and clicked the “Apply” button will the filters be executed.
3. Lower the Number of Marks
When you consider each data point or mark as a record that must be processed on a view, this suggestion makes logical sense: the more marks that must be processed, the longer it may take for the visualization to display.
The efficiency recommendations up to this point should have a significant influence on the quantity of marks you have left to deal with, but it is still important to remember. Depending on your analytical needs, it may not always be possible to minimize the amount of marks on a view, but there may be situations when changing the degree of detail might enhance efficiency.
Consider how you can group data points into hierarchies and/or make the analysis more detailed.
4. Don’t Forget the Data Types
One of Tableau’s most significant features is its ability to do custom computations on the fly.
This enables ‘discovery’ analytics, in which you may swiftly swing between different studies without knowing exactly what you’re searching for. While the computations are quite strong, they can have an impact on the workbook’s performance.
Consider the data types in your calculations to get the most out of the capabilities of the calculated field and keep your workbooks operating smoothly.
In terms of efficiency, not all data types are created equal, with the following data types listed in order of most efficient to least efficient:
- Date Time
Here is a brief explanation for each.
Boolean – A binary result that is either true or false.
Integers – Whole numbers are known as integers.
Float – Any integer, including decimals, is considered a float.
Date – Date gathered on a daily basis.
Date Time — A date that includes the timestamp level.
String – Text
5. Reduce the number of sheets, dashboards, and data sources.
This technique will not only help you be more efficient, but it will also help you preserve your sanity and improve the end-user experience.
In general, the more sheets, dashboards, and data sources you have in a worksheet, the more likely it is that it will run slowly. This is especially true when integrating many sheets on a dashboard with views that combine data from various data sources.
Tableau workbooks, like database architecture, will operate more effectively if you consider how workbooks might be divided down into smaller, separate files.
Not only will this improve productivity, but it will also make it easier for you to manage and assist your end customers. This is considerably easier and more efficient if you use Tableau Server or Tableau Online since several smaller workbooks may share the same cloud-saved data source.
If we genuinely have numerous dashboards that are linked, we prefer to produce a navigation dashboard that assists the end user in locating the most relevant views for their unique business queries.
If you have Tableau Server or Tableau Online, you may configure the navigation links to open new dashboards by adding URL dashboard actions with links to the dashboard locations online.
This method may also be utilized within certain dashboards (for example, add a URL action to execute on Menu that takes the end-user to another dashboard / further information).
While this isn’t an exhaustive list of Tableau efficiency suggestions, I’ve discovered that executing these methods follows (at least) the 80/20 rule, in that these five tips should help capture at least 80% of the prospective efficiency benefits in your workbooks.