Snowflake's Evolution: A Look at the New Features in 2024

Tableau filters allow their users to perform simple filtering using available metrics, as well as perform advanced filtering using complex calculations. While developing dashboards, Tableau offers the flexibility to view different types of filters such as drop-down filters, wildcard filters, list filters and many more. To enhance dashboard interactivity, Tableau has provided “Filter Actions” which allow users to filter an entire dashboard as per the value selected by the user in a particular visualization.
To create a filter that suits the user's requirements, Tableau engages several filtering techniques which also enhance data processing efficiency Each filter is designed for a specific function, and the order in which they are run can significantly impact performance.

6 Types of Filters in Tableau

Tableau employs six different types of filters, which are listed in the sequence of execution:

1. Extract Filters
2. Data Source Filters
3. Context Filters
4. Dimension Filters
5. Measure Filters
6. Table Calculation Filters

1. Extract Filters

Extract filters are utilised during the initial stage of data filtering. When datasets are uploaded to Tableau, we can utilise the extract filter to minimize the number of Tableau queries for the data source. By applying filters to the extract, we can further decrease the size of the data.

2. Data Source Filters

Data Source filters sort out any crucial or sensitive information while putting data into Tableau. It is compatible with both the Live and Extract connections.
We can add the data source filter to any column by selecting the ADD option. After selecting the ADD option, the ADD Filter dialogue box will appear, displaying all the fields. We can then select the field to which we wish to apply the filter and it is possible to change or delete data source filters as needed.

3. Context Filters

A context filter is generally used when we require this filter to be applied before any other filter. It has the potential to enhance the performance of massive data sources. They are made by dragging the dimension to the filter section box and selecting the Add to context option. This will make the dimension pill change its colour to “Grey”, indicating it is a context filter. Similarly, by clicking "Remove from Context", the filter becomes a simple dimension filter. A common use case for this filter would be when a user would like to find the top N products in any category.

4. Dimension Filters

Dimension fields include discrete categorical data, and we can remove or add the values we want to investigate. The process is simple as outlined below:

Step 1: Drag the dimensions from the dimension list to the filter section box.
Step 2: From the Filter Dialog box, select the values that we want to analyze.

The Filter dialogue box contains four tabs:

1. General: To select the members present in the dimension to either include or exclude
2. Wildcard: To filter the result based on a particular pattern
3. Condition: To filter the result based on a particular condition
4. Top: To filter the Top N products belonging to a particular category.

5. Measure Filters

Measure filters are applied to the measure fields, which have quantitative data. It can be used as follows:

1. Drag the measured field from the Measure box to the Filter area
2. Filter dialogue box with several functions will appear.
3. Select the operation that needs to be performed and click Next.

In the subsequent dialog box, there are 4 types of filters:

1. Range: To select the range of values to include in the result.
2. At least: Select the minimum value of a measure to filter the data.
3. At most: To select the maximum value of a measure to filter the data.
4. Special: To select null or non-null values.

6. Table Calculation Filter

Table Calculation Filters, being the last type of filters to be executed in the sequence of execution, do not filter out the underlying data. It is mostly used to filter out contents in the current view. Table Calculations are functions that are utilised for building Calculated Fields like LOOKUP, WINDOW SUM, WINDOW AVG, and so on.

Summing Up

Tableau Filters are the most commonly used feature by many organizations. It allows the clients to filter out confidential data and allows them to provide controlled view access to different internal users so that only data relevant to the user is accessed. Filtering out data means data processing becomes more efficient, which in turn makes the report generation process faster which in turn helps the organization arrive at quick and insightful business decisions.

Use Cases Addendum

Let’s find out how Tableau filters work to solve various tasks :
Use Case 1:

A simple drop-down filter can be used to filter the view of our requirements as shown in the visualization above.
In this example, Dept A has been selected such that the Total Budget is shown only for Dept A. Once we select Dept B, the Total Budget shows the respective value for Dept B

Use Case 2:

In the visualizations above, we can see the customer demographics related to gender, age group and educational status. With the help of Tableau’s Filter Actions, one can click on a particular segment and filter the rest of the visualizations as per the requirement.
For example, if we require to find how many customers in the age group of 35-44 are males, all we have to do is to click on the doughnut chart for “Male”, and the visualizations will show only the data for males. Now, we can find that the number of male customers in the age group of 35-44 is 272.

Use Case 3:

The Filter Action feature in Tableau can be leveraged to develop several creative solutions for customers. One such solution is the “Reset Filter” as shown in the above visualization.
With a combination of filters and filter actions, one can develop a reset filter that, on clicking, would reset all the currently applied filters on the dashboard and bring it back to its original unfiltered view. This comes in handy if the dashboard consists of multiple visualizations and contains heavy data.

TABLEAU CATALOG What could be better than a feature that could provide better visibility and better data management at the same time, and also ensure that the right data is always used for analysis. This is possible with the new feature -Tableau Catalog. This feature provides a complete picture of the data and how each data is connected. Another use of Tableau Catalog is linear and impact analysis. This not only shows which assets will change but also who will be affected by it, which makes work easier for many and avoids wastage of time. EXPLAIN DATA Tableau 2019.3 is up with a new Al-driven feature called the “Explain Data”, which helps people go from the “what” of the data to the “how” of it. With explain data, we can get an explanation for each unexpected value in the data by just a single click. On selecting the desired data point, the ‘explain data’(lightbulb) icon appears. For each value there might be a number of explanations. Each of these explanations are checked and only the most likely ones are provided as visualizations. Now these visualizations can be used for further explorations. TABLEAU SERVER MANAGEMENT ADD-ON Organizations that run critical deployment of Tableau Server at a large scale, have mentioned concerns over manageability and scalability. They have been in search for tools that could organize the management process in an efficient way, which could save a lot of time. Tableau solved this problem by introducing the Tableau Server Management Add-on – a new feature designed to help organizations manage the deployment of Tableau Server. With this, they can quickly react to the changing needs of the business as well as save time by organizing the management process in the most efficient way. Tableau Server Management Add-on, which makes running the critical deployment of tableau at a large-scale server much simpler. The server management add-on feature can help in optimising the performance of deployment by customizing which nodes process background jobs such as extract refreshes and subscriptions and isolating these workloads, to specific nodes. This makes it easier to scale deployments to the needs of their organization. This feature has a few tools, including two for better reliability and scalability and one for content migration, all of which helps the organizations to govern their data effectively. If you are interested in learning more about the latest Tableau release and use cases, please contact us at training@beinex.com/ info@beinex.com and we would be happy to schedule a Tableau demo or training for you and your company.

Note: The Server Management Add-on is not available for Tableau Online, as they manage everything from scaling, performance, and security on behalf of their Tableau Online customers. The Tableau Server Management Add-on can be separately purchased from the Tableau Server deployment.

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While sharing the content, permissions can be given to each user that restricts them from editing, applying different types of filters, and sharing it further. Users can create apps and add specific visuals to create live dashboards that can be accessed through smartphones and tablets. Moreover, one can collaborate with other designers and administrators to work together and create highly customized reports for a particular field.

Challenge

• The IT infrastructure of Atlantis where the Tableau Server platform was hosted was in an on-premise datacenter which was designed to be scalable and robust with multi node physical clusters including the server, storage and network components. However, most of the physical hardware was quite old and not equipped with the latest generation of physical servers.
• Frequent hardware crashes and portal downtime kept troubling the availability of the Tableau Server application. Assigning a touch hand support person to power on the hardware that was down seemed quite impossible due the restrictions during covid period. Hence, Atlantis wanted to look for another viable solution.
• Though the hardware setup at Atlantis was well equipped to meet the occasional spikes in the traffic, it was observed that over a course of 6-month time, most of the IT infra was underutilized than predicted. It was realized that spending huge amount of money on an old hardware plus software maintenance, license costs, internet bandwidth, datacenter cooling and maintenance, touch support personnel and electricity costs – were keeping the business operations challenging.
• There was an attempt by Atlantis to select a cost-effective solution that can host Tableau Server application servers, web servers and archival data. This way IT infra can be re-provisioned to host sensitive data on-premise and the rest on the cloud, thereby reducing the overall physical hardware costs spent on a yearly basis.

Why AWS

• Atlantis decided to migrate Tableau Server, database servers, and archival data to AWS.
• The Tableau Server’s AWS architecture includes Amazon Elastic Compute Cloud (Amazon EC2), that provides complete control of its computing resources, updates to tables in Amazon Relational Database Service (Amazon RDS) and AWS Elastic Load Balancer was used to distribute the traffic to the underlying EC2 instances based on the load.

Benefits

• Atlantis uses AWS services to provision infrastructure and deploy the Tableau Server platform to other departments within it. In addition, the Tableau Server resources that are no longer required to be run all the time are made to auto shutdown thus saving cost. Atlantis reported a 28% cost reduction after implementation of AWS for the Tableau Server platform.
• The implementation of Tableau Server on AWS made Atlantis confident in the security of its data, and its accreditation team is enthusiastic about the monitoring and auditing capabilities provided by AWS tools. With the implementation of IAM roles, Atlantis IT team was able to isolate systems and tightly control user accesses. These capabilities were harder to achieve within the existing infra but were available out of the box with AWS
• By adopting AWS to host the Tableau Server platform, Atlantis has been able to innovate and experiment to a degree previously impossible. For example, Atlantis compared the performance and cost-effectiveness of three different cloud solutions. Without moving to the AWS, the costs associated with running an outdated on-premise hardware would have creeped up and the alternative way of upgrading the existing on-prem infrastructure to the latest hardware models and then hosting the Tableau Server application on top of it would have taken months.