What this blog covers:
- Cause of delayed insights.
- Tips that can improve tableau’s performance on AWS.
- How Kyvos accelerate Tableau’s performance on AWS.
â€śWhat do you like the most about Tableau?â€ť when this question was raised to most Tableau users, their popular opinion was Tableauâ€™s ability to create compelling and beautiful visualizations quickly and easily. It is a leading and intuitive data visualization tool. When combined with one of the most comprehensive cloud platforms, AWS, enables users to analyze datasets with speed and agility to drive better business outcomes quickly.
We are living in a fast-paced world where a delay of even a millisecond can become a cause of losing an audience. This means the performance of Tableau matters a lot. However, as the data volumes increase, cardinalities and the number of dimensions increase, and Tableau takes longer to fetch the results. The problem worsens when the data source and Tableau servers exist on different networks; the communication time between Tableau and the data source causes delayed responses. Another issue that slows down Tableau’s performance is higher analytical complexities. Due to these challenges, you may find it difficult to maintain SLAs for consuming and driving analytics.
Now the question is, how can you improve the performance of Tableau on AWS for cloud-scale data? In this blog, I will share some tips that can help you to optimize the performance of Tableau on AWS –
#1 Optimize Filters in Tableau
Filters are a valuable way to see the data you want to see. Tableau enables you to filter individual views or data sources based on dimensions and measures and highlight any underlying insights. You should use only relevant filters in a report. Irrelevant or redundant filters are considered more work for Tableau as it increases query response time. Optimization of filters is essential to improve the performance of Tableau.
To optimize the filters, firstly you should choose the correct filters. Use â€śinclude filtersâ€ť as they are quicker than â€śexclude filtersâ€ť. In the case of “exclude filter,” Tableau has to load the entire domain of the filtered field before even beginning the actual filtering. This takes more time to bring back the result of filtered data.
Secondly, use the “include all filter values” option. The “include only relevant values” option requires extra computational power because the list is recomputed every time a filter is changed.
#2 Improve Performance with Pre-aggregation of Data
Calculations in Tableau are performed based on users’ requests. If you are dealing with larger datasets to solve complex business problems, performing runtime computations slows down the performance of Tableau on the cloud. It is important to aggregate the data to the granular level required for analysis. But this kind of aggregation requires you to be clear on the level of aggregation needed, which is impossible to know in advance. Therefore, pre-aggregation of data becomes necessary if you don’t want to compromise on scale or performance.
A cloud-native solution like Kyvos uses its Smart OLAPâ„˘ technology to leverage the store and compute capacity of the AWS platform to perform pre-aggregation. Kyvos pre-aggregates data across multiple dimensions and measures and build massively scalable cubes without any data movement. These cubes are distributed across multiple nodes and are stored on AWS. You can now query trillions of rows of data using Tableau and get instant answers to all your business queries.
#3 Create a High-Performing Universal Semantic Layer
The primary cause of slow response times is raw table scans and high concurrency. Having a semantic layer between Tableau and cloud data platforms can reduce query response times from days to minutes.
With Kyvos, build a universal semantic layer between Tableau and AWS to eliminate the limitations of siloed reporting and get instant query responses. Kyvos enables you to model complex business requirements to uncover hidden insights. Instead of segregating business logic and calculations within Tableau, define all your metadata in one place and enable business users across the enterprise to access the single source of truth. Tableau’s powerful visualizations and Kyvos’ ability to handle trillions of rows of data form a powerhouse for faster and smarter analytics on massive data.
#4 Focus on Strategically Designing your Visualizations
When you have worked hard to create a visualization in Tableau, you don’t want it to take more time than usual to load. The first thought that crosses your mind while creating a visualization is to consolidate a lot of information into your visualization. Therefore, you try to add more sheets in the workbooks and more fields and calculations to the view. As a result, visualization takes longer to render than usual.
To improve the speed of your Tableau visualizations, strategically design your visualization, add only relevant fields and calculations, and add fewer sheets in your workbook. This will make visualization render faster in Tableau. Another strategy to improve Tableau visualizations is by limiting the number of marks. Marks are a crucial element for visual analysis in Tableau. It corresponds to a row or group of rows in your data source. However, it can be alluring to have a detailed visualization containing multiple different graphs, but on the other hand, it can increase the processing power to render the visualization. You can improve rendering times by limiting the number of marks.
These tips can help you improve Tableau’s performance on AWS.
Tableau on AWS – A Modern Approach to Faster Analytics
Today, we are in the midst of a data rush. According to research by IDC, data will increase to up to 175 ZB in 2025. The pandemic has also forced many organizations to move toward digital transformation. The goal of organizations today is to achieve analytics ubiquity by providing data access – to analysts to explore data more deeply, to executives who need a 360-degree view of their customers, and to decision-makers who are responsible for optimizing business processes and making data-driven decisions. All these business users are thriving for accuracy and efficiency. They need the correct data to make confident decisions.
A modern approach like Kyvos can enable organizations to unlock the power and flexibility of self-service analytics in the cloud. With Kyvos’ BI acceleration layer on the cloud, they can create a scalable BI environment on AWS and analyze as much data as needed using BI tools like Tableau. It also provides a nativeÂ three-tiered security architectureÂ that supports secure access, gives them confidence in the single source of truth, and breaks down silos across teams to build trusting and collaborative relationships.
Kyvos can be your path towards faster and smarter analytics while using Tableau on AWS. To know more about how Kyvos supercharges Tableau performance,Â click here.
As the data volumes, cardinalities, and dimensions increase, Tableau takes longer to fetch the query results on any cloud platform, including AWS. Another challenge is delayed responses caused by the communication time lag between Tableau and the data source. This happens when the data source and Tableau servers exist on different networks. Higher analytical complexities can also slow down Tableau’s performance.