How can performance be optimized when working with large datasets in Tableau?

Using data extracts and filtering out unnecessary data is a highly effective strategy for optimizing performance when working with large datasets in Tableau. Data extracts are a snapshot of your data at a particular point in time, which enables faster querying and rendering of visualizations compared to live connections that query the database directly. This can be particularly beneficial when dealing with large datasets that could slow down performance during interactions or visual rendering.

By filtering out unnecessary data during the extract process, you reduce the volume of data that Tableau needs to handle, which in turn minimizes load times and increases responsiveness. This filtering can be based on criteria such as date ranges, specific categories, or any other parameter that is relevant to your analysis, thus ensuring that only the most relevant data is loaded into Tableau.

This approach allows Tableau to function more efficiently, leading to a smoother user experience and quicker insights, particularly when dealing with complex analyses or dashboard interactions. In contrast, alternatives like real-time data connections can lead to slower performance due to continuous queries on large databases, increasing the chances of lag. Additionally, simply increasing the number of visualizations without managing the data size may lead to clutter and reduce clarity without enhancing performance, while enhancing internet speed doesn't compensate for high data volume issues inherent in large datasets.