5 Best Practices for Tableau & Hadoop

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Learn how to leverage your big data platforms with fast analytics in Tableau.

Tableau was designed to facilitate real-time conversations with data across multiple data platforms. Business users who have felt stymied by traditional tools have flocked to this modus operandi. So what happens when queries return in hours or minutes rather than seconds? Can their ‘flow’ be maintained?

We are in an age where people can analyze millions or even billions of rows of data at their fingertips yet a user’s expectations is that they have near instantaneous results. When a user’s interactions and response times take more than 2-3 seconds, they become distracted from being “in the flow of visual analysis.” Thus, it is imperative to provide fast query speeds to keep users engaged so that they can gain more insight from their Big Data deployments.

Users can apply a number of best practices to maximize the performance of their Tableau visualizations and dashboards built on Big Data platforms. The best practices largely fall into the following five activities:

  1. Leverage A Fast Interactive Query Engine
  2. Strategically Utilize Live Connections Vs. Extracts
  3. Curate Your Data From The Data Lake
  4. Optimize Your Extracts
  5. Customize Your Connection Performance
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Tableau’s solution for Hadoop is elegant and performs very well. This obviates the need for us to move huge log data into a relational store before analyzing it. This makes the whole process seamless and efficient.

About Tableau

Tableau Software helps people see and understand data. Tableau helps anyone quickly analyze, visualize and share information. More than 46,000 customer accounts get rapid results with Tableau in the office and on-the-go. And tens of thousands of people use Tableau Public to share data in their blogs and websites.

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