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A Guide to Scaling Tableau Server for Self-Service Analytics

Neelesh Kamkolkar, Chef de produit chez Tableau Software

Business intelligence is a mission-critical function for the modern organization. The more accessible your organization makes self-service analytics, the more users in your organization will be empowered to make informed business decisions.

Teams rely on rapid-fire analytics to make decisions that matter right now not in hours or days. This reliance on data requires systems that are fast and easy to configure to meet the availability needs of the enterprise.

This paper provides an overview and recommendations for deploying Tableau Server 9.0 at scale. It includes example deployment scenarios and planning considerations to help you ensure that you size your deployment for success. The recommendations covered in this paper are informed by scale testing we have performed and published in the more extensive Tableau Server 9.0 Scalability: Powering Self-Service Analytics at Scale white paper.

We've also pulled out the first several pages of the whitepaper for you to read. Download the PDF on the right to read the rest.

Why self-service analytics at scale?

The more access that your organization provides to self-service analytics, the more users in your organization can make informed business decisions.

This concept is illustrated in Figure 1. The origin of the horizontal axis is an organization where data and analytics are created and managed in a traditional BI report factory. At the furthest end of the horizontal axis, users are empowered. They rely on self-service analytics to understand, view, and pivot data as primary input when they make business decisions.

The vertical axis shows the range of data access and availability within the organization. When you restrict access to data, keeping it under centralized control, your organization cannot realize the potential of self service at scale.

Users must have access to relevant and timely data to support real-time analytic business decisions.

Conversely, if you open up data without any controls, shadow IT might become the norm and bring with it a destabilizing culture of data anarchy.

A successful self-service analytical culture is a balance of managed data access and empowered business users. Tableau Server provides secure, scalable access to your business data. Tableau Desktop provides the powerful analytic client experience that your employees can use to evaluate, pivot, interrogate, visualize, and share data.

Land and expand

When you think about scaling a Tableau Server deployment, it’s useful to understand how Tableau often grows through an organization. We call this the “land and expand” phenomenon. A “land” is an entry point where someone in an organization discovers Tableau and downloads a single desktop or champions a Tableau Server installation for a pioneer group.

Using Tableau Server combined with larger Tableau Desktop penetration (the “expand”), more groups share content securely and easily without needing a lot of help from IT.

As use expands, IT engages to support the business in deploying, maintaining, and supporting the Tableau user community.

In addition, Tableau Drive provides a methodology to allow larger teams to successfully adopt Tableau with Agile best practices in mind. As customers grow, they often invest in Tableau Server to support their user community at a larger scale, globally.

Figure 2 shows how Tableau can scale up in an organization to meet the business requirements for self-service analytics, while effectively managing data access.

Want to read more? Download the rest of the whitepaper!

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À propos de l'auteur


Neelesh Kamkolkar

Chef de produit chez Tableau Software

Chef de produit chez Tableau, Neelesh Kamkolkar est en charge des aspects de planification et d'exécution, en particulier pour les clients professionnels. Chez Tableau, il est donc chargé de traduire les attentes des clients et des partenaires pour élaborer la vision stratégique des fonctionnalités professionnelles vNext et leur exécution. Avant de rejoindre Tableau, il a occupé diverses fonctions en gestion de produits et en ingénierie chez Doyenz, Microsoft, Hewlett Packard et Mercury Interactive.