Tableau Server 9.0 High Availability: Delivering Mission-Critical Analytics in the Flow

Overview | What you'll learn: 

Business intelligence is becoming a mission-critical function for the modern organization. Teams rely on rapid-fire analytics to make decisions that matter right now not in hours or days. This reliance on data demands a high degree of availability for the supporting systems, requiring systems that are fast and easy to configure to meet the availability needs of the business. In this whitepaper, you'll learn:
  • How Tableau Server offers high availability out of the box
  • How Tableau Server handles failover scenarios
  • Architectural considerations for a high-availability deployment of Tableau Server
  • How to select the best configuration for your business needs

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.

Self-Service Analytics is Mission-Critical

Today more than ever, self-service analytics and data-driven decision-making are becoming the norm in organizations worldwide. Users and decision makers have come to depend on immediate access to data and self-service tools to answer their questions in real time. Executives understand the importance of data-driven decisions at their companies, and rely on these systems daily. This reliance on data requires a high degree of availability to the underlying systems. Each platform’s capabilities need to be more accessible and easily configurable by organizations.

Tableau Server 9.0 delivers the future of mission-critical self-service analytics. It’s rapid-fire, enables an iterative approach to analytics, and empowers users to inform critical decisions. In this paper we will explore how Tableau Server 9.0 delivers self-service analytics at scale with high availability.

Understanding High Availability

A goal for highly-available systems is to minimize downtime of the system. Availability is commonly expressed as a number of nines, and measured as the percentage of actual uptime versus expected uptime. The table below shows concrete examples of how the number of nines corresponds to uptime.

System administrators often have service level agreements (SLA) with their business users to define an acceptable range of uptimes. Based on that SLA, they will choose deployment architectures to meet their goals. Most system administrators have planned downtime for maintenance, upgrades, and patching, and when an unexpected failure occurs, it’s referred to as unplanned downtime. Of course, administrators need to conduct planned maintenance for hardware or software updates; the goal is to minimize unplanned downtime.

We understand how important it is for users to readily see and understand their data. We also realize there will always be events that threaten the availability of business intelligence systems, whether these events are related to hardware, software, networks, or even people. In Tableau Server 9.0, new server processes help keep your system running in the event of component failure. A properly configured multi-node deployment uses these new processes for server high availability (HA). However, unlike most systems, Tableau makes it easy to set up and configure your analytics environment for HA.

Tableau Server Scalability

Tableau Server is architected to scale up and scale out. It provides large organizations with enterprise-class deployment flexibility while still retaining the simple and easy-to-use qualities that make it appealing for smaller teams. Depending on your environment, Tableau Server can run on one or more computers—and run one or more component processes on the same node—in order to best serve both your user demands and your HA requirements.

Tableau Server powers several cloud-scale solutions managed by Tableau IT. For instance, Tableau Public is a custom Tableau Server deployment that supports millions of views each week. As part of our engineering and release process, we deploy beta versions of Tableau Server software to Tableau Public to fine-tune stability and quality before we release to our corporate customers. Below is a point-in-time view of Tableau Public availability based on synthetic transactions run against the servers to measure availability.

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