Spotfire Analytics Flashcards

What is Spotfire's in-database (live query) mode and when should it be used?

Answer: A connection mode where Spotfire pushes all query computations to the source database, never loading data into Spotfire memory — used for very large datasets that exceed available RAM or for real-time data requirements

In-database mode (live connection) keeps all data in the source DBMS. Every filter interaction generates a new SQL query against the source, enabling analytics on datasets too large for in-memory loading while always reflecting current data.

What is a Spotfire Template (.sfs file) used for?

Answer: A reusable analysis structure containing visualisation layout, property controls, and branding without embedded data, providing a standardised starting point for new analyses connected to any data source

A Spotfire Template defines the visual structure and logic of an analysis (page layout, chart types, filters, calculated columns, action controls) without bound data, so analysts can connect their own data source to the template.

How does Spotfire's calculated column feature work?

Answer: A column defined by an expression (Spotfire expression language) that is computed on-the-fly from existing columns without modifying the source data or adding columns to the database

Calculated columns use Spotfire's expression language to derive new values per row from existing columns, evaluated in memory without changing the source data.

What is Spotfire's action control feature and what can it do?

Answer: Interactive UI elements (buttons, input fields, drop-downs) placed on the analysis canvas that execute scripts or property changes when the user interacts with them, enabling workflow-style analytical applications

Action controls are interactive elements analysts add to a Spotfire dashboard that, when clicked or changed, run IronPython or JavaScript scripts, set document properties, or navigate pages — enabling complex application-like analytical experiences.

How does TIBCO Spotfire handle large-scale deployments with many concurrent users?

Answer: Through a distributed Spotfire Server cluster with load balancing and a shared Library database, combined with configurable routing policies and auto-scaling Spotfire Web Player node farms

Spotfire scales horizontally through clustered server nodes behind a load balancer, a shared Library backend (database), and Web Player farms that handle rendering for browser-based users.

What is Spotfire's Recommendations feature and how does it assist analysts?

Answer: An AI-driven feature that automatically suggests relevant visualisation types, chart configurations, and data correlations based on the data loaded into the analysis, reducing time-to-insight for analysts

Spotfire's Recommendations engine uses heuristics and AI to suggest chart types, additional columns to include, potential filters, and statistical analyses relevant to the analyst's current data and exploration context.