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TIBCO Spotfire: A Comprehensive Primer

TIBCO Spotfire: A Comprehensive Primer

By : Berridge, Phillips
3.6 (8)
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TIBCO Spotfire: A Comprehensive Primer

TIBCO Spotfire: A Comprehensive Primer

3.6 (8)
By: Berridge, Phillips

Overview of this book

The need for agile business intelligence (BI) is growing daily, and TIBCO Spotfire® combines self-service features with essential enterprise governance and scaling capabilities to provide best-practice analytics solutions. Spotfire is easy and intuitive to use and is a rewarding environment for all BI users and analytics developers. Starting with data and visualization concepts, this book takes you on a journey through increasingly advanced topics to help you work toward becoming a professional analytics solution provider. Examples of analyzing real-world data are used to illustrate how to work with Spotfire. Once you've covered the AI-driven recommendations engine, you'll move on to understanding Spotfire's rich suite of visualizations and when, why and how you should use each of them. In later chapters, you'll work with location analytics, advanced analytics using TIBCO Enterprise Runtime for R®, how to decide whether to use in-database or in-memory analytics, and how to work with streaming (live) data in Spotfire. You'll also explore key product integrations that significantly enhance Spotfire's capabilities.This book will enable you to exploit the advantages of the Spotfire serve topology and learn how to make practical use of scheduling and routing rules. By the end of this book, you will have learned how to build and use powerful analytics dashboards and applications, perform spatial analytics, and be able to administer your Spotfire environment efficiently
Table of Contents (18 chapters)
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1
Section 1: Introducing Spotfire
6
Section 2: Spotfire In Depth
12
Section 3: Databases, Scripting, and Scaling Spotfire

Data relations (between tables)

A great feature of Spotfire is that it allows freeform exploration between related datasets. You can mark data in one dataset and have other visualizations built on a different data table respond. You can also filter in one data table and the filtering will map to any dependent data tables.

Later on in this chapter, we will be producing a parallel coordinate plot that enables us to look at how various columns interact within European consumer price index data. To assist with navigating through the data, it's useful if lists of regions and zones are created and related back to the retail price index data.

Two datasets are said to be related if they contain columns with the same values as each other. This is a key feature of relational databases and is known as a foreign-key relationship. Spotfire uses the concept of data table relations to represent...

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