Spotfire Desktop is a data analysis and visualization tool that empowers the end user to freely analyze data without the need for a server. Running on the desktop, users can create interactive data visualizations and perform deep analysis on data sources immediately, without waiting for IT.
The desktop client is an advanced tool for the data analyst. It makes it easy to get started, just download it and install it on your desktop. With the Desktop client, you can instantly apply a variety of visualization and data analysis techniques on your data without programming. If you are a beginner, Spotfire Desktop is a great way to learn about visual data discovery as you explore your data. If you are a seasoned data scientist, Spotfire Desktop provides you an almost unlimited range of capabilities for visualization and statistics.
Visualize Your Data
Spotfire Desktop comes with the following data visualizations:
- Bar Chart
- Line Chart
- Tree Map
- Cross Table
- Combination Chart
- Pie Chart
- Scatter Plot
- 3D Scatter Plot
- Heat Map
- Parallel Coordinate Plot
- Summary Tables
- Map Chart
Spotfire Desktop can connect to the following data sources:
- Cloudera Hive
- Cloudera Impala
- HP Vertica
- IBM DB2
- IBM Netezza
- Microsoft SQL Server
- Microsoft SQL Server Analysis Services
- Oracle Essbase
- Oracle MySQL
- Pivotal Greenplum
- Pivotal HAWQ
- SAP HANA
- SAP NetWeaver Business Warehouse
- Teradata Aster
Supported file formats for import include:
- Microsoft Excel Workbooks (.xls, .xlsx, .xlsm)
- Comma-separated Values (.csv)
- Text (.txt)
- Microsoft Access Databases (.mdb, .mde)
- SAS Data Files (.sas7bdat, .sd2)
- Universal Data Link (.udl)
- Sfs-file (.sfs)
- ESRI Shape Files (.shp)
- Spotfire Formats
Advanced Analytic Functions
For the data scientist, Spotfire Desktop offers advanced capabilities to help anticipate emerging trends, take preemptive action, minimize risk, and make better decisions with much greater confidence.
Apply sophisticated data analysis techniques using interactive statistical functions:
- Data relationships identify correlations among large numbers of parameters.
- Line similarity determines distance measurements across data points to help identify similarities among a set of lines.
- K-means clustering partitions data into similar, clustered subsets.
- Hierarchical clustering organizes and manipulates large data sets based on distance and/or similarity.
- Box plots visualize key statistical measures, such as median, mean, and quartiles.
- The integrated R engine (TERR) lets the user write statistical functions and visualize their output in Spotfire.
- Advanced curve fitting summarizes and displays a collection of sample data on top of visualizations.
- Predictive modeling enables you to incorporate regression or classification modeling in your analysis.
- Spotfire Desktop is available for Windows.