Spotfire Ideas Portal
Spotfire 15.1 advances the way people investigate complex data, with AI that works within the analytical experience rather than alongside it. Ask Spotfire can turn natural-language requests into interactive visualizations and use specialized agents to carry out analytical tasks. Reusable Skills help teams capture workflows and apply them to new data.
Beyond AI, the release makes daily analysis easier, expands semiconductor yield investigation and energy workflows, and provides more flexibility for enterprise automation, connectivity, and custom development.
Together, these improvements help engineers, analysts, and organizations move from data to a focused investigation with greater continuity between analytical questions, visual exploration, and repeatable work.
Spotfire AI helps users move from asking questions to exploring answers in the same analytical environment. Ask Spotfire can create and enhance interactive visualizations, Insight Agents can handle specialist tasks, and Skills capture useful analytical workflows for reuse. Connections to external tools and administrator controls extend these experiences while keeping organizations in charge of how AI is used.
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Building an investigation usually involves translating questions into a sequence of visualizations, calculations, and selections. In Spotfire 15.1, Ask Spotfire interprets natural-language requests and creates or enhances analytical views directly in the analysis.
For example, a yield engineer can ask for wafer yield by lot, drill down into wafer maps, and identify the lowest-yielding wafers. Ask Spotfire can assemble the requested views and interact with specialist agents when the task calls for additional operations. Users remain able to explore and refine the result using Spotfire's interactive analysis capabilities.
Based on a prompt like "Yield per wafer per lot with drill down to wafer map trellised by wafer" and "Mark the 8 wafers with lowest yield" the AI assistant builds the requested visualizations, including the wafer map details visualization.
The AI assistant can also enhance existing visualizations. In this case with the prompt: "Add a band that shows +10% and -10% of target".
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Valuable analytical work is often repeated across datasets, projects, or teams. Skills let users capture instructions for multi-step analytical workflows and apply that approach again through Ask Spotfire.
A Skill can guide the assistant through data preparation, the creation of visualizations, and an initial investigation of patterns or outliers. Users can create a Skill from work they have already performed, refine its instructions, and save it in the analysis or Spotfire Library for subsequent use. Instead of rebuilding the same investigation each time, teams can start from a repeatable analytical approach and focus on the results.
Perhaps the easiest way to create a skill is to just build what you need in Spotfire and then ask the AI assistant to turn it to a reusable skill. The skill can be saved to the library and pinned to make it available in other analyses. And just by writing the name of the skill or directly referring to it the skill will run on the new data.
In this case AI placed the visualizations side by side. If one prefers a specific layout one can request that in the skill.
By asking the AI assistant to print the skill, it is possible to review and ask for further updates if needed.
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Some analytical tasks require more than general language instructions. Insight Agents provide specialized operations that Ask Spotfire or a Skill can invoke as part of an investigation.
Agents support tasks such as preparing wafer data, comparing categories, recommending related data, or creating analytical content. Users can call them directly or allow a Skill to use them within a broader workflow. Spotfire-developed agents are available through the add-on browser, making specialists capabilities available when they are needed in an analysis.
If you need to automate or help users with specific tasks, such as automating a part of an analytical workflow or data preparation step you can build your own Insight Agent and share and distribute it in your organization.
Building an Insight Agent is essentially like developing an Action Mod, with APIs to make requests and receive responses from AI models, modify and read the Spotfire analysis and data, make calculations and call data functions, make use of MCP etc. Insight Agents are able to run long-running calculations on a background thread to let the user go on with her work in parallel, and the agent can then present results and suggest actions.
Spotfire AnalyticsSpotfire Industry ProSpotfire Enterprise
Investigations can depend on information and actions outside the analytical application. In Spotfire 15.1, Ask Spotfire, Skills, and Insight Agents can use Model Context Protocol (MCP) connections to interact with compatible external systems.
An AI workflow could, for example, retrieve information from another service or create a follow-up ticket in a connected business system.
Administrators configure MCP connections and manage access by user group, helping organizations extend their analytical workflows in a controlled way.
Spotfire Enterprise
Organizations need control over which AI services and external connections can be used in enterprise analysis. Spotfire combines central AI administration with access controls that support organizational policies. Spotfire 15.0 introduced administration of approved language-model services, group access, and usage visibility.
Spotfire 15.1 extends those controls to MCP connections, which administrators can configure and make available to selected users and groups.
Everyday analysis depends on finding the right data, shaping it efficiently, and choosing visualizations that reveal what matters. Spotfire 15.1 improves discovery and authoring while extending the range of ways users can explore sensitivity, proportions, trends, and events over time. The result is less friction between a question and the visual evidence needed to investigate it.
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Analysts often need additional data to deepen an investigation, but finding the right dataset can mean manually searching through the Spotfire Library. The Data recommender agent helps analysts discover relevant Information Links based on the data already loaded in their analysis.
Recommendations appear directly in the insight panel, making it easy to evaluate and add complementary data as the investigation evolves:
Discover Information Links related to data already loaded in the analysis.
Review Information Links descriptions before deciding whether to add the data.
Add a recommended Information Link as a new data table with a single click.
Use existing relationships between Information Links in the Spotfire Library to drive recommendations.
Available as a Spotfire Insight Agent add-on for Spotfire Analytics and Spotfire Industry Pro 15.0 and later.
Spotfire AnalyticsSpotfire Industry Pro
Color schemes can be managed directly from the visualization properties panel across all plot types, including wafer map visualization.
The updated workflow makes it easier to apply, reuse, and manage color styling at the appropriate scope:
Apply a default color scheme, a color scheme stored in the current analysis, a color scheme from a local file, or a color scheme stored in the Spotfire Library.
Preview available color schemes alongside their names before applying them.
Save a color configuration locally as a file, within the current analysis as a document color scheme, or in the Spotfire Library for broader reuse.
Rename or remove document color schemes directly within the analysis.
Spotfire AnalyticsSpotfire Industry Pro
Preparing wide data tables often involves repeating the same data-type and aggregation changes across many columns. In Spotfire 15.1, analysts can select multiple columns in the Data in Analysis flyout and update these settings together, reducing repetitive work.
Select multiple columns simultaneously within the Data in Analysis flyout
Modify data types across selected fields in a single operation
Set preferred aggregation methods for multiple columns at once
Prevent configuration conflicts using real-time inline validation
Spotfire AnalyticsSpotfire Industry Pro
Understanding which variables have the greatest impact on an outcome is a common analytical task, particularly when exploring sensitivity, uncertainty, or alternative scenarios. The tornado chart visualization provides a clear way to compare these effects and identify the variables that matter most.
The visualization ranks variables by the size of their impact, creating the characteristic tornado shape and making the strongest influences easy to identify:
Compare the potential positive and negative impact of each variable on an outcome.
Rank variables by their overall impact to quickly identify the most influential factors.
Explore sensitivity across measures such as cost, revenue, performance, or other analytical outcomes.
Define a baseline value to show impacts relative to a specific reference point, or use zero as the baseline when no value is specified.
Use color to distinguish the direction of impact and make results easier to interpret.
Available as a Spotfire visualization add-on for Spotfire Analytics and Spotfire Industry Pro 14.6 LTS and later.
Spotfire AnalyticsSpotfire Industry Pro
Part-to-whole relationships can be difficult to communicate clearly across several categories. The waffle chart provides an intuitive, cell-based view of proportions and distributions. The visualization combines the familiarity of bar charts with flexible cell-based encoding:
Display cells horizontally or vertically, with stacked, side-by-side, and 100% stacked layouts.
Represent values using a selection of predefined icons.
Use different icons across categories when additional visual differentiation is useful.
Show partially filled cells to represent values more precisely when they do not align with complete cells.
Color individual cells using a continuous axis to reveal gradients and variation within the displayed values.
Configure the number and spacing of cells to control how values are represented.
Available as a Spotfire visualization add-on for Spotfire Analytics and Spotfire Industry Pro 14.6 LTS and later.
Spotfire AnalyticsSpotfire Industry Pro
Identifying shifts, differences in variability, and emerging patterns can be difficult to recognize when examping distributions separately. The ridgeline plot displays multiple overlapping density curves along a shared axis, making it easier to compare how the distribution of a numerical variable changes across categories, groups, or time periods. The visualization enables users to:
Compare distributions across categories, such as wells, reservoirs, equipment, or production groups.
identify patterns and differences in the shape, spread, and peaks of distributions.
Explore changes over time by comparing distributions across successive time periods.
Analyze lare numbers of groups in a compact, visually intuitive layout.
Particularly valuable in oil & gas, the ridgeline plot supports well log analysis, reservoir characterization, and production surveillance, helping engineers compare geological properties and production behavior across multiple well. Beyond energy, it is equally applicable to manufacturing, quality analysis, and other domains where understanding variations across populations is essential.
Available as a Spotfire visualization add-on for Spotfire Analytics and Spotfire Industry Pro 14.6 LTS and later.
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Area charts are useful for showing how values evolve across a continuous dimension while emphasizing the magnitude of change. The updated area chart provides greater control over how data is displayed and brings the visualization in line with the configuration experience of newer Spotfire visualizations.
The new version expands the chart’s analytical and formatting capabilities:
Configure axis label rotation and control the maximum number of labels displayed.
Define custom scale ranges to focus the visualization on the most relevant values.
Use logarithmic scales when analyzing data that spans a wide range.
Trellis the visualization to compare the same measure across multiple categories or groups.
Use zoom sliders to explore selected portions of the data in greater detail.
Available as a Spotfire visualization add-on for Spotfire Analytics and Spotfire Industry Pro 14.6 LTS and later.
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Exploring large collections of events over time requires both an accurate representation of temporal relationships and efficient ways to navigate, search, and investigate individual events. The updated timeline visualization provides a more flexible way to explore events across both short and long time ranges:
Explore timelines with more than 2,000 events, even across long time ranges.
View events at their actual distance in time, with a scale that adjusts from years to seconds as users zoom.
Represent event durations with an optional end-time axis.
Find and inspect events through search, selection, and a dedicated details panel.
Navigate and select time periods interactively, with flexible horizontal or vertical layouts.
Available as a Spotfire visualization add-on for Spotfire Analytics and Spotfire Industry Pro 14.6 LTS and later.
Spotfire 15.1 brings more engineering workflows directly into Spotfire Industry Pro. New AI assistance supports statistical comparisons and time-series preparation, semiconductor teams gain richer wafer investigations, and energy teams benefit from more flexible well-log analysis and ready-to-run upstream workflows.
Spotfire Industry Pro
Comparing groups is often an early step in an investigation, but reaching a reliable conclusion requires selecting an appropriate statistical test and checking its assumptions. The Analyze category profiles agent helps users perform category comparisons directly from their analysis.
The agent evaluates the selected data, chooses an appropriate statistical method, and explains the resulting differences in the context of the investigation:
Compare two or more categories, or compare an individual category against all other categories automatically from selected data in a box plot.
Select statistically appropriate tests based on the characteristics of the data and validate their assumptions before reporting results.
Adapt the analysis when assumptions are violated, for example by selecting an appropriate non-parametric test for non-normal data.
Identify cases where sample sizes or data characteristics do not support a valid comparison instead of returning a potentially misleading result.
Report statistical significance together with effect size, direction, and magnitude to help distinguish detectable differences from practically meaningful ones.
Explain why a particular statistical test was selected and provide guidance for interpreting both the result and its associated visualization.
The capability can support investigations across a wide range of analytical scenarios, from comparing experimental groups, operating conditions, or production lines to more specialized industry workflows. In semiconductor manufacturing, for example, engineers can use it to determine whether a chamber, tool, recipe, lot, test, or other category behaves differently from the rest.
Available as a Spotfire Insight Agent add-on for Spotfire Industry Pro 15.0 and later.
Spotfire Industry Pro
Time-series data loaded into Spotfire can come from different sources, tables, and sampling schedules, making it difficult to know whether the series are sufficiently complete and aligned for analysis. The time-series data cleansing agent helps users assess and prepare time-series data already available in an analysis before comparing, overlaying, or modeling multiple signals.
The agent examines the time-series data relevant to the investigation and identifies common preparation issues:
Automatically detect time-series data in an analysis across different data sources.
Work with both tall and wide time-series datasets.
Summarize the time range, sampling frequency, and missing values for each series.
Identify gaps and irregularities in the available observations.
Detect differences in sampling frequency or temporal alignment between series.
Recommend appropriate resampling approaches to bring related series onto a common time interval.
Reason across multiple data tables when the series needed for an investigation are stored separately.
Guide users through available actions and data functions to prepare and align the data for further analysis.
The agent is designed for time-series data already loaded into Spotfire from files, databases, exports, or multiple sources, particularly when the data needs validation or alignment before analysis. By identifying gaps and sampling differences early, it helps users establish a consistent analytical dataset before comparing signals or applying more advanced time-series methods.
Upcoming Spotfire Insight Agent add-on for Spotfire Industry Pro 15.1 and later.
Spotfire Industry Pro
Spatial patterns across a wafer can provide important clues about process, equipment, lithography, or handling issues, but distinguishing systematic signatures from random variation often requires experienced visual interpretation. The Detect wafer patterns agent helps semiconductor engineers identify meaningful spatial patterns in wafer data and characterize the type of signature present.
The agent analyzes die-level wafer data to identify structured spatial behavior that may indicate common failure signatures:
Detect edge effects, including failure rings and patterns concentrated around the wafer perimeter.
Identify radial gradients where yield or other measurements change progressively from the center toward the edge.
Detect concentrated center hotspots.
Find localized clusters of abnormal or failing dies.
Recognize repeating stepper-field signatures that may align with exposure or reticle geometry.
Identify linear or curved scratch-like structures associated with spatially correlated failures.
By surfacing recognizable spatial signatures directly within the wafer investigation workflow, the agent helps engineers move more quickly from observing an unusual wafer map to understanding the structure of the pattern and deciding where to investigate next.
Upcoming Spotfire Insight Agent add-on for Spotfire Industry Pro 15.1 and later.
Spotfire Industry Pro
Investigating wafer failures often requires semiconductor engineers to correlate failing wafers with equipment and process history across multiple manufacturing steps. The Equipment commonality analysis agent accelerates these investigations by automatically identifying equipment and process conditions disproportionately associated with wafer failures.
Starting from wafers marked in a visualization, the agent compares the failure population against a baseline cohort to identify and rank potential contributors to yield loss or process variation, helping engineers:
Identify equipment commonalities across failing wafers, including tools, chambers, recipes, and process steps.
Compare failure populations against a baseline to distinguish conditions disproportionately represented among failing wafers.
Rank potential contributors using prevalence, uplift, statistical separation, and supporting evidence.
Investigate linked manufacturing history using wafer identifiers shared across process and equipment data.
Work with flexible data structures, supporting equipment history stored in a single or multiple tables, with tall and wide layouts.
Eliminate repetitive manual analysis, reducing the need for complex queries and repeated cross-tabulations.
By connecting wafer-lever failures investigations with manufacturing history, Spotfire helps yield, process, and equipment engineers narrow down potential root causes, prioritize follow-up investigations, and accelerate the resolution of manufacturing issues.
Available as a Spotfire Insight Agent add-on for Spotfire Industry Pro 15.0 and later.
Spotfire Industry Pro
Wafer map analysis often depends on having a predefined wafer configuration, even when the underlying dataset already contains enough die-coordinate information to approximate the wafer layout. Spotfire's assisted wafer configuration automatically infers likely wafer layouts from semiconductor data so engineers can begin wafer-centric analysis without first creating a configuration manually.
Spotfire uses the available data to generate an initial wafer layout while keeping engineers in control of reviewing and refining the result:
Detect wafer-like data and infer an initial layout from die coordinates.
Display the inferred layout directly in a wafer map.
Review and refine the proposed configuration from the visualization properties panel.
Inspect inferred values, assumptions, and limitations, including incomplete or inconsistent inputs.
Save a refined layout as a managed wafer configuration when needed.
Assisted wafer configuration reduces the setup required to begin wafer analysis while preserving transparency and control over the resulting layout. Engineers can start exploring wafer patterns immediately, validate the inferred configuration as part of their workflow, and formalize it only when necessary.
Spotfire Industry Pro
When investigating large collections of wafers, spatial patterns alone do not always provide enough context to determine which wafers require attention first. Statistics layers bring wafer-level metrics directly into wafer map visualizations, making it easier to combine spatial patterns with key analytical indicators in the same view.
Statistics can be displayed alongside individual or trellised wafer maps and update dynamically as the investigation changes:
Display metrics such as yield, defect counts, bin distributions, metrology statistics, or process indicators directly with each wafer map.
Show multiple statistics simultaneously to provide richer context for each wafer.
Use built-in calculations or define custom metrics with expressions and data from the current or related data tables.
Configure the placement, alignment, size, padding, and layout of displayed statistics.
Keep statistics synchronized with filtering, marking, and data limiting as users interact with the analysis.
By combining spatial wafer patterns with relevant summary metrics, statistics layers help engineers compare wafers more efficiently, identify anomalies, and prioritize deeper investigation without switching between multiple visualizations.
Spotfire Industry Pro
Many semiconductor manufacturing patterns are best understood not only at the die or wafer level, but also in relation to the exposure fields created during photolithography. Wafer map visualizations can now represent exposure field layouts, giving engineers the reticle-level context needed to investigate whether patterns repeat between fields or vary across the wafer.
Exposure fields are integrated directly into the wafer map experience:
Define exposure field properties as part of a wafer configuration.
Display exposure fields as a dedicated layer within wafer map visualizations.
Visualize exposure field layouts alongside wafer outlines and die grids.
Show exposure fields independently of the die grid when only reticle-level context is needed.
Configure the visibility and appearance of the exposure field layer.
Adding exposure field information helps engineers relate yield, defect, metrology, and process patterns to the photolithography layout. This makes it easier to recognize field-related signatures, distinguish repeating reticle effects from broader wafer-level variation, and narrow the search for potential root causes.
Spotfire Industry Pro
Comparing two independent groups is a common analytical task, but a standard t-test may not be appropriate when the data does not meet its assumptions. The Mann-Whitney U test is available directly in the Spotfire expression language, providing a rank-based, non-parametric method for comparing two independent samples.
The new functions make Mann-Whitney testing available wherever Spotfire expressions can be used:
Calculate the Mann-Whitney U statistic with MannWhitneyUTest and its p-value with MannWhitneyUTestP.
Use the functions in calculated columns and custom expressions.
Build reusable, non-parametric group-comparison workflows alongside other statistical calculations.
The Mann-Whitney U test is useful across many analytical scenarios where two groups need to be compared without assuming normally distributed data. For example, manufacturing engineers can compare measurements from different tools or chambers, assess wafers before and after a process change, or investigate differences between production lots directly within their Spotfire analysis.
Spotfire Industry Pro
Well log analysis often brings together continuous logs, categorical intervals, formations, completion information, and multiple depth references that may originate from different data tables. The updated well log chart introduces a layer-based architecture that makes these different sources and representations easier to combine and configure within a single visualization.
Each track is now represented by an independently configured layer, providing greater flexibility in how well data is assembled and displayed:
Combine continuous logs, categorical intervals, formations, and completion information from different data tables.
Configure and reorder tracks as independent layers, using native Spotfire color schemes.
Represent intervals with explicit bottom depths or dedicated background layers.
Display multi-channel measurements as depth-oriented heatmaps.
Add equipment and completion schematics alongside the logs.
Compare alternate depth references, including true vertical and measured depth.
The layer-based design makes the well log chart more flexible for complex subsurface workflows, allowing different types of well information to be combined without first restructuring them into a single table. It also provides a foundation for richer log, completion, and depth-based analysis.
Available as a Spotfire visualization add-on for Spotfire Industry Pro 14.8 and later.
Spotfire Industry Pro
Reservoir engineers and business development teams evaluating an acquisition work to short deadlines against data that arrives from unrelated sources. Well headers, production history, ownership interests and commodity price assumptions each live in a different system, and consolidating them consumes much of the time available before bids close. The Upstream asset valuation accelerator, available on the Spotfire Community Exchange, provides a ready-to-run workflow from loaded well data through to a priced asset valuation.
This accelerator provides a complete valuation workflow without custom application development.
Load well headers by filtered query, pasted API list, or local file, with column mapping into the internal production schema
Fit Arps decline curves across the selected well set as a data operation, with configurable peak detection, production weighting and abandonment rate
Correct individual wells by refitting from a selected point on the production chart or by entering curve parameters directly
Generate type curves normalized to lateral length and apply them to wells without sufficient production history
Schedule undeveloped locations and evaluate economics against reusable pricing and cost cases
Asset teams can now screen a large well population and reach a defensible valuation inside the deal window, rather than assembling the workflow from separate tools at each stage.
Upcoming Spotfire Accelerator for Spotfire Industry Pro 15.1 and later.
Spotfire Industry Pro
The undeveloped portion of an acreage position frequently represents the majority of its value, yet the count of remaining drilling locations is more often estimated than calculated. Spacing units are maintained in land and GIS systems, well data arrives separately, and the spacing assumption behind the result is rarely recorded alongside it. The Well inventory accelerator, available on the Spotfire Community Exchange, estimates remaining drilling locations using existing or modified drilling spacing unit shapefiles.
This accelerator makes the location count reproducible, with each of its inputs visible and adjustable.
Load wells by basin and play or by pasted API list, with filtering to a working subset
Load drilling spacing units from existing shapefiles into a processed layer for selection and appending
Modify unit boundaries using a generated grid with adjustable resolution and buffer distance
Define inventory intervals through aliasing, and specify density for each categorical and reservoir combination
Generate locations across selected units, review summary statistics against existing wells, and export to shapefile or CSV
When a spacing assumption changes, planners can recalculate the location count using visible, adjustable inputs.
Upcoming Spotfire Accelerator for Spotfire Industry Pro 15.1 and later.
Spotfire Industry Pro
Mineral and royalty owners receive payment statements they have little practical means of verifying. Deductions arrive without context, ownership shares depend on how much of each wellbore crosses a given tract, and activity on neighboring acreage is typically discovered after the fact. The Minerals and royalties accelerator, available on the Spotfire Community Exchange, combines payment statements, ownership geometry, well lists, and activity data in one ready-to-run analysis.
This accelerator supports ownership resolution, revenue assurance and activity monitoring within one governed workflow.
Ingest check stub data with dedicated column mapping alongside a master well list and tract and unit shapefiles
Calculate gross acreage overlap between tracts and units, deriving net royalty acres and net revenue interest
Determine implied ownership from the footage of each wellbore inside a shapefile, rather than from a surface or bottom-hole assignment
Identify deduction anomalies by description and amount across a configurable trailing window
Compare realized pricing per payor against a benchmark, and map rig, permit and completion activity within a distance buffer
Owners and land analysts can now establish what they hold, confirm whether payments are consistent with the wider market, and see what is being permitted or drilled around their position.
Upcoming Spotfire Accelerator for Spotfire Industry Pro 15.1 and later.
Spotfire 15.1 helps teams maintain and automate enterprise analytics with less manual effort. Easier upgrade assessments, more flexible Automation Services workflows, and improved administration tools simplify day-to-day operations.
Spotfire Enterprise
Planning an upgrade can require administrators to piece together changes from several Spotfire releases and service packs. The new interactive release note page consolidates changes between two selected versions so you can select a starting and ending release and generate a consolidated table of the intervening release notes, making it easier to assess upgrade impact and identify relevant changes, new features, closed issues, and more.
Coverage: from Spotfire 12.0.0 LTS to the latest published release, including service packs and innovation releases.
Spotfire Enterprise
Automation jobs sometimes need different paths, exits, and notification details depending on the data or execution conditions. Spotfire 15.1 extends Automation Services with more expressive job logic and configurable messaging.
The changes help authors build and operate jobs with fewer workarounds:
Use If and Else tasks, and Break or Exit behavior in conditional or ForEach flows.
Nest conditional and loop blocks to express more involved job logic.
Set a Reply-To address so recipients can respond to the report owner rather than the sending service account.
Insert custom-formatted date and time values in automated messages.
Record user actions when jobs are executed locally.
These capabilities simplify job maintenance and make automated output more useful to recipients.
Spotfire Enterprise
Server administration is easier when routine changes and checks can be performed without unnecessary dependencies. Spotfire 15.1 improves several command-line tools used to inspect preferences, manage rules, and find analysis references.
Administrators can:
List explicitly configured preferences and filter the results using list-preferences.
Import, export, and copy rules directly through the database without requiring a running Spotfire Server or keystore file.
Find analysis URLs referenced by WMS and TMS map layers.
These updates reduce operational friction in scripting, administration, and upgrade preparation.
Spotfire 15.1 expands the ways developers can connect analytical experiences to custom workflows. A public wafer map API makes semiconductor visualizations accessible to scripts, actions, and AI-assisted solutions, while interactive Python data-function debugging helps authors troubleshoot with the data used in their analyses. Together with the Insight Agent development framework, these capabilities support more tailored analytical applications.
Spotfire Industry Pro
Custom semiconductor workflows often need to interact with wafer maps through actions, scripts, AI-assisted analysis, and other extensions, but this previously required relying primarily on the visualization’s user interface. A public API for the wafer map visualization is introduced, providing a supported way for developers to integrate wafer-centric analytics into custom and automated workflows.
The API exposes wafer map content and state so other Spotfire capabilities and extensions can work with the visualization programmatically:
Create, modify, and configure wafer map visualizations programmatically.
Access wafer, die, layer, and configuration information from the current visualization.
Read active selections and markings to incorporate user context into custom workflows.
Control wafer map navigation and visualization state.
Integrate wafer maps with Actions, custom scripts, and other application extensions.
Build AI-assisted workflows that can inspect and interact with wafer-centric analysis.
Spotfire AnalyticsSpotfire Industry Pro
Troubleshooting Python data functions can be difficult when issues only appear with the actual data and execution context used in an analysis. The new Data Function Debugging Extension on the Spotfire Community Exchange connects running Python data functions to the VS Code Python Debugger, allowing authors to investigate and correct scripts interactively.
The extension brings standard Python debugging workflows into Spotfire data function development:
Connect running Python data functions to the VS Code Python Debugger.
Set breakpoints, step through code, and inspect variables during execution.
Debug scripts using the actual input data from the analysis.
Edit scripts in VS Code between runs to test changes quickly.
Optionally save corrected scripts back into the Spotfire analysis once the changes are validated.
Authors can isolate issues and verify fixes against real analysis data before saving changes back to Spotfire.
Spotfire 15.1 makes it easier to connect to critical enterprise data sources and keep analytical environments aligned with evolving technology requirements. Enhanced AVEVA PI connectivity improves access to operational time-series data, while updated SAP HANA authentication and proxy support strengthen enterprise integration. Support for newer R, Ubuntu, and Java versions helps organizations maintain modern, compatible analytical environments.
Spotfire Industry Pro
Working with operational time-series data often requires analysts to control how values are sampled while ensuring that large data requests remain efficient. The AVEVA PI connector adds new authoring, interpolation, and data-retrieval capabilities that make it easier to work with PI data across Spotfire clients.
The updated connector provides greater flexibility when creating connections and retrieving time-series data:
Author AVEVA PI connections directly in the Spotfire web client.
Retrieve interpolated, regularly spaced time-series values using specified intervals such as 1m or 1h.
Control the sampling interval to obtain regularly spaced time-series data suited to the analysis.
Allow administrators to configure how data is fetched from the PI server to optimize performance for larger queries.
Spotfire AnalyticsSpotfire Industry Pro
Enterprise SAP HANA deployments often rely on centralized identity providers and controlled network access, requiring connectors to work with modern authentication and proxy configurations. The SAP HANA connector adds support for OAuth2 identity provider authentication and proxy-based connections.
The updated connector provides greater flexibility for secure enterprise deployments:
Authenticate to SAP HANA using OAuth2 identity providers for permission-based access.
Route SAP HANA connection traffic through proxy servers when required by the network environment.
Configure proxy behavior using custom connection parameters.
Store proxy credentials in user preferences for reuse across connections.
Spotfire AnalyticsSpotfire Industry ProSpotfire Enterprise
R users need access to current language versions to keep analytical workflows aligned with evolving packages and development environments. The Spotfire service for R adds support for R 4.6.
The updated runtime support enables users to:
Run Spotfire data functions using R 4.6.
Develop and maintain R-based analytical workflows against a more recent R runtime.
Use R packages and capabilities that require or benefit from R 4.6.
Spotfire Enterprise
Support for Ubuntu 26.04 LTS as operating system.
Support for Eclipse Temurin JDK 25 LTS.
For the complete list of supported platforms, operating systems, browsers, databases, and third-party software, see the Spotfire System Requirements documentation.