What's New in Spotfire® 10.9

Spotfire 10.9 provides much awaited features such as shared dual scales, support for PostresSQL as Spotfire DB, in addition to a number of productivity improvements in the data canvas, improvements to the map chart and improvements when working with multiple data tables in one visualization.

For Analysts, Spotfire 10.9 thus enables more clear visualizations when visualizing data requiring two scales, and also when visualizing data from more than one data table in one visualization. In the data access area users will benefit from improvements to the Google Big Query, Oracle and Teradata connectors. A new data cleaning recommendation saves time when dealing with dirty data with extra whitespaces.

Administrators may find support for PostgresSQL and Microsoft SQL Server 2019 valuable, as well as Spotfire Server robustness improvements.

Developers get access to new APIs to configure shared dual scales, Python data functions and improvements to the signing of custom extensions.

Watch the 

.

Back to the What's new in Spotfire landing page.

Visual Analytics

Shared dual scales in combination chart, bar chart, line chart, and scatter plot

Spotfire visualizations that support multiple scales now also support shared dual scales. This means that you can create visualizations that display, for instance, 5 pressure values and 3 temperature values, using just two scales; one for pressure and one for temperature. This feature was a popular request from many Spotfire users. 

Scale on right or left side

It is now also possible to select the scale position for visualizations using just one scale. The scale can be positioned on the right or left side, depending on what is most suitable for the data or the type of analysis you want to make. This feature is available in Spotfire Analyst only.

Visualization label display values

It is now possible to use columns from secondary data tables as labels in visualizations, by using the new 'Display values' feature in the expanded Data in analysis flyout. This means, for example, that its possible to visualize data grouped by an ID column, but instead of displaying the technical ID:s as axis labels, show a more human-friendly value only from a column in a secondary table. This makes visualizing data from more than one data table more powerful and user friendly.

This is often handy when visualizing streaming data. Incoming messages typically include identifier-style information, such as a numeric sensor ID, but in a visualization one typically wants to use a human-friendly name like a descriptive name for the sensor (e.g "Tank pressure") instead of the technical ID. 

Default values for document properties

A default value is now set automatically when adding a non-string type document, table or column property.

This is useful when the initial value is not important and will be changed later, either manually or by a script, and is especially helpful for values with complex formats like Date, Time or TimeSpan.

This feature is available in Spotfire Analyst and through the API.

Option to not add all columns automatically when creating a new table

When you create data connections, you have a new performance option available which controls whether table visualizations should load all columns by default, or if users should select the columns to visualize manually.

This option is configured by navigating to Data > Manage data connections > right-click an existing data connection > Edit Data Connection > Performance Settings.

For connections embedded in Spotfire analyses, it's set by navigating to Data > Data connection properties > Settings > Performance Settings (see image above).

This performance option is useful, especially when using in-database data connections that join multiple tables in the underlying database. Adding all columns from such a table may result in an expensive join operation in the database.

Zoom visibility in Business Author

Web authors can now use the Zoom Visibility feature in the map chart like they are used to in the Analyst client.

Zoom visibility enables map chart authors to create geographical drill-downs and give maps a dynamic touch by drilling up and down through spatial data, to see more or less precise data aggregations as users interact with the map and zoom in to lower zoom levels.

Map chart legend based on visible layers 

The map chart now shows the legend only for visible layers.

If you have a map with multiple layers configured to be visible or not according to the zoom visibility property, the legend will show information only for the layers that are visible at the current zoom level. This makes it easier to find information about the data on the map and reduces the need to scroll through legend items, especially when there is a high number of layers configured in a map chart.

The file name is now visible when loading analysis in the web client

The file name of the loading file is now visible in the web client. This is helpful when loading analyses that are slow to load.

Data Wrangling

Data action recommendation ? Remove extra whitespaces

Previous versions of Spotfire have mostly focused on recommendations for new visualizations in the expanded Data in analysis flyout, with the exception of the Link data tables recommendation that created a relation between two similar data tables. Now, Link data tables have gotten company by a data wrangling recommendation, within the new concept of data action recommendations.

The data action recommendation concept is built on the Spotfire recommendation engine and the new recommendation informs you if you have unnecessary whitespace characters (blanks) within your categorical columns. The problem with these categories is that they are seen as separate categories. This becomes apparent, for example, when visualizing the column in a bar chart: with whitespace, there are simply too many categorical bars displayed.

But, with a few clicks, these additional whitespace characters are removed so that you can continue with the analysis.

The recommendation engine detects that the number of unique categorical values in a certain column will be reduced if a Trim transformation is applied. If you choose to apply it, leading and trailing whitespaces from all categorical values in a column will be removed automatically. It can also find extra whitespace characters between words in a string column.

The insight process is triggered for a categorical column when you select a column in the data in analysis flyout, with the recommendation panel expanded.

Image: A bar chart with too many categories.

Image: The recommendation is visible above the visualization recommendations.

Image: The result of the applied recommendation is visible in the visualization.

Image: As with all data wrangling operations, the added Trim transformation step can be edited or removed from the Data Canvas.

Tip: If a space character is available in the middle of a word, then that type of source error will not be found by the new recommendations. However, you can easily fix the error using the Group from marked function, available on right-click in the visualization.

Data canvas indication of which columns and rows were added

The data canvas makes it easy to see which added rows, added columns and calculated columns that are included in a Spotfire final data table. If you select one of these operations in the graphical view, and you are viewing the Data tab in the lower right part of the data canvas, you will now see that Spotfire automatically scrolls and highlights the selected operation's added rows, added columns or calculated columns, respectively. This makes it easier to navigate and review row or column level details.

When switching to the Data preview, the view automatically scrolls to where the new columns begin in the data table.

Add a new calculated column to the final data table, directly from the data canvas

This feature is available in Spotfire Analyst (Windows) and Spotfire Business Author (web).

Image:  Add a new calculated column to the final data table, by clicking its plus sign.

Add a new hierarchy to the final data table, directly from the data canvas

This feature is available in Spotfire Analyst.

Edit an existing hierarchy from the data canvas

This feature is available in Spotfire Analyst.

Data Access

Access Google BigQuery data sets without being a project member

When accessing Google BiqQuery from Spotfire, all data sets within the projects you are a member of are listed automatically. This makes it easy to browse and select the BigQuery data you need. However, you may have been given access to data sets in projects you are not a member of. With this enhancement, you can add those data sets manually.

In the Data Source dialog, you can now add additional projects manually. Spotfire will verify the projects names and list the data sets you have access to together with the data sets retrieved automatically from projects you are a member of.

The data source dialog for Google BigQuery now supports entering additional project names manually.

The Views in Connection dialog will list automatically retrieved data sets together with data sets from projects entered manually in the previous step.

Mark more Google BigQuery items 

When exploring Google BigQuery data by interactive marking in visualizations, the generated queries now use tuples IN. This enables you to mark more items than what was possible in previous Spotfire versions.

Connectors: Fewer on-demand sub-queries

Connector on-demand queries now contain fewer sub-queries. This means more data sources are able to support complex on-demand configurations.

Oracle Transparent Application Failover (TAF) in combination with Oracle RAC support

The Spotfire connector for Oracle now supports Transparent Application Failover (TAF) in combination with Oracle RAC.

Oracle RAC is a cluster database with a shared cache architecture that overcomes the limitations of traditional shared-nothing and shared-disk approaches to provide highly scalable and available database solutions for all business applications. Oracle RAC is a key component of Oracle's private cloud architecture.

Connect to your database by entering a connect descriptor.

Amazon Aurora PostgreSQL support

The TIBCO Spotfire connector for PostgreSQL now supports Amazon Aurora PostgreSQL.

This is possible thanks to a new option to import TLS certificate files and store the certificates as part of Spotfire data source objects.

Amazon RDS for PostgreSQL support

The TIBCO Spotfire connector for PostgreSQL now supports Amazon RDS for PostgreSQL.

This is possible thanks to a new option to import TLS certificate files and store the certificates as part of Spotfire data source objects.

Microsoft Azure Database for PostgreSQL support

The TIBCO Spotfire connector for PostgreSQL now supports Azure Database for PostgreSQL.

This is possible thanks to a new option to import TLS certificate files and store the certificates as part of Spotfire data source objects.

TIBCO Cloud Spotfire - more data sources supported

As a TIBCO Cloud Spotfire user you can now publish analysis files to the Library, open them in the web client and refresh data directly from the additional data sources listed below.

The complete list of supported data sources on TIBCO Cloud Spotfire is available here.

PostgreSQL

1. PostgreSQL
2. Amazon Aurora PostgreSQL
3. Amazon RDS for PostgreSQL
4. Microsoft Azure Database for PostgreSQL

MySQL

1. Oracle MySQL
2. Amazon Aurora MySQL
3. Amazon RDS MySQL
4. Microsoft Azure Database for MySQL

Snowflake

Our native Snowflake integration is finally available on TIBCO Cloud Spotfire as well.

Support for new database versions

The respective data connector now supports the following database versions:

• Dremio 3.x and 4.x
• Oracle 19C
• PostgreSQL 12.x
• SQL Server 2019

More Teradata query bands

The TIBCO Spotfire Connector for Teradata now includes additional query information such as client user and domain.

This is the complete list of query bands:

Administration

PostgreSQL 11 and 12 supported as Spotfire DB

Spotfire now supports PostgreSQL versions 11 and 12 (both for Windows and Linux) as Spotfire Server database.

PostgreSQL version 12 is the latest stable version. PostgreSQL version 11 is the latest version supported by the major Cloud providers.

Microsoft SQL Server 2019 supported as Spotfire DB

Spotfire now supports Microsoft SQL Server 2019 for Windows as Spotfire Server database.

Microsoft SQL Server 2019 (15.x) is the latest public release of SQL Server.

Increased Spotfire Server robustness

TIBCO Spotfire Server (TSS) uses Apache Ignite for server clustering and distributed cache. 

Apache Ignite now runs as stand-alone processes (instead of embedded in the Spotfire Server). This configuration significantly increases the system's stability.

As before, the Apache Ignite processes form a cluster. Now, each Spotfire Server (TSS) process connects to the Ignite cluster as a client. 

In order to simplify deployment, configuration, and administration, the separate Ignite process is managed by the TSS process.

Spotfire Analyst upgrades avoid antivirus false positives

On Windows, starting with upgrades from Spotfire Analyst 10.9, the upgrade .exe file will be launched from the folder where Spotfire Analyst is installed, not from the Temporary folder as previously. This should reduce the risk of antivirus software interrupting upgrades.

Note: Upgrades to Spotfire Analyst 10.9 will still be executed from the Temporary folder.

Developer

API to configure dual shared scales

The C# API now allows configuring a visualization with dual scales. See the Visual Analytics section above for more information on dual scales.

API to add Python data function

The C# API now allows for creating a data function based on a Python script.

Digital signing using local certificate stores

The Spotfire Package Builder console app now supports the digital signing of cab files using a certificate in a local certificate store. This comes with the benefit of not having to provide a password on the command line.

Back to main What's New in Spotfire page