Data access is fundamental to JMP. For the last several releases, we've been building infrastructure so that you can access data efficiently, regardless of where it lives. Once you to get the data you need into JMP, you can begin answering the questions you have. Every version of JMP adds new efficiency gains, performance improvements, and workflow additions. JMP 19 continues that trend.
General
Menu access
Menu access for data connectors has changed in JMP 19; they can now be accessed, as well as Historians and Legacy ODBC, in the Connect To menu. Data connectors are found at File>Connect To>Data Connectors.
SQL console
The SQL console allows you to submit multi-statement SQL commands against supporting databases, as well as saving and running .sql files. You can also use the console to open multiple tables at once.
REST Snowflake connector
This new Snowflake data connector uses a REST-based interface to interact with Snowflake, focusing on performance. The connector is integrated with JMP’s data connector framework, which allows configuration and integration with Query Builder. It provides noticeable performance improvements over the existing ODBC-based Snowflake connector. This new connector can be found as com.jmp.snowflake_rest in the data connector selector.
After configuring a new Snowflake data connector based on your site’s Snowflake administrator settings, you can connect using username and password or OAuth2. For configuring the connection, contact your Snowflake administrator and/or your OAuth2 administrator. Snowflake recommends using the OAuth2 authentication method for increased security.
For JMP Live scripts, using a programmatic access token can help run tasks without requiring a user to log in. The token can be entered into the password field of the Snowflake configuration.
SAS integration
JMP 19 reintroduces the SAS integration that was removed in JMP 18. The new SAS integration uses the SASPy Python module to connect to and interact with SAS. This new SAS integration allows you to connect to SAS Viya and SAS 9.4, either remotely or via local servers to import, sample, and run SQL queries on SAS data. Additionally, users can interact with SAS by submitting SAS code, reading SAS logs, generating ODS HTML5 reports, and importing SAS data into JMP using the GUI or using JSL.
SAS integration is installed on demand upon first use. Connect to SAS with the data connector selector and choosing the base configuration for your desired connection type. Both remote and local SAS connections require Java to be installed on the system per SASpy. Similar to JMP 18, JMP 19 does not install Java.
Table import and export
SAS integration provides three new data connectors to access data tables from SAS: remote, local, and SAS Viya. They can be accessed with the data connector interface. SAS Query Builder supports access to SAS data sets, allowing complex queries to be created in a friendly user interface. To export a data table, open it, go to File>Export..., and select the SAS export option. This opens the SAS Export dialog where you can then choose the export location and other options.
Viya and CAS compatibility
When using a Viya connection, you can automatically connect to CAS and access both CAS and traditional SAS tables. This connector does not require Java. A user can interact with CAS one of two ways: with CAS integration that was introduced in JMP 16 or though the SAS program editor that has the full SAS Viya programming capabilities.
Program editor
Included is a SAS program editor, which allows SAS code to be submitted and reports generated reports generated in SAS to be shown in JMP windows. The results and code can optionally be stored inside a JMP project.
SAS results
SAS integration allows SAS results to be viewed. By default, when SAS code is submitted, the results are organized in a JMP project. The results, organized by execution date, can include any generated SAS data imported by JMP, any generated SAS report, and the SAS code that generated the output.
Python-based connectors
User-defined connectors
New in JMP 19, data sources can be configured and connected to various data sources via an extendable Python interface. The new Python API creates custom data connectors that are built upon Python and can connect to a variety of data sources, even ones that don’t have a built-in connector in JMP. For more information and the full specification of this new interface, see the Python Data Connector Demo add-in, available on the JMP Marketplace, for a code scaffold so you can start building your own connections.
Add-ins
The JMP Marketplace now offers new data connector add-ins that connect even more data sources and resources. The add-ins are built and maintained by JMP staff and members of the JMP Community, so you can customize your experience even further. The specific data connector add-ins require JMP 19 and above.
AWS S3 Data Connector
The AWS S3 Data Connector add-in is a new way to connect to AWS S3 from within JMP. With this add-in, you can connect with AWS to import your data. The add-in is now available on the JMP Marketplace for JMP 19 and beyond.
Microsoft OneDrive and SharePoint Data Connectors
The OneDrive and SharePoint Data Connectors add-in uses the Python integration and Python data connector interface to connect to SharePoint and import your data.
TDMS Data Connector
The TDMS Data Connector allows you to browse all TDMS (LabVIEW 4 (tm)) data files in a directory and to open all groups within one file as a JMP data table. Alternatively, you can open individual channels within a TDMS group by selecting a specific file to examine. The data connector interface also provides a way to work on the data within JMP Query Builder.
Salesforce Data Connector
The Salesforce Data Connector allows users to browse and query objects in their Salesforce environment. There are options to introspect a Salesforce object to view its attributes and to import all data pertaining to the object.
PostgreSQL Data Connector
As an alternative to ODBC in JMP, users can connect to a PostgreSQL instance using a data connector. It allows users to explore schemas and tables in their database and open data tables for analysis. Note, this add-in is suited for smaller data sets since the processing is performed client-side.
MySQL Data Connector
As an alternative to ODBC in JMP, users can connect to a MySQL instance using a data connector. It allows users to explore schemas and tables in their database, and open data tables for analysis. Note, this add-in is suited for smaller data sets since the processing is performed client-side.
AspenTech IP.21 importer
The AspenTech IP.21 importer enables direct, native access to Aspen InfoPlus.21 (IP.21) historian data within JMP. Modeled after the AVEVA PI Importer, it offers both an interactive import dialog and full JSL support, allowing you to authenticate with your server, browse available data sources, select and filter tags, and define time ranges and retrieval methods. Flexible options such as interpolated or aggregate retrieval and asynchronous import make it easy to bring process data into JMP for time-series analysis, visualization, or integration with other sources, streamlining workflows for industries that rely on IP.21.
Co-authors: Bryan Boone (@bryan_boone), Neal Siekierski (@nealsiek), Chris Hesser (@chrishesser), and Michael Hecht (@hecht_jmp).
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