You can use the Data Asset Export node
to write data either to remote data sources by using connections or to a data asset in your project.
This node is useful if you want to update these data assets or connections with data that is
generated in SPSS Modeler.
To configure the node, start by picking the connection or data
asset where you want to export the data:
In the node's properties, click Change data asset.
Select either Connection or Data asset, and pick
the connection or data asset that you want.
For a list of supported data sources, see Supported data sources for SPSS Modeler. If you export
data to an SPSS
Statistics.sav file, check that field names do not contain spaces. The
.sav file format does not support spaces in field names.
If the data asset or connection contains data already, you can choose how to handle the new data
and the existing data. For example, you can append the new data to the existing data or replace the
existing data. The available options depend on the file type and connection.
After the node runs, you can find the data at the location that you specified.
Attention: If your .csv file
contains any malicious payloads in an input field (in formulas for example), these payloads
might be executed.
Setting the field delimiter and decimal symbol
Different countries use different symbols to separate the integer part
from the fractional part of a number. For example, some countries use a comma (4,5) instead of a
period (4.5). And countries sometimes use different symbols to separate fields in data. For example,
you might use colons or tabs rather than using commas to separate the fields. You can specify which
of these symbols to use. Double-click the node to open its properties and specify data formats.Figure 1. Field delimiter and decimal symbol options
Exporting data to Planning Analytics (TM1)
To export data to Planning Analytics, you must use Planning Analytics version 2.0.5 or
higher.
When you export to a Planning Analytics database connection, you select a view, and
the dimensions are automatically mapped to fields by name. Before you export to Planning Analytics,
make sure that the dimensions can be mapped to your data fields (rename or derive the fields if
needed). You can encounter errors if the input schema doesn't exactly match the view of the export
schema. Since the target schema is fixed, renaming of derivation must be from the incoming data.
Important:
You can overwrite only an existing data asset. You can't append to a data asset, stop with an
error, do nothing, or create a new one as you can with other connection types. The data is replaced.
Select the option Replace the data set when exporting to Planning
Analytics.
You receive a WDP Connection Error in the following situations:
Export to an unsupported version of Planning Analytics (earlier than 2.0.5)
Export to an existing cube that has a different schema than the incoming data
Export to a nonexistent cube
Export using any option under If the data set already exists other than
Replace the data set.
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Cloud Pak for Data relationship map
Use this interactive map to learn about the relationships between your tasks, the tools you need, the services that provide the tools, and where you use the tools.
Select any task, tool, service, or workspace
You'll learn what you need, how to get it, and where to use it.
Tasks you'll do
Some tasks have a choice of tools and services.
Tools you'll use
Some tools perform the same tasks but have different features and levels of automation.
Create a notebook in which you run Python, R, or Scala code to prepare, visualize, and analyze data, or build a model.
Automatically analyze your tabular data and generate candidate model pipelines customized for your predictive modeling problem.
Create a visual flow that uses modeling algorithms to prepare data and build and train a model, using a guided approach to machine learning that doesn’t require coding.
Create and manage scenarios to find the best solution to your optimization problem by comparing different combinations of your model, data, and solutions.
Create a flow of ordered operations to cleanse and shape data. Visualize data to identify problems and discover insights.
Automate the model lifecycle, including preparing data, training models, and creating deployments.
Work with R notebooks and scripts in an integrated development environment.
Create a federated learning experiment to train a common model on a set of remote data sources. Share training results without sharing data.
Deploy and run your data science and AI solutions in a test or production environment.
Find and share your data and other assets.
Import asset metadata from a connection into a project or a catalog.
Enrich imported asset metadata with business context, data profiling, and quality assessment.
Measure and monitor the quality of your data.
Create and run masking flows to prepare copies of data assets that are masked by advanced data protection rules.
Create your business vocabulary to enrich assets and rules to protect data.
Track data movement and usage for transparency and determining data accuracy.
Track AI models from request to production.
Create a flow with a set of connectors and stages to transform and integrate data. Provide enriched and tailored information for your enterprise.
Create a virtual table to segment or combine data from one or more tables.
Measure outcomes from your AI models and help ensure the fairness, explainability, and compliance of all your models.
Replicate data to target systems with low latency, transactional integrity and optimized data capture.
Consolidate data from the disparate sources that fuel your business and establish a single, trusted, 360-degree view of your customers.
Services you can use
Services add features and tools to the platform.
Develop powerful AI solutions with an integrated collaborative studio and industry-standard APIs and SDKs. Formerly known as Watson Studio.
Quickly build, run and manage generative AI and machine learning applications with built-in performance and scalability. Formerly known as Watson Machine Learning.
Discover, profile, catalog, and share trusted data in your organization.
Create ETL and data pipeline services for real-time, micro-batch, and batch data orchestration.
View, access, manipulate, and analyze your data without moving it.
Monitor your AI models for bias, fairness, and trust with added transparency on how your AI models make decisions.
Provide efficient change data capture and near real-time data delivery with transactional integrity.
Improve trust in AI pipelines by identifying duplicate records and providing reliable data about your customers, suppliers, or partners.
Increase data pipeline transparency so you can determine data accuracy throughout your models and systems.
Where you'll work
Collaborative workspaces contain tools for specific tasks.
Where you work with data.
> Projects > View all projects
Where you find and share assets.
> Catalogs > View all catalogs
Where you deploy and run assets that are ready for testing or production.
> Deployments
Where you manage governance artifacts.
> Governance > Categories
Where you virtualize data.
> Data > Data virtualization
Where you consolidate data into a 360 degree view.
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