You can use the Data Asset node to pull in data from remote data sources that use
connections or from your local computer. First, you must create the connection.
Note for connections to a Planning Analytics database, you must choose a view (not a cube).
You can also pull in data from a local data file (.csv,
.txt, .json, .xls,
.xlsx, .sav, and .sas are supported).
Only the first sheet is imported from spreadsheets. In the node's properties, under
DATA, select one or more data files to upload. You can also drag-and-drop the
data file from your local file system onto your canvas.
Note: You can import a stream (.str) into watsonx.ai Studio that was created in SPSS Modeler
Subscription or SPSS Modeler client. If the
imported stream contains one or more import or export nodes, you are prompted to convert the nodes.
See Importing an SPSS Modeler stream.
Setting data format options
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
Inferring data structure
SPSS Modeler processes a sample of the records in the data to
infer the structure of the data and the types of data. Adjust the number for Infer record
count if the first 1000 records are not a good sample for the number of records that you
have. Sometimes, SPSS Modeler can make incorrect inferences about
the structure of the data. For more information, see Troubleshooting SPSS Modeler.
Importing data from an SPSS Statistics file
If you import data from an SPSS Statistics file (.sav), the following
options are available:
Variable names. Select a method of handling variable names and labels upon
import from an SPSS Statistics .sav file. Metadata that you choose to include
here persists throughout your work inSPSS Modeler and may be exported
again for use in IBM SPSS Statistics.
Read names and labels. Select to read both variable names and labels into
SPSS Modeler. This option is enabled by default, and variable names
are displayed in the Type node. Labels are displayed in charts, model browsers, and other types of
output. By default, the display of labels in output is disabled.
Read labels as names. Select to read the descriptive variable labels from
the SPSS Statistics .sav file rather than the short field names, and use these
labels as variable names in SPSS Modeler.
Values. Select a method of handling values and labels upon import from an
SPSS Statistics .sav file. Metadata that you choose to include here persists
throughout your work in SPSS Modeler and can be exported again for
use in SPSS Statistics.
Read data and labels. Select to read both actual values and value labels
into SPSS Modeler. This option is enabled by default, and the values
themselves are displayed in the Type node. Value labels are displayed in the Expression Builder,
charts, model browsers, and other types of output.
Read labels as data. Select if you want to use the value labels from the
.sav file rather than the numerical or symbolic codes that are used to
represent the values. For example, selecting this option for data with a gender field whose values
of 1 and 2 represent male and female, converts the
field to a string and imports male and female as the actual
values.
It's important to consider missing values in your SPSS Statistics data before you select
this option. For example, if a numeric field uses labels only for missing values (0
= No Answer, –99 = Unknown), then selecting the Read
labels as data option imports only the value labels No Answer and
Unknown and converts the field to a string. In such cases, you should import
the values themselves and set missing values in a Type node.
Use field format information to determine storage. If you deselect this
option, field values that are formatted in the .sav file as integers (such as
fields that are specified as Fn.0 in the Variable View in IBM SPSS Statistics) are imported
using integer storage. All other field values except strings are imported as real numbers.
If you select this option (default), all field values except strings are imported as real
numbers, whether formatted in the .sav file as integers or not.
Read timestamp as date. By default, all timestamp values are shown as
dates. Deselect this option to override this behavior.
Using SQL to pull in data
In the Data Asset import node properties, under Mode, you can select
SQL Query if you want to use custom SQL to import data from a database. Use
an SQL SELECT statement to pull in rows or columns of data from a database. The
Source path field doesn't apply if you're using the SQL
Query mode.Figure 2. Custom SQL query
The following example pulls in certain rows of data from a database
table:
select * from GOSALES.ORDER_DETAILS
where UNIT_COST > 40,000 LIMIT 4
And this example pulls in certain columns of data from a database
table:
select QUANTITY, UNIT_COST, UNIT_PRICE from GOSALES.ORDER_DETAILS
The SQL syntax that you use can vary depending on the database platform. For example, if you pull
in data from an Informix database, Informix requires field names to be surrounded by double
quotation marks. For
example:
select "Age", "Sex" from testuser.canvas_drug
This SQL feature should only be used to pull in data. Use caution so as not to manipulate the
data in your database.
The following databases currently support this custom SQL feature:
Amazon Redshift
Apache Hive
Apache Impala
Compose for PostgreSQL
Db2 on Cloud
Db2 Warehouse
Google BigQuery
Informix
Microsoft SQL Server
MySQL
Netezza
Oracle
Pivotal Greenplum
Salesforce.com
Snowflake
SAP ASE
SAP IQ
Teradata
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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.
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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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