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Choosing a tool
Choosing a tool

Choosing a tool

The core services for Cloud Pak for Data as a Service provide a range of tools for users with all levels of experience in preparing, analyzing, and modeling data, from beginner to expert. The right tool for you depends on the type of data you have, the tasks you plan to do, and the amount of automation you want.

To pick the right tool, consider these factors.

The type of data you have

  • Tabular data in delimited files or relational data in remote data sources
  • Image files
  • Textual (unstructured) data in documents

The type of tasks you need to do

  • Prepare data: cleanse, shape, visualize, organize, and validate data.
  • Analyze data: identify patterns and relationships in data, and display insights.
  • Build models: build, train, test, and deploy models to make predictions or optimize decisions.

How much automation you want

  • Code editor tools: Use to write code in Python, R, or Scala, all also with Spark.
  • Graphical builder tools: Use menus and drag-and-drop functionality on a builder to visually program.

  • Automated builder tools: Use to configure automated tasks that require limited user input.

Find the right tool:

Tools for tabular or relational data

Tools for tabular or relational data by task:

Tools for tabular or relational data
Tool Tool type Prepare data Analyze data Build models
Jupyter notebook editor Code editor
Federated Learning Code editor
RStudio Code editor
Data Refinery Graphical builder
Data privacy Automated builder
Watson Query Graphical builder
DataStage Graphical builder
Dashboard editor Graphical builder
SPSS Modeler Graphical builder
Decision Optimization model builder Graphical builder and code editor
AutoAI Automated builder
Metadata import Automated builder
Metadata enrichment Automated builder
IBM Match 360 with Watson (Beta) Automated builder

Tools for textual data

Tools for building a model that classifies textual data:

Tools for textual data
Tool Code editor Graphical builder Automated builder
Jupyter notebook editor
RStudio
SPSS Modeler

Tools for image data

Tools for building a model that classifies images:

Tools for image data
Tool Code editor Graphical builder Automated builder
Jupyter notebook editor
RStudio

Accessing tools

To use a tool, you must create an asset specific to that tool, or open an existing asset for that tool. To create an asset, click New asset and then choose the asset type you want. This table shows the asset type to choose for each tool.

Tools to asset type mapping
To use this tool Choose this asset type
Jupyter notebook editor Jupyter notebook
Data Refinery Data Refinery flow
Data privacy Masking flows
DataStage DataStage flow
Dashboard editor Dashboard
SPSS Modeler Modeler flow
Decision Optimization model builder Decision Optimization
AutoAI AutoAI experiment
Federated Learning Federated Learning experiment
Metadata import Metadata import
Metadata enrichment Metadata enrichment
IBM Match 360 with Watson (Beta) Master data configuration

To edit notebooks with RStudio, click Launch IDE > RStudio.

Jupyter notebook editor

Use the Jupyter notebook editor to create a notebook in which you run code to prepare, visualize, and analyze data, or build and train a model.

Required service Watson Studio

Data format Any

Data size Any

How you can prepare data, analyze data, or build models Write code in Python, R, or Scala, all also with Spark. Include rich text and media with your code. Work with any kind of data in any way you want. Use preinstalled or install other open source and IBM libraries and packages. Schedule runs of your code Import a notebook from a file, a URL, or the Gallery. Share read-only copies of your notebook externally.

Get started To create a notebook, click New asset > Notebook.

Learn more Load and analyze public data sets video Videos about notebooks Sample notebooks Documentation about notebooks

Data Refinery

Use Data Refinery to prepare and visualize tabular data with a graphical flow editor. You create and then run a Data Refinery flow as a set of ordered operations on data.

Required services Watson Studio or Watson Knowledge Catalog

Data format Tabular: Avro, CSV, JSON, Microsoft Excel (xls and xlsx formats and first sheet only), Parquet, SAS with the "sas7bdat" extension (read only), TSV (read only), or delimited text data asset
Relational: Tables in relational data sources

Data size Any

How you can prepare data Cleanse, shape, organize data with over 60 operations. Save refined data as a new data set or update the original data. Profile data to validate it. Use interactive templates to manipulate data with code operations, functions, and logical operators. Schedule recurring operations on data.

How you can analyze data Identify patterns, connections, and relationships within the data in multiple visualization charts.

Get started

To create a Data Refinery flow, click New asset > Data Refinery flow.

Learn more Videos about Data Refinery Shape Data video
Documentation about Data Refinery

Watson Query

Use Watson Query to connect multiple data sources into a single self-balancing collection of data sources or databases.

Data format Relational: Tables in relational data sources

Data size Any

How you can prepare data Connect to multiple data sources. Create virtual tables.

Get started To create virtual tables, Click Data > Data virtualization. From the service menu, click Virtualization > Virtualize > Tables.

Learn more Watson Query documentation

DataStage

Use DataStage to prepare and visualize tabular data with a graphical flow editor. You create and then run a DataStage flow as a set of ordered operations on data.

Data format Tabular: Avro, CSV, JSON, Parquet, TSV (read only), or delimited text files
Relational: Tables in relational data sources

Data size Any

How you can prepare data Design a graphical data integration flow that generates Orchestrate code to run on the high performing, DataStage parallel engine. Perform operations such as: Join, Funnel, Checksum, Merge, Modify, Remove Duplicates, and Sort.

Get started

To create a DataStage flow, click New asset > DataStage flow.

Learn more DataStage documentation

Dashboard editor

Use the Dashboard editor to create a set of visualizations of analytical results on a graphical builder.

Required service Cognos Dashboard Embedded

Data format Tabular: CSV files Relational: Tables in some relational data sources

Data size Any size

How you can analyze data Create graphs without coding. Include text, media, web pages, images, and shapes in your dashboard. Share interactive dashboards externally.

Get started

To create a dashboard, click New asset > Dashboard.

Learn more

Videos about dashboards
Documentation about dashboards

SPSS Modeler

Use SPSS Modeler to create a flow to prepare data and build and train a model with a flow editor on a graphical builder.

Required services Watson Studio

Data formats Relational: Tables in relational data sources Tabular: Excel files (.xls or .xlsx), CSV files, or SPSS Statistics files (.sav) Textual: In the supported relational tables or files

Data size Any

How you can prepare data Use automatic data preparation functions. Write SQL statements to manipulate data. Cleanse, shape, sample, sort, and derive data.

How you can analyze data Visualize data with over 40 graphs. Identify the natural language of a text field.

How you can build models Build predictive models. Choose from over 40 modeling algorithms. Use automatic modeling functions. Model time series or geospatial data.
Classify textual data. Identify relationships between the concepts in textual data.

Get started To create an SPSS Modeler flow, click New asset > Modeler flow and then choose IBM SPSS Modeler.

Learn more SPSS Modeler - refreshed UI for an enterprise data science powerhouse video Documentation about SPSS Modeler

Decision Optimization model builder

Use Decision Optimization to build and run optimization models in the Decision Optimization modeler or in a Jupyter notebook.

Required services Watson Studio

Data formats Tabular: CSV files

Data size Any

How you can prepare data Import relevant data into a scenario and edit it.

How you can build models Build prescriptive decision optimization models. Create, import and edit models in Python DOcplex, OPL or with natural language expressions. Create, import and edit models in notebooks.

How you can solve models Run and solve decision optimization models using CPLEX engines. Investigate and compare solutions for multiple scenarios. Create tables, charts and notes to visualize data and solutions for one or more scenarios.

Get started To create a Decision Optimization model, click New asset > Decision Optimization, or for notebooks click New asset > Notebook.

Learn more Videos about Decision Optimization Documentation about Decision Optimization

AutoAI tool

Use the AutoAI tool to automatically analyze your tabular data and generate candidate model pipelines customized for your predictive modeling problem.

Required service Watson Machine Learning

Data format Tabular: CSV files

Data size Depends on model type. See AutoAI Overview for details.

How you can prepare data Automatically transform data, such as impute missing values and transform text to scalar values.

How you can build models Train a binary classification, multiclass classification, or regression model. View a tree infographic that shows the sequences of AutoAI training stages. Generate a leaderboard of model pipelines ranked by cross-validation scores. Save a pipeline as a model.

Get started To create an AutoAI experiment, click New asset > AutoAI experiment.

Learn more Documentation about AutoAI

Federated Learning

Use the Federated Learning tool to train a common model using distributed data. The data is never combined or shared, preserving data integrity while providing all participating parties with a model based on the aggregated data.

Required service Watson Machine Learning

Data format Any

Data size Any size

How you can build models Choose a training framework. Configure the common model. Configure a file for training the common model. Have remote parties train their data. Deploy the common model.

Get started To create an experiment, click New asset > Federated Learning experiment.

Learn more Documentation about Federated Learning

Metadata import

Use the metadata import tool to automatically discover and import technical and process metadata for data assets into a project or a catalog.

Required service Watson Knowledge Catalog

Data format Any

Data size Any size

How you can prepare data Import data assets from a connection to a data source.

Get started To import metadata, click New asset > Metadata import.

Learn more Documentation about metadata import Videos about Watson Knowledge Catalog

Metadata enrichment

Use the metadata enrichment tool to automatically profile data assets and analyze data quality in a project.

Required service Watson Knowledge Catalog

Data format Relational and structured: Tables and files in relational and non-relational data sources Tabular: Avro, CSV, or Parquet files

Data size Any size

How you can prepare and analyze data Profile and analyze a select set of data assets in a project.

Get started To enrich data, click New asset > Metadata enrichment.

Learn more Documentation about metadata enrichment

IBM Match 360 with Watson

Use IBM Match 360 with Watson to create master data entities that represent digital twins of your customers. Model and map your data, then run the matching algorithm to create master data entities. Customize and tune your matching algorithm to meet your organization's requirements.

Required services IBM Match 360 with Watson
IBM Watson Knowledge Catalog

Data size Up to 1,000,000 records (for the Beta Lite plan)

How you can prepare data Model and map data from sources across your organization. Run the customizable matching algorithm to create master data entities. View and edit master data entities and their associated records.

Get started To create an IBM Match 360 configuration asset, click New asset > Master data configuration.

Learn more Documentation about IBM Match 360 with Watson

RStudio IDE

Use RStudio IDE to analyze data or create Shiny applications by writing R code. RStudio can be integrated with a Git repository which must be associated with the project.

Required service Watson Studio

Data format Any

Data size Any size

How you can prepare data, analyze data, and build models Write code in R. Create Shiny apps. Use open source libraries and packages. Include rich text and media with your code. Prepare data. Visualize data. Discover insights from data. Build and train a model using open source libraries. Share your Shiny app in a Git repository.

Get started To use RStudio, click Launch IDE > RStudio.

Learn more Overview of RStudio IDE video
Videos about RStudio
Documentation about RStudio

Data privacy

Use the Data privacy tool to prepare masked copies or masked subsets of data from the catalog. Data is de-identified using advanced masking options with data protection rules.

Required service Watson Knowledge Catalog

Data format Relational: Tables in relational data sources

Data size Any size

How you can prepare data, analyze data, or build models Import data assets from governed catalog to project. Create masking flow job definitions to specify what data to mask with data protection rules. Optionally subset data to reduce size of copied data. Run masking flow jobs to load masked copies to target database connections.

Get started Ensure that pre-requisite steps in Watson Knowledge Catalog are completed. To privatize data, do one of the following tasks:

  • Click New asset > Data privacy.
  • Click the menu options for individual data assets to mask that asset directly.

Learn more Documentation about masking data

Parent topic: Projects