SAS Data Preparation Features List

Data & metadata access

• Use any authorized internal source, accessible external data sources and data held in-memory in SAS Viya.
  • View a sample of a table or file loaded in the in-memory engine of SAS Viya, or from data sources registered with SAS/ACCESS, to visualize the data you want to work with.
  • Quickly create connections to and between external data sources.
  • Access physical metadata information like column names, data types, encoding, column count and row count to gain further insight into the data.
• Data sources and types include:
  • Amazon S3.
  • Amazon Redshift.
  • DNFS, HDFS, PATH-based files (CSV, SAS, Excel, delimited).
  • DB2.
  • Hive.
  • Impala.
  • SAS LASR^™.
  • ODBC.
  • Oracle.
  • Postgres.
  • Teradata.
  • Feeds from Twitter, YouTube, Facebook, Google Analytics, Google Drive, Esri and local files.
  • SAS Cloud Analytic Services (CAS).

Data provisioning

• Parallel load data from desired data sources into memory simply by selecting them – no need to write code or have experience with an ETL tool. (Data cannot be sent back to the following data sources: Twitter, YouTube, Facebook, Google Analytics, Esri; it can only be sourced from these sites).
• Reduce the amount of data being copied by performing row filtering or column filtering before the data is provisioned.
• Retain big data in situ, and push processing to the source system by including SAS In-Database optional add-ons.

Guided, interactive data preparation

• Transform, blend, shape, cleanse and standardize data in an interactive, visual environment that guides you through data preparation processes.
• Easily understand how a transformation affected results, getting visual feedback in near-real-time through the distributed, in-memory processing of SAS Viya.

Machine learning & AI suggestions

• Take advantage of AI and machine learning to scan data and make intelligent transformation suggestions.
• Accept suggestions and complete transformations at the click of a button. No advanced or complex coding required.
• Automated suggestions include:
  • Casing.
  • Gender analysis.
  • Match code.
  • Parse.
  • Standardization.
  • Missing value imputation for numeric variables.
  • One hot encoding.
  • Remove column.
  • Whitespace trimming.
  • Convert column data type.
  • Center and scale.
  • Dedupe.
  • Unique ID creation.
  • Column removal for sparse data.

Column-based transformations

• Use column-based transformations to standardize, remediate and shape data without doing configurations. You can:
  • Change case.
  • Convert column.
  • Rename.
  • Remove.
  • Split.
  • Trim whitespace.
  • Custom calculation.
• Support for wide tables allows for the saving of data plans for quick data preparation jobs.

Row-based transformations

• Use row-based transformations to filter and shape data.
• Create analytical-based tables using the transpose transformation to prepare the data for analytics and reporting tasks.
• Create simple or complex filters to remove unnecessary data.

Code-based transformations

• Write custom code to transform, shape, blend, remediate and standardize data.
• Write simple expressions to create calculated columns, write advanced code or reuse code snippets for greater transformational flexibility.
• Import custom code defined by others, sharing best practices and collaborative productivity.

Multiple-input-based transformations

• Use multiple-input-based transformations to blend and shape data.
• Blend or shape one or more sets of data together using the guided interface – there’s no requirement to know SQL or SAS. You can:
  • Append data.
  • Join data.
  • Transpose data.

Data profiling

• Profile data to generate column-based and table-based basic and advanced profile metrics.
• Use the table-level profile metrics to uncover data quality issues and get further insight into the data itself.
• Drill into each column for column-level profile metrics and to see visual graphs of pattern distribution and frequency distribution results that help uncover hidden insights.
• Use a variety of data types/sources (listed previously). To profile data from Twitter, Facebook, Google Analytics or YouTube, you must first explicitly import the data into the SAS Viya in-memory environment.

Data quality processing

SAS Data Quality in SAS Viya is included in SAS Data Preparation.

Data cleansing

• Use locale- and context-specific parsing and field extraction definitions to reshape data and uncover additional insights.
• Use the extraction transformation to identify and extract contact information (e.g., name, gender, field, pattern, identify, email and phone number) in a specified column.
• Use parsing when data in a specified column needs to be tokenized into substrings (e.g., a full name tokenized into prefix, given name, middle name and family name).
• Derive unique identifiers from match codes that link disparate data sources.
• Standardize data with locale- and context-specific definitions to transform data into a common format, like casing.

Identity definition

• Analyze column data using locale-specific rules to determine gender or context.
  • Use identification analysis to analyze the data and determine its context, which is particularly valuable if the data or source of data is unfamiliar.
  • Use gender analysis to determine the gender of a name using locale-specific rules so the data can be easily filtered or segmented.
  • Create a unique ID for each row with unique ID generator.
  • Identify the subject data in each column with identification analysis.
  • Identify, find and sort data by tagging data with columns and tables.

Data matching

• Determine matching records based upon locale- and context-specific definitions.
• Easily identify matching records using more than 25 context-specific rules such as date, address, name, email, etc.
• Use the results of the match code transformation to remove duplicates, perform a fuzzy search or a fuzzy join.
• Find like records and logically group together.

System & job monitoring

• Use integrated monitoring capabilities for system- and job-level processes.
• Gain insight into how many processes are running, how long they’re taking and who is running them.
• Easily filter through all system jobs based on job status (running, successful, failed, pending and canceled).
• Access job error logs to help with root-cause analysis and troubleshooting. (Note: Monitoring is available using SAS Environment Manager and the job monitor application.)

Data import & data preparation job scheduling

• Create a data import job from automatically generated code to perform a data refresh using the integrated scheduler.
• Schedule data explorer imports as jobs so they will become an automatic, repeatable process.
• Specify a time, date, frequency and/or interval for the jobs.

Data lineage

• Explore relationships between accessible data sources, data objects and jobs.
• Use the relationship graph to visually show the relationships that exist between objects, making it easier to understand the origin of data and trace its processing.
• Create multiple views with different tabs, and save the organization of those views.

Plan templates & project collaboration

• Use data preparation plans (templates), which consist of a set of transformation rules that get applied to one or more sources of data, to improve productivity (spend less time preparing data).
• Reuse the templates by applying them to different sets of data to ensure that data is transformed consistently to adhere to enterprise data standards and policies.
• Rely on team-based collaboration through a project hub used with SAS Viya projects. The project’s activity feed shows who did what and when, and can be used to communicate with other team members.

Batch text analysis

• Quickly extract contents of documents, and perform text identification and extraction.