SAS Data Preparation provides an interactive, self-service environment for users who need to access, blend, shape and cleanse data to prepare it for reporting or analytics.
For more insight such as viewing SAS Data Preparation system requirements, downloading white papers, viewing screenshots and seeing other related material, please visit http://www.sas.com/data-preparation
SAS Data Preparation saves valuable time in preparing data. Business analysts, citizen data scientists, and nontechnical individuals can contribute quickly using an intuitive interface that provides point-and-click actions for critical functions – no coding or SQL skills required. Data scientists and IT users can use the same interface to prepare reusable plans for business analysts.
Boost productivity through reusability, collaboration, and self-service
No specialized skills or coding are required to access, merge and shape data. Data preparation tasks can be saved in projects, then shared and reused by others.
Empower analytics users with fast results.
Prebuilt transformations and data cleansing functions facilitate iterative data exploration and refinement. And with in-memory distributed processing and parallel I/O, responses can be delivered in near-real time.
Reduce total cost of ownership.
Make the most of your existing resources by giving them a visual, interactive interface that guides them through data preparation tasks with software that requires very little training.
Spend more time on reporting and analytics
By leveraging business analysts, citizen data scientists, and nontechnical resources in data preparation work you can invest more time in driving the business with reporting and analytics
Powerful data transformations
Leverage row- and column-based transformations to standardize, remediate, and shape data. Use code-based transformations to leverage powerful SAS code. Transform multiple inputs without SQL or SAS.
Built-In Data Quality
Standardize data with locale- and context-specific definitions into a common format, like casing. Perform data identification for patterns such as gender, email, and phone. Match codes to assist in “fuzzy” matching. Use parsing to tokenize data into substrings, such as Address. Change case, convert column, rename, remove, split, trim whitespace, and your own custom calculations.
Built-In Data Profiling
Generate column-based and table-based basic and advanced profile metrics. Use table-level profile metrics to uncover data quality issues. 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.
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.
Comprehensive data and metadata access
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, and SAS® Cloud Analytic Services (CAS).
Retain big data in situ, and push processing to Teradata or Hadoop by including SAS In-Database optional add-ons.
Of course you would! To learn more about SAS Data Preparation system requirements, download white papers, view screenshots and see other related material, please visit: http://www.sas.com/data-preparation
You can also message me @DanielPaner here at any time!