By Synectics
How new features in SAS DI studio help you discover insights, manage data, and make analytics approachable
SAS Data Integration Studio is a visual design tool. It has always been useful for building, implementing, and managing data integration processes, and new features in the SAS® 9.4 TS1M4 release have made it even better. This article will concentrate on the transformations that connect to SAS Viya and Amazon Web Services.
SAS Data Integration Studio New Features
The latest releases of SAS® Data Integration Studio, DataFlux Data Management Studio, and other SAS® Data Management feature provide new and enhanced features to help data warehouse developers, data integration specialists, and data scientists carry out data management tasks more efficiently and with greater control and flexibility. There are several new data connectivity features that support discovering and ingesting new data content. New features include enhanced support of big data platforms such as Hadoop and the Cloud.
Connecting to Sas® Yiya™
SAS® Data Integration Studio, SAS® Enterprise Guide®, SAS® Enterprise Miner™, and other SAS® applications have added new capabilities to integrate with SAS® Viya™ and SAS® Cloud Analytic Services. These capabilities package the SAS/CONNECT® code needed to support transferring data and programs to the SAS Viya platform. SAS Data Integration Studio has implemented a new transformation that supports this integration. To use the transformation, you first register a SAS Cloud Analytic Server (CAS) definition and a new application server in the SAS® Metadata Repository that contains a SAS Connect Server to the SAS Viya platform. You can then register a library that references that application server. Once you have set up the environment in the SAS® Metadata Server, you can use the transformation to move data and programs to and from the CAS environment. You can also submit your own code via the code tab in the transformation to the CAS server, and the code will execute in the CAS system. Figure 1 illustrates the transformation and a partial example of the generated code in SAS® Data Integration Studio.
Transferring data to and from the cloud
There are several new features in SAS that support data access to and from cloud data sources. One new feature is integration with Amazon Redshift. Available from Amazon Web Services (AWS), Redshift is a highly scalable cloud data storage system. The SAS/ACCESS® to Amazon Redshift engine, introduced in the third maintenance release for SAS® 9.4, has been enhanced to support bulk data loads for increased performance. This data source type has also been added to SAS® Data Integration Studio and other SAS clients for easy connectivity of this data source to jobs and reports. Enhancements have also been made to support SQL pushdown to Amazon Redshift for SAS procedures, including FREQ, MEANS, SUMMARY, and TABULATE. For moving data to and from various cloud sources, SAS supports secure FTP (SFTP) via the FILENAME statement. For content specific to Amazon, SAS has also released a new procedure, PROC S3. Figure 2 illustrates these two methods.
SAS Data Integration Studio has added three new transformations that make it easier to generate SAS code using these methods (Figure 3).
About the Author
Sai Potluri is a Senior ETL Architect with Synectics for Management Decisions, Inc. working in the EDW program. He has 12 years of experience in implementing data warehousing solutions using a wide variety of ETL tools such as Ab Initio, Informatica, Talend, and SAS Data Integration Studio. Among Sai Potluri’s primary responsibilities is the ETL Architecture, ETL Design, Development and research outputs in his area of expertise. Sai has written research papers in SAS which will be published and presented at the SouthEast SAS® Users Group (SESUG) conference at the SAS campus, in Cary, North Carolina in November.
References: Pendergrass, J. (2017). The Architecture of the SAS® Cloud Analytic Services in SAS® ViyaTM, Paper SAS309-2017. Retrieved from http://support.sas.com/resources/papers/proceedings17/SAS0309-2017.pdf
About The Author
