For users who are just getting started with SAS solutions, it can be overwhelming to learn how to take the data you have collected and turn it into something readable and useful. Fortunately, SAS Data Management exists to help users with these kinds of data management problems. In this comprehensive review of SAS Data Management, we cover key capabilities the solution offers to transform data into visually appealing, easy-to-understand reports or insights.
- What is SAS Data Management?
- What are the top features?
- Benefits of working with SAS Data Management
- SAS Data Management alternatives
What is SAS Data Management?
SAS Data Management is a data management and integration solution that allows users to manage business processes with ETL (extract, transform load), access management, debugging, audit trails and metadata. The solution provides access to data stored in various locations, including the cloud, data lakes, legacy systems and other enterprise applications.
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SAS Data Management incorporates various data management solutions, including data quality, integration and access, preparation, governance, and event stream processing options. With this comprehensive portfolio, users can choose the most appropriate product or service for their needs and budget.
What are the top features?
SAS Data Management is a comprehensive data management suite for businesses. The solution includes a range of features that make it easy to store, manage and access data in any format or location. These are some of the top features:
Centralized development environment
The centralized development environment enables users to create new projects and import existing projects using a single integrated interface. In addition, it simplifies the creation of data processing workflows by providing an intuitive graphical interface with drag-and-drop functionality. The drag-and-drop capabilities let non-technical users design custom programs for manipulating, analyzing, managing and reporting on data.
This tool can be integrated with other tools such as SAS Visual Analytics, allowing developers to produce reports and dashboards easily. During development, users can access logs and debug jobs interactively. Audit history and check-in/check-out allow designers to see which jobs or tables were changed when and by whom.
Integrated process designer
Users can create and change data management workflows with a visual, end-to-end process designer. It provides flexibility to configure user-defined processes with automatic parameterization, error handling, and additional features like transformation parameters between steps or parallelization across different compute nodes. In addition, the process designer controls the execution of data integration, SAS-stored processes and data quality jobs.
ETL and extract, load and transform (ELT)
Data management is a crucial component of any big data project. ETL is a process by which data flows through an organization and is transformed to make it more accessible. ELT typically extracts data from a warehouse database and loads it into Hadoop for further processing.
SAS Data Management ETL and ELT features improve teamwork and reusability with prebuilt SQL-based transformations for building tables and joining, inserting, deleting and updating data. ETL can also be used to change the data’s structure and perform complex calculations like time series forecasting and text mining. These two methods together can help you store more data without losing valuable information.
Data governance and metadata management
Administrators can use SAS Data Management to identify and classify data and metadata through a process called data discovery. You can also run reports on data lineage or the history and historical use of data.
The dashboard allows you to monitor these processes in real time. Features like access control allow you to establish permissions for different users, either using the existing roles or creating new ones. Using SAS Data Management, business users can independently manage data, fine-tune processes and analyze results.
With data federation, data can be accessed through a single query or dashboard, even if it’s stored across multiple databases, servers or cloud-based storage systems. As a result, users don’t have to worry about connecting to individual databases for every query or pulling information together from different applications.
The management module provides virtual access to database structures, enterprise applications, mainframe legacy files, text XML message queues and other sources. It also offers user analytics, which enables users to see how the data is being used in real time and better understand their needs.
This message queuing service is asynchronous, which means that sending and receiving applications do not have to be online simultaneously. This can be a major benefit for businesses that need to update their data or software but must wait on another party before they can start their updates.
SAS metadata bridge for big data
The metadata bridge is a tool that creates a bridge between imports-and-exports metadata and third-party applications for easier access to data from Hadoop and other sources. This feature can be especially beneficial for organizations that use SAS as their primary analytical platform but must import data from other databases and programs.
Enhanced administration and monitoring
This feature provides administrators with greater visibility into system usage, configuration settings, and resource utilization, which allows them to manage capacity more effectively and prevent performance problems. It also offers reporting capabilities that provide business intelligence functionalities. These enable decision-makers to understand the status of their company’s IT environment at any given time.
Integration of SAS data loader for Hadoop
The SAS data loader integration for Hadoop makes processing big data easier and faster. Administrators can monitor where all jobs are running, which jobs need attention and how long tasks take to complete. When migrating or syncing datasets, administrators can decide whether they want to overwrite or append existing data.
Migration and synchronization
The migration module enables you to move data from one location or format to another. And the sync module lets you synchronize two or more datasets residing in different locations or formats, so they can be used together as a single set.
Benefits of working with SAS Data Management
SAS Data Management was created by SAS Institute and is an integrated set of business intelligence tools that can help businesses reduce costs, improve performance and maximize the return on investment (ROI) of their analytical investments.
A benefit of working with SAS Data Management is that the solution offers enterprise-wide data governance, quality and security. This can help a company identify where its risks are and mitigate those risks. They also offer a full suite of analytics products for both big data analytics and more traditional business intelligence needs.
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The easy-to-use interface makes it simple to explore datasets, manage projects, automate workflows, publish insights and reports, and use collaborative software applications to boost team productivity. The usability of this tool means that it can be a great solution for more junior data teams and companies that wish to democratize data analysis in their organizations.
SAS Data Management alternatives
While SAS is one of the most popular data management tools, there are other options your company may want to consider, depending on their needs and budgetary requirements:
Hevo is an end-to-end, bidirectional data pipeline platform designed for modern ETL, ELT and reverse ETL needs. Hevo’s modern architecture leverages the cloud to provide a unified environment with high availability. In addition, the solution includes built-in visualization tools that help users monitor their workload and gain insights into how their jobs run to optimize their performance.
Informatica PowerCenter is a data integration solution that enables data connections from any system or application. It can extract, transform and transfer data between systems. Other key features include automated data validation testing, business and IT collaboration, operations and governance oversight, real-time data for applications, and analytics and advanced data transformation capabilities.
IBM InfoSphere Information Server
IBM InfoSphere Information Server is a data integration and data quality tool that manages data from disparate sources and provides analytics on the information. This tool is useful for understanding, cleansing, monitoring and transforming data to make it available for new purposes or in different formats.
It can bring together multiple data types with different formats and levels of detail, including structured and unstructured business records. Analysts can also use it to monitor performance metrics across all business processes.
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