Want to Become a SAS Data Integration Developer? Take Your Next Step Now
As a part of the SAS Global Certification program, the exam A00-260 SAS Data Integration Developer has been introduced. It tests the candidate's ability to apply the skills and knowledge necessary for Data Integration Development in the SAS environment.
The candidate qualifies as a Data Integration Developer for his skills in tasks such as defining the architecture of the platform for SAS Business Analytics, creating metadata for source and target data, making metadata for target data and jobs, working with transformations, working with tables and table loader transformation, working with slowly changing dimensions, defining induced modifications and deploying positions.
Data Integration Developers design and implement integrations between software platforms, programs, and applications. They are expert problem solvers with comprehensive programming skills and proficiency in multiple coding languages and frameworks. They also determine errors, provide support, and develop strategies to navigate complex system overlaps.
How Does SAS Data Integration Developer Work?
SAS Data Integration Developer works by connecting source systems to target systems. Yet how these connections take place can considerably differ. Sometimes, this occurs in real-time via ETL and data warehouses. Other times, the data that moves from source to target is a carbon copy of the real thing, a procedure understood as data replication.
Where data virtualization is concerned, APIs are a crucial piece of the puzzle. An API is a back-end structure that allows the interactivity of different software. With this context, it should be reasonably easy to understand why API integrations have a significant role in data integration.
To close the space between what is a highly technical undertaking and business users, software developers and data integration architects create software such as low-code development platforms to ease the heavy lifting. These platforms use drag-and-drop features and visual components to assign non-expert citizen developers to build their business processes and workflows.
In general, many data integration tools are available to ease access and unify critical data.
Steps on the Data Integration Developer Roadmap
Organizations must combine all their data regardless of where it resides to obtain any value from it. Connecting data sources, shining light on dark data, processing and cleaning data in real-time, and automating analytics environments.
So, where should one begin? The answer lies in building a Data Integration Developer roadmap.
Let’s look at three crucial parts of advice from technology experts:
1. Put Context around Data
The first step in the data integration roadmap is comprehending your data. This includes looking at the volume, breadth, velocity, and integration needs of different data sources. While at it, soft light on dark and unstructured data. Also, limit the number of locations where unstructured data is stored. This saves your organization from legal or regulatory risks.
When you discover and catalog data, the process puts context around it. So, you must comprehend your data's current state and the desired outcome. Hence, map out where you are on your data integration journey and where you want to go.
2. Define Roles and Objectives
Define your objectives and determine the problem you are addressing.
Try asking questions like:
The next step is to start lowering your data story into a framework that addresses risks and the data life cycle.
Lastly, consider data as a complete life cycle from acquisition to insightful analysis. Do not abandon data; ensure it is integrated into the total data picture. If you are only analyzing 10-15% of your data, 85-90% of your business insights are still not being realized.
3. Look for the Right Data Solutions
Research solutions that best fit your business. You must build a system that can anticipate and respond to a changing environment as well as an evolving technology. So make sure you have a culture that adapts to change, thereby allowing you to stay ahead of the evolving data landscape.
Furthermore, a Data Integration Developer roadmap should take into concern emerging technologies. Next-generation data integration platforms will guide organizations to automatically discover additional data types and claim fine access controls for downstream data consumers.
Your data integration roadmap should be driven by state-of-the-art data infrastructure, as objected to an infrastructure you are struggling with.
Summing up
The ultimate goal of a Data Integration Developer roadmap should be deriving data value. If you do not understand what your data means, if it is not correctly benchmarked, or if it is just plain confusing, your business and your customers will be lost.