SAS and Big Data
SAS uses its high-performance analytics infrastructure and statistical software to give many techniques for analysing big data. SAS offers several alternatives for distributed processing. In-database analytics, in-memory analytics, and grid computing are examples. On-premise or cloud deployments are also possible.
For a long time, SAS has been handling complicated enormous data challenges. It bought text analytics provider Teragram a few years ago to improve its strategy of analysing both structured and unstructured data and integrating it for descriptive and predictive modelling. Text analytics is now part of the company's comprehensive analytics infrastructure, and text data is treated as just another data source.
SAS continues to innovate in the field of high-performance analytics to ensure that customer expectations are met. The goal is to solve issues that used to take weeks to solve in days or solve problems that used to take days to solve in minutes instead. The SAS High-Performance Analytics Server, for example, is an in-memory solution that lets you build analytical models with all of your data, not just a part of it. According to SAS, this analysis can include hundreds of variables and millions of documents. The solution runs on EMC Greenplum or Teradata appliances as well as on commodity hardware using Hadoop Distributed File System (HDFS).
Why SAS?
SAS is the market leader in commercial analytics, with a comprehensive set of statistical tools, a user-friendly interface (Enterprise Guide and Miner), and outstanding technical support. It is the most expensive platform in the market but has the most up-to-date statistical functions to justify the cost.
Now, the question arises when there are several less expensive alternates for data mining tools in the market, why should one choose SAS? The answer to this question is explained through the points below:
- SAS is a leader in corporate jobs since most of the big enterprises use this software in their company.
- If one knows SQL, SAS is simple to learn, and even if they don't, the platform's repository contains a stable GUI interface. The platform also includes substantial libraries and documentation, which may be found on the websites of many colleges. SAS training certification, on the other hand, can be costly. But the demand for this skill in the market makes it worth every penny.
- Once it is purchased and deployed, businesses don't have to worry about functioning. An effective customer support team ensures the SAS tools' smooth operation. They assist users with operations, new technique adaption, contractual complexities, etc.
- It has the ability to perform parallel computation as well as data processing in RAM. It can be used for complex simulations and for determining the probability of data distribution.
- SAS offers basic functional graphical capabilities, and customising these plots is a difficult task.
Frequently Asked Questions
What are the advantages of SAS?
The advantages of SAS are as follows:
- The sentence structure in SAS is relatively easy to remember. It is simple to learn and requires no programming knowledge. Anyone can learn it.
- SAS is extremely capable of handling extensive databases with ease.
- SAS is a relatively simple programming language. The debugging procedure is straightforward. It is easy to comprehend.
- SAS's organisation maintains adequate checks because SAS analysis requires a whole organisation. It has built-in customer assistance.
Describe SAS architecture.
The SAS architecture can be broadly divided into three categories:
- Client Tier: The application is installed on a machine where the user is seated in the client layer. It is made up of the elements that are utilised to see the portal and its contents.
- Middle Tier: The middle layer provides a single point of access to enterprise data. This tier's components are responsible for processing all content access.
- Back Tier: The back tier is where data and compute servers are situated, which may include business objects. It's a directory server for businesses.
What is Big Data?
Big data is a collection of structured, semistructured, and unstructured data that may be mined for information and used in machine learning, predictive modelling, and other advanced analytics projects. Big data processing and storage systems, as well as technologies that help with big data analytics, have become commonplace in enterprise data management infrastructures.
What does text analytics mean?
The process of analysing unstructured text, extracting essential information, and translating it into structured data that may be used in various ways is known as text analytics. Text analytics combines machine learning, statistical, and linguistic tools to deduce insights and patterns from vast amounts of unstructured text or text that does not have a specified format.
Conclusion
In this article, we learned about SAS and its relation to Big Data. We saw the characteristics of SAS and where it stands in terms of Big Data. We also answered a fascinating yet fundamental question of why is SAS better than others.
We hope this blog has helped you enhance your knowledge. If you want to learn more, check out our articles on Big Data: Types, Characteristics & 5Vs – Coding Ninjas Blog, Big Data: A guide for beginners – Coding Ninjas Blog, Outlining the Key difference Big Data vs Data Science | Coding Ninjas Blog, Big Data Engineer Salary in Various Locations- Coding Ninjas Blog and Top 100 SQL Problems. Do upvote our blog to help other ninjas grow.
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