6 Reasons to Re-Architect Your SAS Analytics for Large Datasets

Lynn Orlando. September 22, 2020
6 Reasons to Re-Architect Your SAS Analytics for Large Datasets

The world is getting more digital every day. The amount of data generated across all industries is exploding. This trend means that we’re producing a huge amount of data every minute. Collecting, storing, managing, and analyzing these copious amounts of data is a major challenge. SAS analytics is a great tool to help in this regard. However, it faces major performance issues while crunching large datasets. Plus, SAS licensing fees can be expensive for customers, and poorly utilized SAS cores makes the economics look worse.

This article looks at these problems and lists six reasons why businesses should consider re-architecting their SAS analytics for large datasets.

Analyzing Large Datasets Causes Major Issues

For many businesses and organizations, getting fast results from their SAS programs when working with large datasets is an operational requirement. They’re looking for key insights from the tons of data collected to help reduce costs, improve customer satisfaction, increase revenues, etc. Businesses want to derive competitive advantages from this data, and they want these results as fast as possible so that they can make decisions quickly. However, many SAS users experience multiple challenges when working with large datasets with millions or billions of data points.

Legacy tools require an enormous amount of time to analyze all of this data. Their inefficient processes have performance issues when dealing with large datasets. Also, the large volumes of data cause storage shortages that legacy systems cannot handle. Additionally, they face slow batch jobs and frequent data load failures, which makes it challenging to create reports with updated data. These issues not only cause delays and downtimes but also increase processing and storage costs.

Thus, it’s imperative for analysts and data scientists to understand how SAS works best with large datasets. They must be able to identify processing bottlenecks and resolve the issues. They need to make their code more efficient and seamlessly scale the storage on the cloud. The process efficiency is about how well they can manage, manipulate, and move the data around. Each company’s requirement is different, so they need to customize the solution for their specific needs and then then consider learning how to re-architect their SAS analytics. Why is re-architecting SAS analytics a key requirement when working with large datasets and big data? Keep reading!

Reasons to Re-Architect Your SAS Analytics

Let’s face it: change can be difficult, and re-architecting involves change. However, when appreciable business benefits result from that change, the decision to embark upon the new journey becomes easier to make, especially with these six compelling reasons.

1. Slow Processing and Throughput
An inefficient SAS analytics process working on large datasets will take a lot of time. The CPU time is wasted on operations that aren’t necessary or are redundant. Increasing processing power will help reduce wasted time, but it would still mean running inefficient processes, so you should re-architect your SAS analytics to improve the total program time. For example, one way to reduce CPU time is to send the query to the database server. This will execute the query on the database server and send only the required data instead of sending the whole dataset across the network and then executing the query. A similar case is when you need to subset a large dataset based on specific criteria. Instead of sending the entire database, you can use WHERE subsetting, which occurs before the data passes to SAS for processing. The WHERE statement in most of the cases proves to be efficient and faster in performance than an IF statement. A reduced CPU and program time help generate the analysis output quicker. Faster output is vital to gaining key insights in a shorter time and making faster decisions.

2. High Total Memory Usage
Processing large datasets inefficiently is a recipe for disaster in terms of memory usage. You have only a limited amount of RAM. Working with lots of data can easily use up the available system memory. You can try to configure your memory options to set the amount of memory used per process. However, though allocating more memory may improve performance, it will also impact other processes needed to perform analysis. You can upgrade your hardware or rent a cloud service like Amazon Web Services that offers machines with tens of gigabytes of RAM. If you re-architect your SAS, you will be able to reduce the amount of memory required for the execution. If you compress the large dataset, you may be able to solve memory issues because the dataset becomes smaller. A compressed dataset reduces the amount of data passed to SAS, which minimizes the data movement in memory. Thus, you can re-architect your SAS analytics to run the large dataset without running out of memory in the same configuration and device.

3. Non-Scalable Application
Scalability is one of the most important requirements for any application or analytics operation. As the dataset gets larger, you’ll observe scalability issues, such as sluggish performance or service outages. Thus, analyzing large datasets will take an eternity, or not happen at all. The ideal process scales up seamlessly and automatically whenever there is a huge spike in resource demand. To improve the scalability of analyzing your large dataset, you should re-architect your SAS analytics.

4. High Number of Input/Output (I/O) Operations
For very large datasets, the time it takes to read and write the data to disk is often the primary contributor to processing delays. Data input/output (I/O) is frequently the bottleneck for every performance-critical big data analytics operation. Thus, I/O optimization is a critical task to ensure smooth and efficient big data analysis. Once you speed up your data loading for large datasets, you’ll be able to generate results faster for all analyses and projects. By compressing the large dataset into a smaller size, you’ll require fewer I/O operations. Reducing I/O operations enables your SAS analytics to read the data from disk faster and, thereby, convert it into the format needed for processing much faster. You can also look to leverage a massively parallel processing (MPP) platform to input the data in parallel processes. However, this won’t reduce the number of I/O operations. If you re-architect your SAS analytics, you might be able to optimize the I/O operations.

5. Running Out of Storage Space
With the huge amount of data that’s continuously produced, businesses’ databases are becoming overwhelmed. To analyze large datasets, you need to store the data somewhere first. But it’s a common issue to keep running out of space if you use legacy storage solutions. A modern, ideal solution can seamlessly scale the storage on the cloud. Thus, data scaling techniques have become extremely vital to keep up with this overflow of data.

6. High Network Latency
In business intelligence, data latency is how long it takes for an application to retrieve source data from a data warehouse to perform analytics on it. Having low network latency allows for fast, predictable, and deterministic response time to big data analytics. The bottleneck can be the bandwidth limit of the network you’re using. Investing in direct data communication lines might be unrealistic and extremely expensive. Subsetting, summarizing, or compressing the data are other ways to ensure low latency. That’s because subsetting columns and rows or pre-summarizing the data on the database management system (DBMS) side ensures less data movement over the network to the SAS platform. You can also re-architect your SAS analytics to enable smaller datasets over the same network bandwidth to ensure low data latency.

Re-Architect SAS Analytics to Handle Large Datasets

In this digital age of big data, being able to manage and analyze large datasets efficiently is a competitive advantage. Organizations struggle with slow performance, low memory, constantly running out of storage, and low scalability. This means they aren’t able to extract maximum value from their expensive and resource-thirsty data analysis operations.

Thus, organizations should consider making their processes and tools ready for large datasets before they run into these challenges. If you’re ready to take steps toward re-architecting, there are various resources online about how to do this. You can also join our webinar to learn the basics and a few advanced techniques on how to re-architect your SAS analytics for large datasets.

This post was written by Aditya Khanduri. Aditya currently handles product and growth at Cryptio.co, and he’s also built a couple of B2B products. He’s proficient in data analysis with Python and has worked with multiple startups in the blockchain and artificial intelligence sector.

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