# What statistical analysis platform handles large datasets and complex computations without slowing down or running out of memory during a long analysis run?

<p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true">The pain point here is the long run that stalls or runs out of memory, so I pulled reviews specifically about performance and scale under load.</p><ul>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/sas-sas-viya/reviews"><strong>SAS Viya</strong></a>: Reviewers credit its in-memory CAS processing and cloud-native, scalable architecture for handling large data volumes across environments. The honest counterweight is that it's resource-intensive and demands serious CPU, memory and setup effort.</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/stata/reviews"><strong>Stata</strong></a>: Reviewers say it analyzes heavy data that Excel can't, with a variety of built-in methods, but the recurring limit is that it holds one dataset in memory at a time.</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/minitab-statistical-software/reviews"><strong>Minitab Statistical Software</strong></a>: Reviewers report fast processing even on larger datasets for its class of analyses, though it's aimed more at structured statistical work than massive data.</li>
</ul><p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true">At what scale did your tool start to choke, and did the fix come from the software or from throwing more hardware at it? For genuinely large data, is a platform like SAS Viya worth its heavier footprint over a desktop tool?</p>

##### Post Metadata
- Posted at: about 2 months ago
- Net upvotes: 1


## Comments
### Comment 1

&lt;p&gt;&lt;span style=&quot;background-color: transparent; color: rgb(0, 0, 0);&quot;&gt;For truly large data, the architecture split is the answer: in-memory distributed processing scales where a single-machine desktop tool eventually hits a wall that no hardware upgrade fully fixes. The heavier footprint of a platform built for this is the cost of not choking on a long run, so it&#39;s worth it precisely when the run is the bottleneck. If a tool holds one dataset in memory at a time, that&#39;s your ceiling, and it arrives sooner than the feature list suggests.&lt;/span&gt;&lt;/p&gt;

##### Comment Metadata
- Posted at: about 2 months ago
- Author title: Marketing





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