# Which wide column solutions offer the best scalability and storage efficiency?

<p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true">Hi G2 community! So, scalability is the whole reason to reach for a wide column database, but storage efficiency (how much hardware you burn to get there) varies a lot. I've been looking at the <a class="a a--md" elv="true" href="https://www.g2.com/categories/wide-column-database">Wide Column Database category</a> for what reviewers say about scaling behavior and how efficiently each uses the underlying resources.</p><ul>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/scylladb/reviews"><strong>ScyllaDB</strong></a><strong>:</strong> Reviewers repeatedly credit its shard-per-core architecture with efficient hardware use, scaling predictably as cores are added and needing fewer nodes for the same load. Getting there depends on solid capacity planning.</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/hbase/reviews"><strong>HBase</strong></a><strong>:</strong> Reviewers highlight linear and modular scaling on commodity hardware for billions of rows, with memory compression called out as a storage-efficiency plus. It expects the Hadoop/HDFS stack underneath.</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/cassandra/reviews"><strong>Cassandra</strong></a><strong>:</strong> Horizontal scaling with no single point of failure is its hallmark, and reviewers scale it across large clusters. The efficiency caveat they raise is high per-node RAM needs, so scaling isn't cheap in terms of resources.</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/amazon-keyspaces/reviews"><strong>Amazon Keyspaces</strong></a><strong>:</strong> Serverless auto-scaling with virtually unlimited throughput and storage means you don't manage capacity yourself, which reviewers like for elastic scaling, at a cost premium.</li>
</ul><p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true">When you scaled one of these, what actually constrained you first: node count and RAM, storage growth, or the operational effort of expanding the cluster? And which stayed efficient as it grew?</p>

##### Post Metadata
- Posted at: 6 days ago
- Author title: Marketing
- Net upvotes: 1


## Comments
### Comment 1

&lt;p&gt;With Cassandra, the first thing that bit us wasn&#39;t node count, it was RAM. We underestimated how hungry it gets per node and ended up over-provisioning just to stay stable. ScyllaDB&#39;s story on hardware efficiency is genuinely interesting but I haven&#39;t personally run it at the scale where that difference would be obvious. Would be curious to hear from anyone who&#39;s done a direct comparison on the same hardware.&lt;/p&gt;

##### Comment Metadata
- Posted at: 4 days ago
- Author title: SEO Content Specialist





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