# Which columnar databases scale smoothly for analytics teams growing from startup to enterprise-level data volumes?

<p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true">I've been tracking how <a class="a a--md" elv="true" href="https://www.g2.com/categories/columnar-databases">columnar databases</a> handle growth for a piece on scaling analytics infrastructure, and which columnar databases scale smoothly for analytics teams growing from startup to enterprise-level data volumes without re-platforming every 18 months is the part most vendor comparisons gloss over. The re-platforming cost shows up late but hurts badly.</p><ul>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/google-cloud-bigquery/reviews"><strong>Google Cloud BigQuery</strong></a><strong>:</strong> Free at small scale, with the same serverless platform scaling to handle petabyte-level transformations instantly as volume grows. No infrastructure decisions to revisit at any stage.</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/snowflake/reviews"><strong>Snowflake</strong></a><strong>:</strong> Compute and storage scale independently, so data volume can grow without immediately scaling compute costs with it. Performance stays stable as volume increases significantly over time.</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/amazon-redshift/reviews"><strong>Amazon Redshift</strong></a><strong>:</strong> Handles enterprise-scale workloads well within AWS, but node management complexity and concurrency tuning requirements surface more visibly as the jump from early-stage to large-scale happens.</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/rocket-vertica/reviews"><strong>Rocket Vertica</strong></a><strong>:</strong> Adding compute clusters scales to handle huge tasks, with deployment across all major clouds and on-premises. Expensive to maintain at scale and complex to implement, so better suited to teams past the early growth phase with DBA capacity in-house.</li>
</ul><p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true">For teams that have grown through multiple data volume stages on the same platform, at what point did the original choice start to feel like a constraint?</p>

##### Post Metadata
- Posted at: about 2 months ago
- Author title: Marketing Executive
- Net upvotes: 1


## Comments
### Comment 1

&lt;p&gt;BigQuery and Snowflake both scale well, but the experience feels different once usage grows. One hides complexity, the other gives you more knobs over time. At what point do you think teams usually start feeling the need to rethink their original choice?&lt;/p&gt;

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





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