# 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: 3 months ago
- Author title: Marketing Executive
- Net upvotes: 1


## Comments
### Comment 1

&lt;p&gt;The original choice starts feeling restrictive when scaling stops being automatic and the team has to spend more time tuning infrastructure than using the data. That’s why BigQuery and Snowflake feel safer for long-term growth, while Redshift or Vertica make more sense once you have the team to manage that complexity.&lt;/p&gt;

##### Comment Metadata
- Posted at: about 1 month ago
- Author title: Marketer



### Comment 2

&lt;p&gt;The point about additional teams competing for resources answers part of what I was getting at. It makes me wonder whether “scaling smoothly” should include organizational growth as much as data growth. Snowflake’s separation helps there, but does BigQuery remain equally hands-off once different teams need their own cost controls, workload priorities, and governance?&lt;/p&gt;

##### Comment Metadata
- Posted at: about 1 month ago
- Author title: Writer



### Comment 3

&lt;p&gt;To your question about when the original choice starts feeling like a constraint, my read is that it&#39;s usually not a data volume threshold at all. It&#39;s the point where a second or third team starts querying and workloads begin competing. Snowflake separating compute from storage is interesting on exactly that, since it means a growing team can be handled by adding compute independently rather than by revisiting the platform decision.&lt;/p&gt;

##### Comment Metadata
- Posted at: about 1 month ago
- Author title: Tech Consultant



### Comment 4

&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: 3 months ago
- Author title: Writer





## Related discussions
- [How well does Trello scale into a larger team?](https://www.g2.com/discussions/1-how-well-does-trello-scale-into-a-larger-team)
  - Posted at: over 13 years ago
  - Comments: 6
- [Can we please add a new section](https://www.g2.com/discussions/2-can-we-please-add-a-new-section)
  - Posted at: over 13 years ago
  - Comments: 0
- [Quantifiable benefits from implementing your CRM](https://www.g2.com/discussions/quantifiable-benefits-from-implementing-your-crm)
  - Posted at: over 13 years ago
  - Comments: 4