# What Are the Highest Rated ETL Tools for Teams Managing 100+ Active Data Pipelines?

<p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true">Hey All! Within <a class="a a--md" elv="true" href="https://www.g2.com/categories/etl-tools">ETL Tools</a>, a couple of platforms carry reviewer evidence specifically describing reliability at a real operational scale.</p><ul>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/fivetran/reviews"><strong>Fivetran</strong></a>: reviewers describe pulling data reliably from systems like Salesforce, Jira, and NetSuite into a warehouse, with connectors that rarely go down and issues resolved with a simple historical resync when they do, though the consumption-based pricing draws real criticism for being hard to predict.</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/databricks/reviews"><strong>Databricks</strong></a>: reviewers at large organizations point to processing information at scale while eliminating data silos, with centralized governance reducing the friction of connecting many microservices and pipelines into one reliable source of truth.</li>
</ul><p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true">Would be useful to hear from data teams running this many pipelines directly here: did pricing complexity end up being a bigger operational headache than the actual pipeline reliability?</p><p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true"></p><p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true"></p>

##### Post Metadata
- Posted at: 16 days ago
- Author title: Marketing
- Net upvotes: 1


## Comments
### Comment 1

&lt;p&gt;At 100+ pipelines, I’d measure operational effort per pipeline rather than pipeline count alone. Connector reliability matters, but so does how quickly the team can identify which upstream change caused a failure and recover without a full reload. If every schema change becomes an investigation, the platform can scale technically while the data team still can’t.&lt;/p&gt;

##### Comment Metadata
- Posted at: 15 days ago
- Author title: Writer





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