# What's the best big data processing platform for data engineering teams scaling large-scale processing workloads?

<p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true">Looking for input from G2 reviewers on the scaling problem specifically: Where the <a class="a a--md" elv="true" href="https://www.g2.com/categories/big-data-processing-and-distribution">big data processing and distribution platform</a> must handle that growth without requiring proportional increases in engineering effort to manage infrastructure, tune performance, or re-architect pipelines.</p><p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true">The platforms with the strongest large-scale processing evidence:</p><ul>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/databricks/reviews"><strong>Databricks</strong></a><strong>:</strong> Trusted by adidas, AT&amp;T, Bayer, Mastercard, and 70% of the Fortune 500. The serverless compute option is specifically credited with avoiding paying for idle infrastructure through auto-scaling.</li>
<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> Petabyte-scale, serverless model, no cluster to provision, no auto-scaling to configure, compute scales automatically with query complexity. </li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/snowflake/reviews"><strong>Snowflake</strong></a><strong>:</strong> Elastic scaling of virtual warehouses, allocating extra compute for demanding workloads and scaling back once complete is the most specifically reviewed scaling feature. </li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/amazon-emr/reviews"><strong>Amazon EMR</strong></a><strong>:</strong> Managed Hadoop, Spark, and Hive clusters with EC2 Auto Scaling for handling large-scale processing workloads without manual cluster management. </li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/teradata-autonomous-knowledge-platform/reviews"><strong>Teradata Autonomous Knowledge Platform</strong></a><strong>:</strong> Extreme performance for processing large data volumes is the most consistently cited reviewer strength. The massively parallel processing architecture is validated at enterprise scale across financial services and information technology organisations. </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 value input from data engineering teams that have scaled a Databricks, Snowflake, or BigQuery deployment from initial deployment to significantly larger workload volumes. What was the first scaling bottleneck you hit that required an architectural change rather than just more compute, and how did the platform's tooling help or hinder diagnosing and resolving it?</p>

##### Post Metadata
- Posted at: 14 days ago
- Author title: Marketing Executive
- Net upvotes: 1


## Comments
### Comment 1

&lt;p&gt;At larger scale, I’d look beyond raw compute and ask how much operational work accompanies each jump in volume. BigQuery’s serverless model is attractive for that reason, while Databricks gives teams a broader environment for complex data engineering workloads. The platform that scales with the least additional engineering attention can be more valuable than the one that simply processes fastest.&lt;/p&gt;

##### Comment Metadata
- Posted at: 14 days ago
- Author title: Writer





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