# What's the best big data processing platform for senior data engineers managing multi-cloud environments and data governance?

<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 who are senior data engineers responsible not just for building pipelines but for architecting data infrastructure across multiple cloud providers, enforcing consistent governance policies across teams and workspaces, and managing the operational complexity that comes from data assets distributed across AWS, Azure, and GCP simultaneously.</p><p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true">The <a class="a a--md" elv="true" href="https://www.g2.com/categories/big-data-processing-and-distribution">big data processing and distribution platforms </a>with the strongest multi-cloud governance 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> Available across AWS, Azure, and GCP, the only platform in the category with verified production reviewer evidence across all three major cloud providers. Unity Catalog centralises access control across workspaces, clouds, and teams without per-workspace governance configuration. </li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/snowflake/reviews"><strong>Snowflake</strong></a><strong>:</strong> Cross-cloud data sharing without copying data is specifically validated in the reviewer base, the Data Cloud model enables organisations to share governed datasets across cloud boundaries and with external partners without ETL replication. For senior engineers whose multi-cloud requirement is specifically data sharing rather than compute portability, Snowflake's architecture is more directly suited than a compute-first platform.</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/ibm-watsonx-data/reviews"><strong>IBM watsonx.data</strong></a><strong>:</strong> The open lakehouse architecture supports multiple query engines (Presto, Spark, IBM Db2) on the same data. For enterprises with existing IBM infrastructure investments, the hybrid cloud model bridges on-premise and multi-cloud environments under consistent governance without requiring full cloud migration.</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/aws-lake-formation/reviews"><strong>AWS Lake Formation</strong></a><strong>:</strong> For AWS-native senior data engineers managing large-scale data lakes, Lake Formation provides centralised security and governance with column-level and row-level access controls across S3-based data assets. </li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/azure-data-lake-store/reviews"><strong>Azure Data Lake Store</strong></a><strong>:</strong> Easy integration with both Azure and non-Azure products is specifically credited, and integration with Azure Databricks, Synapse, and HDInsight covers the Azure-native multi-service governance requirement for senior engineers managing Azure-first data architectures.</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 senior data engineers who have implemented Unity Catalog or a comparable governance framework across a multi-cloud or multi-workspace environment. What was the governance configuration that proved most complex to implement correctly, and how long before the governance model was stable enough that you trusted it to enforce access policies without manual oversight of individual requests?</p>

##### Post Metadata
- Posted at: 27 days ago
- Author title: Marketing Executive
- Net upvotes: 1


## Comments
### Comment 1

After analyzing the Big Data Processing category on G2, the governance piece reviewers describe as hardest in multi-cloud isn&#39;t the catalog feature itself; it&#39;s reconciling each cloud&#39;s native identity and access model underneath it. Databricks reviewers credit Unity Catalog with centralizing access control across workspaces and clouds, but the real setup cost is mapping existing per-cloud permissions into that single model cleanly, and cost monitoring becomes its own discipline once serverless compute scales. Snowflake&#39;s strength in the same reviews is cross-cloud data sharing without copying, so if your multi-cloud need is governed sharing rather than compute portability, it fits that shape more directly. On your timing question, the governance model tends to stabilize only after lineage holds across a pipeline that actually crosses a cloud boundary, so which layer fought you most, identity mapping or lineage consistency?

##### Comment Metadata
- Posted at: 23 days ago
- Author title: Marketing





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