# Which big data processing platforms eliminate tool-switching between engineering and analytics teams?

<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 cross-team collaboration problem specifically: for organisations where data engineers build pipelines and data analysts or scientists consume the results: Which<a class="a a--md" elv="true" href="https://www.g2.com/categories/big-data-processing-and-distribution"> </a><a class="a a--md" elv="true" href="https://www.g2.com/categories/big-data-processing-and-distribution">Big Data Processing and Distribution</a> platforms most reduce or eliminate that hand-off gap?</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 cross-team collaboration 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> Cclosing the collaboration gap between data engineers and data scientists: engineers build pipelines, and scientists consume the same tables directly in notebooks without data duplication or sync issues. The biggest advantage is having ML and data engineering under one roof, so there is no switching between different tools. The built-in monitoring features and AI Genie enable analytics team members to investigate pipeline failures without needing engineering support.</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/snowflake/reviews"><strong>Snowflake</strong></a><strong>:</strong> The compute-storage separation model allows engineering workloads (data loading, transformation) and analytics workloads (BI queries, dashboards) to run on separate virtual warehouses without one impacting the other — the resource isolation that makes shared platform use practical. </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> The serverless model and shared project model allow engineering and analytics teams to query the same datasets without separate infrastructure provisioning. Connected Sheets allows analytics teams to use Google Sheets as a BigQuery query interface without leaving their familiar environment.</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/kyvos-semantic-layer/reviews"><strong>Kyvos Semantic Layer</strong></a><strong>:</strong> For organisations where the engineering-analytics gap manifests as inconsistent metric definitions across teams, different tools calculating the same KPI differently, Kyvos's semantic layer standardises metric and KPI definitions centrally, making dashboards, analytics tools, notebooks, and AI systems all operate on the same understanding of the business.</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/posit-team/reviews"><strong>Posit Team</strong></a><strong>:</strong> For R and Python data science teams, Posit Workbench provides centralized development environments supporting RStudio, VS Code, and Jupyter alongside Posit Connect for publishing outputs and Posit Package Manager for shared package governance. </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 leads who have measurably reduced tool-switching between their team and the analytics or data science team they support. What was the specific hand-off step that created the most friction before, how was it eliminated, and what unexpected benefit emerged from having both teams in the same platform that you did not anticipate when you made the platform decision?</p>

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


## Comments
### Comment 1

&lt;p&gt;I’d separate sharing the same platform from actually removing the handoff. Databricks gets interesting because engineers and data scientists can work from the same underlying tables without creating another copy for analysis. The test for me would be how often analysts can answer their next question without opening an engineering ticket.&lt;/p&gt;

##### Comment Metadata
- Posted at: about 1 month ago





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