# Which low-code machine learning tools connect well with existing data warehouses like Snowflake or BigQuery without needing a separate ETL pipeline?

<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 mapping this for a research note, since the integration story is often the thing that decides adoption. Pulling the<a class="a a--md" elv="true" href="https://www.g2.com/categories/low-code-machine-learning-platforms"> </a><a class="a a--md" elv="true" href="https://www.g2.com/categories/low-code-machine-learning-platforms">low-code machine learning platforms</a> reviews that actually name warehouses or heavy data integration, these came up:</p><ul>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/dataiku"><strong>Dataiku</strong></a> - Reviewers specifically mention Snowflake integration and easy setup, plus visual flows and pre-built connectors that get a live model on client data quickly.</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/gemini-enterprise-agent-platform"><strong>Gemini Enterprise Agent Platform</strong></a> - GCP reviewers repeatedly cite tight BigQuery and Cloud Storage integration that makes data handling efficient without extra pipeline work.</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/alteryx"><strong>Alteryx</strong></a> - The most-cited tool for replacing ETL work, with reviewers describing extracting, cleansing, and blending data across sources and connecting to many services with low code.</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/sas-sas-viya"><strong>SAS Viya</strong></a> - Reviewers connect it to databases like Oracle and pull from multiple warehouses for analysis, with open integration across Python, R, and REST APIs.</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/qlik-predict"><strong>Qlik Predict</strong></a> - A logistics reviewer highlighted datalake automation and real-time streaming integration from different platforms with a no-code approach.</li>
</ul><p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true">If you've wired one of these into Snowflake or BigQuery, did it read and write cleanly enough to skip a separate ETL layer, or did you still need a pipeline tool in front of it? And how did governance on the warehouse side hold up once the ML tool was connected?</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: 18 days ago
- Net upvotes: 1


## Comments
### Comment 1

&lt;p&gt;Dataiku reviewers praise its Snowflake pushdown and native connectors, allowing transforms within the warehouse, nearly skipping an ETL layer. Reads are smooth, and planning for write-back and scheduling benefits from some extra configuration.&lt;/p&gt;

##### Comment Metadata
- Posted at: 3 days ago
- Author title: Marketer and Business Owner





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