# What are the best Big Data Integration Platforms for data engineers managing extraction and transformation across multiple data sources?

<p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true">I'm researching Big Data Integration Platforms specifically for data engineers juggling extraction and transformation across many different data sources at once. Within <a class="a a--md" elv="true" href="https://www.g2.com/categories/big-data-integration-platforms">Big Data Integration Platforms</a>, <strong>Alteryx</strong>, <strong>Workato</strong>, and <strong>Snowflake</strong> come up most for this kind of multi-source work.</p><ol>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/alteryx/reviews"><strong>Alteryx</strong></a>: known for strong data blending and transformation tools that let engineers pull from many source types into one workflow without heavy custom scripting.</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/workato/reviews"><strong>Workato</strong></a>: built around connecting a wide range of systems together, with automation layered on top of the integration itself.</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/snowflake/reviews"><strong>Snowflake</strong></a>: strong for consolidating data from many sources into a single warehouse, with transformation tools that scale well as source count grows.</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/snaplogic-agentic-integration-and-applied-ai-platform/reviews"><strong>SnapLogic Agentic Integration and Applied AI Platform</strong></a>: built specifically for complex, multi-source integration pipelines with a visual pipeline designer.</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>: handles extraction and transformation at scale, especially for teams already working within the Google Cloud ecosystem.</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/amazon-redshift/reviews"><strong>Amazon Redshift</strong></a>: a common choice for engineers consolidating data from many AWS-adjacent sources into one place for transformation.</li>
</ol><p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true">For data engineers actually managing extraction and transformation across many source systems, which part of the pipeline ends up needing the most manual babysitting, the extraction step, the transformation logic, or just keeping all the source connections stable?</p>

##### Post Metadata
- Posted at: 4 days ago
- Net upvotes: 1


## Comments
### Comment 1

&lt;p&gt;Keeping source connections stable would probably require the most ongoing babysitting. Transformation logic can be tested and versioned, but upstream APIs, schemas, credentials, and rate limits can change without warning. I’d want strong connector monitoring and schema-drift alerts so engineers know exactly which source broke before downstream transformations start failing.&lt;/p&gt;

##### Comment Metadata
- Posted at: 3 days ago





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