# What is the best software for integrating big data with analytics platforms?

<p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true">Hi everyone! I’m exploring tools that make it easier to bring together big data from multiple sources and connect it seamlessly with analytics platforms. The goal is to enable real-time insights, improve reporting accuracy, and reduce the engineering effort needed to manage complex pipelines. Based on reviews in the <a class="a a--md" elv="true" href="https://www.g2.com/categories/big-data-integration-platforms">Big Data Integration Platforms category</a>, here are a few strong contenders:</p><p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true"><strong>Google Cloud BigQuery – Best for Analytics-Ready Integration</strong></p><p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true"><a class="a a--md" elv="true" href="https://www.g2.com/products/google-cloud-bigquery/reviews">Google Cloud BigQuery</a> is more than a warehouse—it integrates with a wide range of data pipelines and analytics tools. Its serverless architecture and ability to query massive datasets in real time make it a go-to for teams that need analytics-ready data without extensive infrastructure management.</p><p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true"><strong>Snowflake – Best for Cross-Cloud Data Sharing</strong></p><p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true"><a class="a a--md" elv="true" href="https://www.g2.com/products/snowflake/reviews">Snowflake</a> provides a highly scalable data cloud that makes integrating with BI and analytics platforms straightforward. Features like secure data sharing and support for structured and semi-structured data help organizations collaborate across departments and even with external partners.</p><p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true"><strong>Azure Data Factory – Best for Orchestrating Complex Data Pipelines</strong></p><p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true"><a class="a a--md" elv="true" href="https://www.g2.com/products/azure-data-factory/reviews">Azure Data Factory</a> excels at connecting diverse data sources and preparing them for analytics. It supports both ETL and ELT workflows, integrates well with Microsoft’s analytics stack, and offers hybrid deployment options for enterprises operating across on-premises and cloud environments.</p><p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true"><strong>AWS Glue – Best for Automated Data Preparation</strong></p><p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true"><a class="a a--md" elv="true" href="https://www.g2.com/products/aws-glue/reviews">AWS Glue</a> simplifies the process of preparing data for analytics by providing serverless ETL with built-in transformations. It integrates directly with Amazon Redshift, Athena, and third-party BI tools, making it an efficient choice for teams already in the AWS ecosystem.</p><p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true"><strong>IBM StreamSets – Best for Streaming Data to Analytics Platforms</strong></p><p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true"><a class="a a--md" elv="true" href="https://www.g2.com/products/ibm-streamsets/reviews">IBM StreamSets</a> focuses on real-time data integration, enabling analytics platforms to process continuous streams without delay. Its DataOps functionality ensures data quality and governance while maintaining visibility across complex pipelines.</p><p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true">If your team has connected big data pipelines to analytics platforms, which solution did you choose? Did it help speed up reporting and reduce time-to-insight, or did you run into scalability challenges?</p>

##### Post Metadata
- Posted at: 9 months ago
- Net upvotes: 1


## Comments
### Comment 1

&lt;p&gt;I’ve noticed &lt;a href=&quot;https://www.g2.com/products/snowflake/reviews&quot; rel=&quot;noopener noreferrer&quot; target=&quot;_blank&quot;&gt;Snowflake&lt;/a&gt; and &lt;a href=&quot;https://www.g2.com/products/google-cloud-bigquery/reviews&quot; rel=&quot;noopener noreferrer&quot; target=&quot;_blank&quot;&gt;BigQuery&lt;/a&gt; are often compared for analytics integration, but &lt;a href=&quot;https://www.g2.com/products/azure-data-factory/reviews&quot; rel=&quot;noopener noreferrer&quot; target=&quot;_blank&quot;&gt;Azure Data Factory&lt;/a&gt; seems to shine when orchestration is the priority. I&#39;m curious—has anyone used &lt;a href=&quot;https://www.g2.com/products/ibm-streamsets/reviews&quot; rel=&quot;noopener noreferrer&quot; target=&quot;_blank&quot;&gt;IBM StreamSets&lt;/a&gt; specifically to feed streaming data into BI dashboards?&lt;/p&gt;

##### Comment Metadata
- Posted at: 9 months ago





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