Combining data from different sources—databases, SaaS apps, on-prem systems, and cloud platforms—is a critical step for creating a single source of truth. Without the right tools, teams risk inconsistent reporting and incomplete insights. Based on highly rated solutions in the Big Data Integration Platforms category, here are some of the top options:
Workato – Best for SaaS and Application Integrations
Workato helps unify data across apps, databases, and cloud platforms through automation-driven pipelines. Its low-code recipes allow teams to blend multiple data sources while applying validation rules, making it a strong fit for business and IT teams working together.
Azure Data Factory – Best for Enterprise-Scale Orchestration
Azure Data Factory is widely used for orchestrating ETL and ELT pipelines across on-prem and cloud sources. It supports a large library of connectors, helping enterprises combine structured and unstructured data into analytics-ready pipelines.
IBM StreamSets – Best for Complex, Multi-Source Pipelines
IBM StreamSets enables organizations to merge streaming and batch data from many systems. Its DataOps approach ensures data is monitored, governed, and processed in real time, which is especially valuable when combining large-scale, multi-source data flows.
AWS Glue – Best for Schema Matching and Transformation
AWS Glue simplifies the process of combining data from different sources by automatically detecting schemas and storing metadata in its catalog. With built-in transformations, it ensures that data from multiple origins is harmonized before being loaded into analytics platforms.
5X – Best for Modern Data Stack Integration
5X provides a managed framework that helps businesses stitch together multiple tools in their modern data stack. It supports integrations across warehouses, BI tools, and pipelines, making it a flexible option for fast-growing organizations.
Have you used any of these platforms to combine data from diverse sources? Which features mattered most to your team—automation, governance, or ease of scaling?
##### Post Metadata - Posted at: 8 months ago - Net upvotes: 1 ## Comments ### Comment 1 <p>I’ve seen <a href="https://www.g2.com/products/azure-data-factory/reviews" rel="noopener noreferrer" target="_blank">Azure Data Factory</a> shine for enterprise-scale integrations, while <a href="https://www.g2.com/products/workato/reviews" rel="noopener noreferrer" target="_blank">Workato</a> feels lighter and faster to deploy for SaaS-heavy teams. Has anyone here tested <a href="https://www.g2.com/products/5x/reviews" rel="noopener noreferrer" target="_blank">5X</a> to manage a modern data stack that pulls from both operational and analytics sources?</p> ##### Comment Metadata - Posted at: 8 months ago ## Related discussions - [How well does Trello scale into a larger team?](https://www.g2.com/discussions/1-how-well-does-trello-scale-into-a-larger-team) - Posted at: almost 13 years ago - Comments: 6 - [Can we please add a new section](https://www.g2.com/discussions/2-can-we-please-add-a-new-section) - Posted at: almost 13 years ago - Comments: 0 - [Quantifiable benefits from implementing your CRM](https://www.g2.com/discussions/quantifiable-benefits-from-implementing-your-crm) - Posted at: almost 13 years ago - Comments: 4