Big data integration platforms facilitate the integration and analysis of large-scale data across cloud applications and databases, helping companies manage and utilize enormous volumes of data collected from IoT endpoints, applications, and communications by creating structured pipelines that connect big data processing outputs to downstream systems.
Core Capabilities of Big Data Integration Platforms
To qualify for inclusion in the Big Data Integration category, a product must:
- Integrate big data processing data to external sources
- Ingest and distribute large sets of homogenous and heterogeneous data
- Create a structured pipeline for big data management processes
Common Use Cases for Big Data Integration Platforms
Data engineering and IT teams use big data integration platforms to connect large-scale data environments with business applications and analytics systems. Common use cases include:
- Integrating processed big data clusters with cloud applications and databases for downstream use
- Simplifying the management of high-volume IoT and application data across distributed environments
- Building structured data pipelines that enable consistent, reliable access to big data insights across the organization
How Big Data Integration Platforms Differ from Other Tools
Big data integration platforms typically require big data to have been processed prior to integration, working in conjunction with big data processing and distribution software rather than replacing it. While some platforms provide stream analytics capabilities, their primary focus is on data management and integration pipelines rather than real-time analytical processing.
Insights from G2 on Big Data Integration Platforms
Based on category trends on G2, pipeline flexibility and broad connector support for cloud applications and databases as standout capabilities. Improved data accessibility across systems and reduced integration complexity stand out as primary outcomes of adoption.
