Constellation ShortList™ Data Integration and Transformation for Cloud-Based Analytical Data Platforms

This ShortList evaluates platforms that enable enterprises to deliver decision- grade data into cloud-based analytical data platforms and the operational systems where decisions are executed. These solutions move beyond traditional batch-oriented pipelines by combining data integration, AI-ready transformation, near real-time orchestration, and activation to support analytics, AI, and operational decisioning at scale. 

The focus is on platforms that help data and AI leaders reduce time-to-decision by:

• Integrating data from operational systems, SaaS applications, and onprem sources into cloud data warehouses, lakes, and lakehouses using batch, micro-batch, and CDC-based ingestion.
• Transforming raw data into decision-ready datasets, semantic definitions, and AI-ready data using governed, reusable logic and consistent business meaning.
• Orchestrating and monitoring end-to-end data delivery with automation, reliability, and freshness controls so data can be trusted at the moment decisions are made. 
• Activating analytics and AI outputs into downstream systems through reverse ETL, write-back, and event-driven or near real-time synchronization. 

Compared with last year’s focus on modern ELT for cloud analytics, this ShortList reflects a shift toward decision-grade data delivery, where semantic consistency, orchestration, and activation adding three vendors: Informatica, Qlik (Talend), and Boomi (Rivery).

Threshold Criteria

Constellation considers the following criteria for these solutions:

Broad, high-performance connectivity to data from a wide range of cloud-native and legacy on-prem sources, using pre-built and extensible connectors. 

• Low-latency and CDC-based ingestion supporting batch, micro-batch, and near real-time data delivery for analytics, AI, and operational decisioning. 
• ELT-first, cloud-native transformation within leading cloud data warehouse and lakehouse platforms, with support for ETL-style processing where required. 
• Governed transformation and semantic consistency, enabling reusable business logic, metrics, and definitions to be shared across analytics and AI workloads. 
• Data-centric orchestration and dependency management, coordinating ingestion, transformation, and delivery workflows based on data readiness, freshness, and downstream impact. 
• Activation through synchronization, write-back, and or reverse ETL, enabling analytics and AI outputs to be delivered into downstream systems and services.
• Operational reliability and observability, including monitoring, alerting, and freshness controls that ensure data can be trusted at the moment decisions are made. 
• Governance and operations support, including version control, testing, logging, auditability, and environment management to promote reuse and compliance.
• Cloud-first delivery with hybrid support, offering managed services or cloud-native deployment across AWS, Azure, and Google Cloud, with hybrid connectivity where required. 
• AI-assisted productivity and automation features that support developer efficiency, optimization, or anomaly detection in production or controlled preview. 
• Automated delivery of high-context, vector-ready data and business semantics to power autonomous AI agents and low-latency RAG workflows 
• Demonstrated capabilities or active roadmaps for self-healing orchestration and automated schema evolution to maintain data continuity.