ETL tools for AI are software that collect, clean and organise data so AI can use it easily.
Now, the explosion of enterprise AI deployment has exposed a challenge: getting data ready for machine learning (ML) models at scale.
This is where ETL comes in.
Organisations worldwide are discovering that the infrastructure enabling AI matters as much as the algorithms themselves – driving attention to data integration platforms that have long operated in the background of IT operations.
These tools, traditionally tasked with extracting, transforming and loading information between systems, now serve as foundational architecture for ML operations.
The platforms address twin imperatives of preparing vast datasets for AI models while maintaining governance standards demanded by regulators.
The market spans cloud-native services from hyperscalers, legacy enterprise platforms undergoing significant modernisation – and specialist vendors deploying autonomous capabilities to address mounting data engineering workloads.
10: Matillion
Company: Matillion
CEO: Matthew Scullion
Specialisation: Data Productivity Cloud driving cloud data transformation using Agentic AI (Maia)
Matillion provides the Data Productivity Cloud, led by Matthew Scullion.
The company is pioneering Agentic AI with its framework Maia, described as a team of virtual data engineers that automates up to 80% of data engineering tasks.
This addresses what Scullion calls the productivity crisis, delivering business-ready data for Gen AI.
Matillion’s strategy seeks to provide an autonomous data workforce without scaling human headcount.
The company takes a critical stance against pure Zero-ETL models, arguing they merely shift complexity to the query point.
9: Alteryx
Company: Clearlake Capital Group and Insight Partners
CEO: Andy MacMillan
Specialisation: Code-free analytics automation and data science democratisation for business users
Alteryx specialises in analytics automation, targeting business users with a code-free platform.
Andy MacMillan leads the company, which allows non-coders to perform complex ETL and predictive modelling through a visual interface.
Clearlake Capital Group and Insight Partners acquired the business in March 2024 to accelerate innovation focused on AI initiatives.
The platform functions as an accelerator during data preparation phases of ML (ML) operations, enabling citizen data scientists to create analytical datasets before models are operationalised on infrastructure built for scale.
8: Talend (Qlik)
Company: Qlik (Acquired Talend)
CEO: Mike Capone
Specialisation: Cloud data integration and quality focused on robust data health and governance
Talend now operates as Qlik Talend Cloud following its acquisition.
Executives including Mike Capone promote the platform’s focus on data health, quality and governance.
The merger is a strategic move to create a unified data value chain.
Qlik is targeting legacy data management users.
The company offers dedicated conversion tooling and positions itself as an alternative for organisations seeking to avoid vendor lock-in.
7: SAS Data Management
Company: SAS Institute
CEO: Jim Goodnight
Specialisation: Comprehensive data quality, governance and analytics platform modernisation services
SAS Institute provides data management tools focusing on consistency, quality and governance through ETL workflows.
Jim Goodnight’s company has built a reputation around SAS Data Governance, which addresses regulatory compliance demands in AI.
The market is demonstrating a transition, however.
Organisations are migrating legacy SAS workloads to cloud-native platforms including AWS, Azure, Databricks and Snowflake, seeking lower operational costs and faster performance.
This dynamic is reshaping SAS’s role from primary execution engine to governance authority across disparate cloud environments.
6: Oracle Data Integrator
Company: Oracle
CEOs: Mike Sicilia and Clay Magouyrk
Specialisation: High-performance ELT/Zero-ETL integration leveraging OCI and Autonomous Lakehouse
Oracle Data Integrator serves as a cornerstone of the company’s data integration strategy.
The platform is now positioned within Oracle’s AI Data Platform strategy, developing the Zero-ETL paradigm that eliminates complex intermediate staging steps.
Zero-ETL enables direct connections to mission-critical business application data, moving it to the Oracle Autonomous AI Lakehouse.
This proves powerful because Oracle often controls the source systems, effectively creating vendor lock-in through superior data velocity within its ecosystem.
5: Informatica PowerCenter and Cloud Data Integration
Company: Informatica
CEO: Amit Walia
Specialisation: Enterprise data management, mastering and migration powered by CLAIRE AI
Informatica is transitioning from its legacy PowerCenter to the cloud-native Intelligent Data Management Cloud under Amit Walia.
The platform uses the CLAIRE AI engine to automate source recommendations, governance and data quality, creating trusted customer profiles and reducing duplicate entries.
Support for PowerCenter 10.5 ends in March 2026, compelling enterprises to expedite their cloud shift.
The company emphasises data trust for regulated industries whose AI projects demand stringent quality controls, supporting automation-led cloud migrations in partnership with Snowflake and Deloitte’s Migration Factory.
4: IBM InfoSphere DataStage
Company: IBM
CEO: Arvind Krishna
Specialisation: High-performance, scalable ETL/ELT integrated into the hybrid watsonx platform
IBM DataStage, an ETL solution since 1997, is now strategically integrated into the hybrid cloud watsonx.data ecosystem under Arvind Krishna.
The platform features a remote engine architecture, allowing a cloud-based control panel for pipeline design to operate separately from a secure data panel for execution.
This enables pipelines to execute wherever data resides – on-premises or in hybrid cloud – optimising for cost by minimising data egress.
The capability addresses challenges of cloud adoption, allowing organisations to adopt advanced AI tools without complete data migration.
3: Google Cloud Dataflow
Company: Google Cloud
CEO: Thomas Kurian (Google Cloud CEO)
Specialisation: Fully managed streaming ETL/ELT platform using Apache Beam for real-time AI
Google Cloud Dataflow is a fully managed streaming platform built on Apache Beam for batch and stream data processing.
Thomas Kurian emphasises its integration with Vertex AI and Gemini models.
The platform is engineered to deploy multimodal data processing for Gen AI, enabling parallel ingestion and transformation of images, text and audio.
Google teams routinely process petabytes of data, scaling to 4,000 workers per job.
This intense focus on low-latency data preparation positions Dataflow for organisations requiring high-throughput AI features for immediate operational responses.
2: Azure Data Factory
Company: Microsoft (Azure)
CEO: Satya Nadella (Microsoft CEO)
Specialisation: Cloud-scale hybrid data integration and orchestration for analytics and MLOps
Azure Data Factory is Microsoft’s cloud-scale data integration service supporting code-free ETL and ELT processes.
As part of Satya Nadella’s AI-first strategy, ADF delivers integrated data to Azure Synapse Analytics. The platform creates reproducible ML pipelines, defining reusable steps for data preparation, training and scoring.
This logs lineage data for ML lifecycle governance, ensuring quality assurance and consistent metadata tracking.
ADF handles enterprise requirements including file validation, data deduplication and secure credential storage in Azure Key Vault.
The architecture emphasises orchestration across disparate environments, making it robust for hybrid cloud scenarios where pipeline repeatability and governance across varied resource targets prove paramount for enterprises operating across multiple cloud providers.
1: AWS Glue
Company: Amazon Web Services (AWS)
CEO: Matt Garman
Specialisation: Serverless, Apache Spark-based data preparation for analytics and ML
AWS Glue is AWS’s serverless ETL service designed to handle big data workloads.
Matt Garman leads the service, which utilises Apache Spark to distribute data processing across worker nodes, enabling faster transformation through in-memory processing.
The platform’s Gen AI integration through Amazon CodeWhisperer offers an ETL coding assistant that automatically generates code for jobs.
This automation addresses the difficulty of writing complex Spark code, lowering the technical skill barrier and broadening the user base beyond specialised data engineers.
Glue’s role in ML operations is foundational: it provides interactive sessions with Spark and uses Glue crawlers to catalog metadata, capturing critical data lineage within Amazon SageMaker Unified Studio.
The tight integration with the broader AWS ecosystem gives Glue a structural advantage, making it difficult to displace once embedded in production workflows.
