IBM watsonx.data integration

Power AI-ready pipelines with agentic data integration across hybrid environments

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Overview

Your AI is only as reliable as the data feeding it

Most integration tools solve for one thing. Batch only. Cloud only. Structured data only. watsonx.data integration solves for all of it — structured and unstructured, batch and real-time, on-premises and cloud from one unified control plane with governance and observability embedded.  

Features

Your data, activated for AI

Describe the data you need in natural language. Automatically generate pipeline plans, validate and execute across any integration style, accelerating data delivery. Proactively detect and diagnose failures, reducing manual monitoring and remediation effort.

Dark UI showing “Describe output data” step with text box for data description

Automate ingestion and preparation of documents, text, and other unstructured data types to support analytics and AI workflows. watsonx.data integration processes unstructured content alongside structured sources from one unified control plane.

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Ingest and process streaming data with low latency to power real-time decisions. watsonx.data integration builds resilient pipelines that scale across streams while protecting against schema changes and data drift.

A UI illustration of watsonx.data integration showing the unified control panel

Design batch pipelines once and run them anywhere—on-prem, in the cloud, or across hybrid environments. watsonx.data integration delivers high-performance ETL and ELT pipelines with flexible authoring and built-in reliability

Dark UI panel titled “Compile” showing options ETL selected, TETL, and ELT, with ETL highlighted in blue.

Monitor pipeline health and detect data incidents early. Built-in observability alerts teams to anomalies, supports root cause analysis, and helps ensure reliable, trusted data delivery.

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Synchronize data across systems with low-latency replication. watsonx.data integration captures and delivers only required changes, helping ensure continuous operations and up-to-date applications.

Data Observability UI for watsonx.data integration

See watsonx.data integration in action

Discover how IBM watsonx.data integration helps organizations streamline complex data estates, eliminate data silos, reduce rising workload costs and support hybrid cloud deployments—all while maximizing ROI from existing data investments.

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Use cases

Become AI-ready

Accelerate data delivery with agentic data integration

Reduce data wait times from weeks to minutes. Agentic data integration automates data pipeline creation, monitoring, and optimization to deliver governed data faster, eliminating manual handoffs and delays.

Learn more about agentic data integration
Unify batch and streaming integration

Support both batch and real-time data integration, enabling you to choose the right processing approach for each use case and business need.

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Governed data delivery across hybrid environments

Some data cannot leave its jurisdiction. watsonx.data integration’s hybrid deployment and execution flexibility lets organizations with hybrid data estates process and deliver data in a compliant manner.

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Simplify connectivity and data movement across platforms

Move data from distributed sources including core systems such as Mainframe and SAP into modern data platforms and lakehouses, using robust connectors and flexible hybrid deployment options.

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Case studies

Real clients, real results
 

Moving from reactive to proactive data quality 

Autodesk Inc. reduced time to detect data quality issues from days to minutes with data observability capabilities in watsonx.data integration software.​  

1000+
DAGs actively monitored
Zero
Reduced detection time from weeks and days to zero
Team of creative people working at desks in modern office

Pricing

Flexible pricing

Start with what you need. Scale as you grow.

Whether you prefer fully managed SaaS or self-managed software, IBM offers deployment and pricing options designed for enterprise environments.

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Take the next step

Whether you are consolidating a fragmented tool stack, building AI pipelines for the first time, or rethinking how your data engineering team scales, there is a path forward from where you are now. Build it on an integration layer that was designed for that job.

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