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Data Integration
Managing expectations of artificial intelligence (AI) capabilities without addressing data proliferation and inaccessibility is the most immediate data leadership challenge. Data teams are struggling with siloed data, real-time data processing and data quality issues. Job failures and performance bottlenecks add to rising data integration costs. One-purpose integration tools limit your ability to design and run data pipelines that meet service level agreements (SLAs) on performance, cost, latency, availability and quality.
Data integration offers a modular approach to data integration and management, allowing you to create well-designed extract, transform, load (ETL) or extract, load, transform (ELT) data pipelines, each tailored to unique use cases, using a simple graphical user interface (GUI). It supports data processing in batches or real-time, whether on cloud or on-premises. With its continuous data observability capability, you can proactively manage data monitoring, alerting and quality issues from a single platform.
Data integration is designed to create, manage and monitor data pipelines, helping ensure trusted and consistent data is accessible at scale and at speed.
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Unify diverse data sources, power model training and enhance AI's contextual understanding and capabilities.
Match integration styles to meet SLAs on performance, cost, latency, availability, quality and security.
Ingest data from applications regardless of where data resides in the data fabric—on-premises, in the cloud or in a hybrid environment.
IBM StreamSets enables streaming data pipelines through an intuitive graphical interface, facilitating seamless data integration across hybrid and multicloud environments.
IBM DataStage is an industry-leading data integration tool that helps you design, develop and run jobs that move and transform data.
IBM Databand is observability software for data pipelines and warehouses that automatically collects metadata to build historical baselines, detect anomalies and triage alerts to remediate data quality issues.
Create a well-designed data pipeline that uses the right integration style, whether ETL, ELT, all-at-once ingestion or change data capture (CDC), in batches or in real time.
Embed data integration capabilities anywhere in your existing hybrid, multicloud infrastructure, and manage everything with a single control plane.
Design scalable and resilient data pipelines with modular, repeatable templates and standardized practices such as DataOps, and push them to production at scale.
Detect data incidents earlier with continuous data observability, resolve them faster, and deliver higher-quality data to the business.
Manage all data types—structured, semistructured and unstructured—from a single platform.
Use cases
Create resilient, high performing and cost optimized data pipelines for your generative AI (gen AI) initiatives, real-time analytics, warehouse modernization and operational needs.
Explore the strategic steps to design and implement a data strategy that drives business advantage.
2024 Gartner® market guide on data observability tools
Learn how a multicloud data integration strategy can democratize data.