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.