Rocket^® DataEdge^™

DataEdge is our flexible enterprise data integration platform that brings together governance, discovery, lineage, data virtualization (no-copy access), and real-time integration (CDC replication), so you can deliver trusted, governed, real-time data for analytics and AI. We connect IBM Z and IBM i, distributed systems, and cloud data platforms—vendor-agnostic, at enterprise scale.

Most teams see impact in weeks, sometimes days. Inside of 90 days you can deliver concrete wins such as pinpointing your top 10 datasets for classification (owners, sensitivity, policies), enable cost/usage telemetry by dataset, and surface lineage and sensitive data tags in security dashboards. Track KPIs like time to refresh non-prod, production defects tied to data, MTTR for data incidents, cost per dataset, and trust/freshness scores.

Yes, it works across your estate without forcing copies. Use data virtualization for a logical data layer with federated query, semantic views, policy-governed SQL/REST, pushdown, and caching (no-copy access), and real-time, bi-directional CDC replication when you need continuously synced copies; either way, you can offload transforms, avoid duplicate pipelines, and reuse one CDC stream for one-to-many targets. Deliver data faster, while cutting pipeline sprawl, reducing MIPs cost, and transforming overhead by reusing one stream across many targets.

DataEdge operationalizes governance with automated discovery and rationalization, central policies, RBAC, and encryption. It enforces data quality and transactional fidelity, captures end-to-end, column-level lineage across hybrid sources and targets, and maintains audit trails. Controls apply at both query and delivery with lineage-driven impact analysis and policy propagation so changes don’t break downstream. The result is safe self-service, faster approvals, and audit-ready evidence—without bottlenecking teams.

DataEdge de-risks change with low-latency CDC for parallel run, so legacy and cloud target stay in sync while you validate function and performance. Lineage-aware impact analysis identifies downstream dependencies, validates mappings, and helps reconcile data integrity before, during, and after cutover. Cutovers are controlled (not big-bang), with bi-directional replication for clean rollback. Integrity features (schema evolution, ordered replay, checkpoint/restart) plus end-to-end monitoring to protect SLAs throughout the transition.