- contact@insightautohub.com
Plan, execute, and validate database, workload, Kafka, and BI migrations — with AI-generated test cases, automated rollback, and weeks instead of months between go-live dates.
Production migrations across
A 3-minute walkthrough of an end-to-end database migration — planning, AI-generated test cases, execution, and automated validation.
Most migration tools handle one source. Most testing tools don’t understand schemas. InsightAutoHub does both, with the safety rails enterprise teams require.
Move workloads between any source and target — on-prem to cloud, cloud to cloud, vendor to vendor. Automated planning, scripted execution, validated outcome.
AI generates test cases from your schema and data — not just CRUD smoke tests. Row-level diffs, statistical drift checks, and golden-query comparison.
Design migration runs as workflows. AI agents handle the repetitive steps, escalate the surprises, and roll back automatically when validation fails.
Four industries where AutoHub’s validation rigor and audit trail matter most.
Compliance and data integrity for banking, payments, and core systems.
HIPAA-compliant testing for EHR systems and patient-data migrations.
Platform migrations with zero customer-visible downtime.
ERP and supply-chain migrations with IoT data ingestion.
Cloud SaaS for quick starts. Self-hosted in your cloud accounts or on-prem when data sovereignty matters.
Every migration run is recorded. Every validation step has provenance. If a stage fails, automated rollback returns the system to the last known good state — no manual recovery scrambles at 2am.
Aggregated outcomes from production migration projects. We’ll size the ROI for your project on the discovery call.
Less time spent on testing & validation
Source & target systems supported
Auditable run history
Rollback on validation failure
Buyer questions we hear from platform and data engineering teams. More in our discovery call.
Databases (MySQL, Postgres, Oracle, MSSQL, Redshift, BigQuery, Snowflake, ADW), data warehouses, Hadoop / Spark / Airflow workloads, Kafka clusters (Confluent, MSK, Redpanda), and BI / dashboarding tools (Tableau, Looker, PowerBI). Custom paths via the connector framework.
Self-host on Kubernetes or Docker in your cloud or data center. The AutoHub control plane and worker pods run alongside your data — nothing crosses your perimeter except, optionally, telemetry you opt into.
Every workflow has rollback steps defined alongside the forward steps. On validation failure the workflow halts, alerts your team, and automatically rolls back to the last checkpoint. You decide whether to retry, escalate, or abort.
Every workflow run records who launched it, which version of the migration script ran, every row count / checksum / golden-query comparison, and every approval / override. Exportable for regulator and internal-audit reviews.
Field notes on migration playbooks, validation strategies, and AI-augmented testing.
What separates the migrations that ship in one quarter from the ones that drag on for years — discovery, validation, and the boring parts that matter most.
Row counts and checksums catch the easy stuff. Distribution checks, golden queries, and AI-generated synthetic edge cases catch the rest.
Why every forward step in a migration should have an inverse, and how to design the inverse before you need it.