Data Warehouse Ingestion
Rapid Ingestion
Modern enterprises often have structured application data scattered across legacy databases, ERP systems, file-shares, and on-prem or cloud platforms. With InfoCorvus’s Data Warehouse Ingestion service, you can rapidly consolidate that data into a modern warehouse or data lake — without heavy engineering, custom coding, or long development cycles.
What We Deliver
Pre-configured, no-code / low-code ingestion pipelines that load data from any structured source into your data warehouse or data lake.
Rapid deployment — from project kickoff to warehouse-ready data, in weeks (not months).
Clean, validated, warehouse-ready data: whole schemas or subsets mapping, deduplication, validation, optional masking/anonymization for sensitive fields.
Ability to discover personal and sensitive data before exposing it to the target.
Support for both batch and near real-time ingestion streams (where source systems permit) allowing for changed data propagation.
Delivery into modern warehouse or lake environments — cloud or on-premise — depending on your architecture.
Governance, lineage, and documentation built into the ingestion process so data is traceable and ready for analytics/BI.
Speed to Value
Speed to Value
Using ROAD as the foundation, our ingestion service leverages pre-built connectors and orchestration, minimizing setup overhead. We turn years of fragmented data into unified analytics store fast.
Simplicity — No-Code / Low-Code
Simplicity — No-Code / Low-Code
Business teams or data-ops staff can configure sources, mappings pipelines, and schedules via UI or config rather than custom scripts, reducing reliance on heavy specialized engineering resources.
Enterprise-Grade
Enterprise-Grade
Designed for scale: whether you have small datasets or terabytes of legacy data, going on-premise, hybrid, or cloud, we support all. As with ROAD, targets can include cloud warehouses, lakes, or hybrid storage models.
Flexible Use-Cases
Flexible Use-Cases
Ideal for data consolidation, cloud migration, analytics enablement, long-term archival with queryability, compliance & governance-backed archive-access, and modernization initiatives, data lakes for AI/ML modeling.
Unified Data Life-Cycle Management
Unified Data Life-Cycle Management
Using ROAD as the foundation, our ingestion service leverages pre-built connectors and orchestration, minimizing setup overhead. We turn years of fragmented data into unified analytics store fast.
Typical Workflow
Source Inventory & Discovery
Identify all structured data sources across your landscape (databases, legacy apps, file stores, external feeds).
No-Code Pipeline Configuration
Use pre-built connectors or configuration UI to define pipelines, scheduling, and load parameters; no hand-coding required.
Validation, Cleansing & Masking (optional)
Data is validated against schema rules, optional cleansing, or masking/anonymization rules where necessary.
Load to Warehouse/Lake
Data is ingested into the target (cloud or on-prem), optimally structured, indexed or tuned for query performance.
Governance, Metadata & Documentation
Full metadata, lineage tracking, documentation and optional archival policies applied — supporting compliance and long-term value.