# Data Warehouse Ingestion

## Rapid Ingestion

No Code

Low Code

Maximum Value

Modern enterprises often have structured application data scattered across ERP systems, custom applications, file-shares, and on-prem or cloud platforms and legacy databases. 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.
- Governance, lineage, and documentation built into the ingestion process so data is traceable and ready for analytics/BI.
- 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.

## Why infoCorvus

### 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

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

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

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

# Typical Workflow

1. ## Source Inventory & Discovery
   Identify all structured data sources across your landscape (databases, legacy apps, file stores, external feeds).

2. ## No-Code Pipeline Configuration
   Use pre-built connectors or configuration UI to define pipelines, scheduling, and load parameters; no hand-coding required.

3. ## Validation, Cleansing & Masking (optional)
   Data is validated against schema rules, optional cleansing, or masking/anonymization rules where necessary.

4. ## Load to Warehouse/Lake
   Data is ingested into the target (cloud or on-prem), optimally structured, indexed or tuned for query performance.

5. ## Governance, Metadata & Documentation
   Full metadata, lineage tracking, documentation and optional archival policies applied — supporting compliance and long-term value.

## Use Case

- Migrating on-prem legacy databases or data warehouses (e.g. Oracle, SQL Server) into a cloud data warehouse.
- Consolidating enterprise data silos (ERP, CRM, transaction systems, file shares) for unified analytics and business intelligence.
- Building a governed data archive + analytics platform — preserving historical data while freeing legacy systems for retirement.
- Enabling rapid data availability for analytics, AI/ML, and reporting without months of outdated traditional and costly ETL development.