Assess
Inventory of warehouses, marts, pipelines and consumption patterns; sized for Snowflake credit usage and ROI before migration begins.
Survey
Launching complex data initiatives without auditing your underlying asset landscape leads to fragmented storage, broken processing pipelines, and massive cloud resource waste. The Survey phase is a continuous discovery and data-profiling period. Before building data lakehouses or training AI models, it catalogs available data sources, analyzes structural schemas, and traces system dependencies to establish a clear, data-driven starting point.
- Automated Data Discovery: Deploy automated crawlers to scan multi-cloud object storage, on-premise relational databases, and SaaS application endpoints to map the entire corporate data footprint.
- Data Quality Profiling: Run automated statistical analyses on raw datasets to identify structural anomalies, missing values, duplicate strings, and data variance issues early.
- Metadata & Lineage Auditing: Map how data moves across legacy pipelines, uncovering hidden dependencies and documenting current data-transformation workflows.
- Compliance & PII Locating: Continuously scan tables to pinpoint where sensitive personal identifiable information (PII), financial records, or healthcare data resides.
The Survey phase replaces assumptions with hard technical facts. Identifying structural data issues, unmapped storage tables, and shadow IT infrastructure early prevents expensive course corrections mid-project. It provides data engineering teams with a clear roadmap of what data is reliable, what needs cleaning, and how to structure storage tiers safely.