Architect
Read/write ratios, latency budgets, durability requirements and data-volume forecasts that drive cluster sizing and topology.
Modifying or migrating an enterprise data layer without an exact baseline map is a primary driver of database downtime, corrupted queries, and runaway cloud infrastructure costs. The Survey phase is our deep-dive structural discovery and code audit period. Before we refactor a single database schema or spin up a new NoSQL cluster, our engineers profile your active data payloads, map system interdependencies, and isolate hidden latency sources to build an unassailable data-driven blueprint.
- Query Execution Profiling: We capture and analyze slow-running queries, scanning patterns, and execution logs to pinpoint database bottlenecks and identify manual index deficiencies.
- Schema & Payload Analysis: We audit your existing relational tables, document formats, and data-type variances to map out structural compatibility matrices for modernization.
- Dependency & Lineage Mapping: We map how applications, microservices, third-party APIs, and downstream business intelligence (BI) systems read from and write to your databases.
- Compliance & Sensitive Data Auditing: We scan your storage volumes using automated scripts to flag exposed Personal Identifiable Information (PII), payment numbers, or healthcare data requiring protection.
The Survey phase removes all guesswork from database engineering. By uncovering hidden database triggers, unoptimized queries, and undocumented data schemas early, we protect your organization from mid-project surprises. It allows us to accurately size your target database footprint, preventing expensive over-provisioning while ensuring your new data tier handles peak traffic spikes smoothly.