Build the Data Foundation Your AI Deserves

Typically 8–16 weeks depending on complexity. Simple migrations (single source, clean schema) can complete in 8 weeks. Enterprise-scale migrations with multiple source systems, complex transformations, and regulatory requirements take 12–16 weeks. We run parallel environments so there's zero downtime during cutover.

Assessments start at $15K–$25K (1–2 weeks). Full modernization projects range from $75K–$300K+ depending on scope. We provide detailed cost models upfront including a 3-year TCO comparison showing your current spend vs. the modernized platform — most clients see 30–40% infrastructure cost savings within year one.

It depends on your workload mix. Snowflake excels at concurrent SQL analytics. Databricks is best for ML/AI workloads with Spark. BigQuery wins on zero-ops simplicity and Google ecosystem integration. We evaluate your specific requirements — query patterns, team skills, existing cloud contracts — and recommend the best fit. Sometimes the answer is a combination.

A lakehouse combines the flexibility of a data lake (store any format) with the performance of a warehouse (fast SQL queries). You need one if you're running AI/ML workloads alongside traditional BI, or if you're paying for both a data lake AND a warehouse separately. Technologies like Delta Lake, Apache Iceberg, and Apache Hudi make this practical.

Yes — zero-downtime migration is our specialty. We run parallel environments with continuous data sync (using CDC tools like Debezium). When the target platform is validated and performing at parity, we cut over during a maintenance window with rollback capability. Your users don't notice the switch.

We implement automated data quality checks at every stage — row counts, schema validation, value distribution comparisons, and referential integrity checks. Tools like Great Expectations and Monte Carlo run continuously during migration to catch discrepancies before they reach production. Nothing goes live without passing our validation suite.

We map all downstream dependencies (Tableau, Power BI, Looker, custom apps) before migration starts. Reports are migrated or reconnected as part of the project — not left as an afterthought. We validate that every dashboard produces identical results on the new platform before cutover.

Yes — we're certified across AWS, Azure, and GCP. We also work with Snowflake and Databricks (which run on all three). Our recommendation is based on your requirements, existing contracts, and team capabilities — not our vendor partnerships.

Data mesh is a decentralized data architecture where domain teams own their data products. It works well for large organizations (500+ data users) with clearly defined domains. For mid-market companies, a centralized lakehouse with clear ownership policies often delivers 80% of the benefit at 20% of the complexity. We'll recommend the right approach for your scale.

Compliance is architected from day one, not bolted on. We implement column-level encryption, automated PII detection, row-level security, audit logging, and data retention policies directly in the platform. For healthcare clients, we ensure HIPAA BAA coverage across all services. For financial services, we align with SOC 2 Type II controls.