We migrate enterprise data estates to Snowflake, Databricks, AWS Redshift, and GCP BigQuery with a structured, phased approach that eliminates downtime risk and ensures your data arrives complete, accurate, and performant.
Zero-downtime cutovers
Parallel-run validation before every switch
Data fidelity guaranteed
Row-count and checksum validation at every layer
Cost-optimized architecture
Right-sized compute from day one
Whether you're leaving a legacy on-prem warehouse, consolidating across clouds, or rebuilding an aging architecture, we have a proven playbook for each scenario.
Migrate legacy on-prem data warehouses, Hadoop clusters, and relational databases to cloud-native platforms. We handle schema conversion, historical data loading, pipeline replatforming, and cutover coordination so your team experiences zero disruption during transition.
Move between cloud providers or upgrade to a more capable platform without starting from scratch. Whether you're leaving a legacy SaaS warehouse or consolidating workloads from multiple clouds, we manage the complexity of multi-cloud data movement at scale.
Modernize aging data warehouse architectures that have become too expensive, too slow, or too brittle to support business growth. We redesign data models, consolidate redundant systems, and replatform to modern cloud-native warehouses built for today's analytics demands.
We're platform-agnostic and certified across Snowflake, Databricks, AWS, and GCP so we can recommend the right destination for your specific workloads—not the one we're incentivized to sell.
The cloud data platform built for elastic compute scaling, zero-copy cloning, and seamless data sharing across organizations. Our certified Snowflake architects design efficient warehouse configurations, optimize credit consumption, and implement Snowpark for in-platform ML workloads.
The unified analytics platform built on Delta Lake that combines data engineering, data science, and machine learning in one collaborative environment. We design lakehouse architectures on Databricks that reduce data duplication and accelerate both ETL and AI workloads.
Amazon's fully managed cloud data warehouse, tightly integrated with the AWS ecosystem. We migrate workloads to Redshift Serverless or provisioned clusters, configure RA3 node storage separation, and connect your existing AWS services—S3, Glue, SageMaker—into a cohesive architecture.
Google Cloud's serverless, highly scalable data warehouse with built-in machine learning capabilities via BigQuery ML. We design partitioned and clustered table strategies, optimize slot reservations, and integrate BigQuery with Dataflow, Pub/Sub, and Vertex AI for end-to-end data workflows.
We break every cloud migration into five clearly defined phases with explicit entry and exit criteria so nothing is left to chance.
We inventory your existing data estate—sources, volumes, schemas, pipelines, and dependencies—and identify migration risks, data quality issues, and cost drivers.
We design the target architecture, define migration waves, establish success criteria, and build a detailed project plan with clear milestones and rollback procedures.
We migrate a representative subset of data and workloads to validate the approach, measure performance, and surface any unforeseen issues before committing to full migration.
We execute the migration in coordinated waves, running source and target systems in parallel to validate data fidelity and business logic parity before each cutover.
Post-migration we tune query performance, right-size compute, optimize storage costs, implement monitoring, and hand off to your team with full documentation.
Start with a no-cost migration readiness assessment. We'll scope your data estate, identify risks, and give you a clear migration plan within two weeks.
Start Your Cloud Migration