Data Warehousing Consulting: Modernize, Migrate, or Optimize
Your data warehouse is either your organization's most valuable analytical asset or its most expensive bottleneck. Whether you're migrating from legacy on-premises systems, modernizing to a lakehouse architecture, or optimizing what you have — the engineering decisions determine which outcome you get.
Architecture Design
Dimensional modeling, star schema, warehouse vs lakehouse decisions
Cloud Migration
Teradata, Oracle, SQL Server → Snowflake, Fabric, Databricks, Redshift
Performance Optimization
Query tuning, partition strategy, cost management, materialized views
ETL Modernization
SSIS, Informatica → modern pipelines in Fabric, dbt, Airflow, ADF
Your warehouse was built for a different era. Your data demands have changed.
Ten years ago, a data warehouse served reports to a dozen analysts running queries during business hours. Today, enterprises need warehouses that serve hundreds of concurrent users, power real-time dashboards, feed machine learning models, and integrate with AI applications — all while keeping costs predictable.
The modernization decision isn't one-size-fits-all. Some organizations need a full migration to a cloud-native lakehouse. Others need to optimize their existing Snowflake or Synapse deployment. Some need both — a modern warehouse for BI alongside a lakehouse for data science. The right answer depends on your workloads, your existing investments, and where your data strategy is heading.
This complexity is why data warehouse architects remain among the hardest roles to fill. The engineers who can evaluate platforms, design migration paths, and execute without breaking existing reporting are rare. Xylity's consulting-led matching process identifies this expertise through scenario-based assessment — not just keyword matching on resumes.
Data warehousing consulting capabilities
Architecture, migration, optimization, and modernization — staffed by pre-qualified warehouse engineers matched to your platform and use case.
Warehouse Architecture & Design
Dimensional modeling, star and snowflake schemas, slowly changing dimensions, conformed dimensions across subject areas. Data vault for enterprises with complex source systems. Architecture that balances query performance with data freshness and maintenance cost.
Cloud Migration
From on-premises (SQL Server, Oracle, Teradata, Netezza) to cloud (Snowflake, Fabric, Databricks, Redshift, BigQuery). Schema translation, ETL conversion, data validation, parallel-run testing, and cutover planning. The most common — and most risk-prone — warehouse project.
Lakehouse Modernization
Evolving from traditional warehouse to lakehouse architecture: supporting both SQL analytics and data science on the same data. Fabric lakehouse, Databricks lakehouse, or hybrid approaches that keep warehouse BI running while adding lakehouse flexibility.
Performance Optimization
Query tuning, partition and clustering strategy, materialized view design, workload management, and concurrency optimization. Sometimes the right move isn't migration — it's making your current warehouse perform the way it should.
ETL & Pipeline Modernization
Migrating legacy ETL (SSIS, Informatica, DataStage) to modern orchestration: dbt for transformation, Airflow or Prefect for orchestration, ADF or Fabric pipelines for ingestion. Testable, version-controlled, CI/CD-enabled data pipelines.
BI Integration & Optimization
Optimizing the warehouse layer that feeds Power BI, Tableau, and other BI tools. Semantic layer design, aggregate tables, query caching, and Direct Lake implementation for Fabric environments.
Warehouse platforms we design, migrate, and optimize
Snowflake
Multi-cluster warehouse, time travel, data sharing, Snowpark for ML
Microsoft Fabric
Warehouse + lakehouse endpoints, T-SQL, Direct Lake, OneLake storage
Databricks SQL
SQL warehouses on lakehouse, serverless compute, Unity Catalog
Amazon Redshift
Columnar storage, Spectrum for S3, RA3 nodes, serverless
Azure Synapse
Dedicated SQL pools, serverless, Spark integration (migrating to Fabric)
Google BigQuery
Serverless, columnar, BigLake for multi-cloud, ML integration
SQL Server / Oracle
On-premises optimization and cloud migration source platforms
dbt + Airflow
Modern transformation and orchestration layer for any warehouse
Pre-qualified warehouse engineers, matched to your platform
Assessment
We map your current warehouse architecture, pain points, and modernization goals. Migrate, optimize, or modernize — the matching starts from your situation.
Platform Matching
Engineers matched for your specific platform: Snowflake, Fabric, Databricks, or legacy systems. Migration path experience and domain knowledge verified.
Scenario Evaluation
Candidates demonstrate warehouse expertise: schema design decisions, migration risk mitigation, query optimization strategies. 92% pass your screen on first match.
Deploy & Validate
Your specialist contributes from week one. Data validation frameworks ensure every migrated table matches source-of-truth before cutover.
Warehouse expertise for enterprises and delivery partners
Modernizing your warehouse but struggling to find architects who've done it?
Data warehouse migration is high-stakes engineering — get it wrong and you break the reporting that runs the business. Xylity matches pre-qualified warehouse architects who've executed production migrations: from on-premises to cloud, from legacy ETL to modern pipelines, from siloed warehouses to unified lakehouses. Companies of 500-10,000 employees trust our consulting-led process.
Start a Consulting Engagement →Client needs a warehouse migration but your bench lacks platform depth?
Warehouse projects require specific platform expertise: Snowflake architecture, Fabric lakehouse design, Databricks SQL optimization. When a client's migration calls for skills your team doesn't have, Xylity delivers curated profiles in days. IT services companies of 20-1,000 employees use Xylity to staff warehouse engagements with confidence.
Scale Your Data Delivery →Data warehousing — answered
Your warehouse modernization deserves
engineers who've done it in production.
Tell us about your warehouse — current state, target state, and timeline. We'll match pre-qualified architects in an average of 4.3 days.