A Single Source of Truth Your Whole Organization Can Trust
We architect and implement modern data warehouses and lakehouses on Snowflake, Databricks, BigQuery, or Redshift, delivering clean, governed data that business users actually trust and use.
Warehouse & Lakehouse
- Snowflake Implementation
- Databricks Lakehouse
- BigQuery & Redshift
- Semantic Layer
Cloud Data Warehouse Design
A well-designed data warehouse is the foundation of every analytics program. We design for query performance, cost efficiency, and long-term maintainability.
Get Started- Snowflake, BigQuery, and Redshift implementation
- Dimensional modeling: star and snowflake schemas
- Slowly changing dimension (SCD) implementation
- Semantic layer and business metrics definitions
- Role-based access control and data governance
Data Lakehouse Architecture
The lakehouse pattern combines the flexibility of a data lake with the performance and governance of a warehouse — enabling both analytics and ML on the same platform.
Get Started- Databricks Delta Lake and Unity Catalog implementation
- Medallion architecture: bronze, silver, gold layers
- Apache Iceberg and Delta format for ACID transactions
- Unified platform for SQL analytics and ML workloads
- Photon engine optimization for sub-second query performance
Data Modeling & Semantic Layer
Great data architecture means nothing if analysts can't find and understand the data. We build semantic layers that translate raw tables into business-meaningful concepts.
Get Started- dbt semantic layer with reusable metrics definitions
- Looker LookML semantic model development
- Cube.dev headless BI semantic layer
- Business glossary and data dictionary
- Self-service analytics enablement for business users
Cost Optimization & Governance
Cloud data warehouses can become expensive quickly without proper cost controls. We implement FinOps practices specific to data platforms.
Get Started- Query optimization reducing compute costs 30–50%
- Clustering, partitioning, and materialization strategy
- Cost monitoring dashboards by team and workload
- Automated suspension of idle warehouses and clusters
- Storage tiering for infrequently accessed historical data
What We Deliver
A comprehensive set of Warehouse & Lakehouse capabilities, designed to work together or independently.
Snowflake Implementation
Complete Snowflake setup with virtual warehouses, RBAC, and data sharing.
Databricks Lakehouse
Delta Lake, Unity Catalog, and medallion architecture on Databricks.
BigQuery & Redshift
GCP BigQuery and AWS Redshift warehouse design and optimization.
Semantic Layer
Business metrics and KPI definitions in dbt, Looker LookML, or Cube.dev.
Cost Optimization
Query tuning, clustering, and FinOps practices reducing warehouse spend.
Data Governance & RBAC
Column-level security, data masking, and audit logging across the platform.
Proper clustering, partitioning, and materialization strategies deliver 10x+ query improvements.
Cost optimization engagements consistently reduce warehouse spend by 30–50%.
Delta Lake and Iceberg enable ACID transactions on petabyte-scale datasets.
Why Choose InnovTen
We don't just deliver projects. We build partnerships that drive long-term outcomes.
Single Source of Truth
One governed platform replacing data silos, spreadsheets, and conflicting reports.
Query Performance
Optimized schemas, clustering, and caching delivering sub-second analytical queries.
Controlled Costs
FinOps practices and automated controls preventing cloud data bill surprises.
Enterprise Governance
Row and column-level security, data masking, and complete audit trails.
Self-Service Analytics
Semantic layers that enable analysts to query confidently without asking engineers.
ML-Ready Platform
Lakehouse architecture supports both analytics and machine learning on the same data.
Our Delivery Process
How we approach every Warehouse & Lakehouse engagement, from first call to ongoing operations.
Data Discovery & Assessment
Inventory data sources, volumes, access patterns, and consumer requirements.
Architecture Design
Select platform, design schema, define medallion layers, and plan access control model.
Platform Build
Provision infrastructure, implement data models, load historical data, and set up governance.
Semantic Layer & BI
Build business metrics layer and connect BI tools for analyst self-service.
Optimize & Operate
Query optimization, cost monitoring setup, and documentation handover.
Data Discovery & Assessment
Inventory data sources, volumes, access patterns, and consumer requirements.
Architecture Design
Select platform, design schema, define medallion layers, and plan access control model.
Platform Build
Provision infrastructure, implement data models, load historical data, and set up governance.
Semantic Layer & BI
Build business metrics layer and connect BI tools for analyst self-service.
Optimize & Operate
Query optimization, cost monitoring setup, and documentation handover.
Warehouse & Lakehouse in Action
Real-world applications across industries we've delivered for.
Data Silo Consolidation
Unified Snowflake warehouse replacing 8 departmental databases and Excel reports, as a single source of truth for 500 users.
Databricks ML Platform
Delta Lake lakehouse supporting both BI reporting and 20+ ML models on the same unified data platform.
Warehouse Cost Reduction
Redshift optimization program: query rewrites, clustering keys, and distribution styles cut compute costs by 45%.
BigQuery Migration
Migrated 5TB on-premises data warehouse to BigQuery in 8 weeks, achieving 6x query performance improvement.
Frequently Asked Questions
Common questions about our Warehouse & Lakehouse services.
Snowflake is the most accessible for pure SQL analytics teams and has the best data sharing features. Databricks is the best choice if you need both analytics and ML on a unified platform. BigQuery is ideal for GCP-native organizations and variable workloads since it's serverless. We help you evaluate based on your team's skills, workload patterns, and existing cloud.
A lakehouse combines the raw storage flexibility of a data lake with the query performance and governance of a warehouse. You need it if you have ML workloads that require access to raw or semi-structured data alongside BI analytics, or if you need to store petabytes of data cost-efficiently. For pure BI use cases, a traditional warehouse is simpler.
We design and execute historical data loads as part of every implementation, typically loading full history first, validating against source systems, then switching to incremental loads. For large volumes, we parallelize loading across multiple threads.
Medallion architecture organizes data into three layers: Bronze (raw, unmodified source data), Silver (cleaned and conformed), and Gold (business-ready aggregates and metrics). It's the standard pattern for Databricks Delta Lake and increasingly used in other platforms too.
Ready to Get Started with Warehouse & Lakehouse?
Tell us about your project. We'll respond within 24 hours with a clear next step.