Your Single Source of Truth
A modern data warehouse is the foundation of data-driven decision making. It consolidates data from across your organization—sales, finance, operations, customer interactions—into a single, trusted repository optimized for analytics and reporting.
We design, build, and optimize cloud-native data warehouses that scale with your data and deliver insights faster. Whether you're migrating from a legacy on-premises warehouse or building a new data platform from scratch, we help you unlock the full potential of your data.
100+
Warehouses Built10PB+
Data Managed5x
Faster Queries40%
Cost Reduction
Data Warehousing Capabilities
End-to-end services for modern data warehousing
Data Warehouse Design & Architecture
We design scalable, high-performance data warehouse architectures using Kimball, Inmon, or Data Vault methodologies.
- Dimensional Modeling
- Data Vault 2.0
- Lakehouse Architecture
- Real-time Warehousing
Cloud Data Warehouse Implementation
Build and deploy modern cloud data warehouses on leading platforms with automated pipelines and best practices.
- Snowflake
- Amazon Redshift
- Google BigQuery
- Azure Synapse
Data Integration & ETL/ELT
Build robust data pipelines to ingest, transform, and load data from diverse sources into your warehouse.
- Batch & Real-time Pipelines
- Change Data Capture (CDC)
- Data Transformation (dbt)
- Orchestration
Performance Optimization
Optimize your data warehouse for speed and cost with advanced tuning, partitioning, and clustering strategies.
- Query Optimization
- Partitioning & Clustering
- Materialized Views
- Auto-scaling
Data Governance & Security
Implement robust data governance, access controls, and data quality frameworks for trusted analytics.
- Data Lineage
- Access Control (RBAC)
- Data Masking
- Data Quality Monitoring
Legacy Warehouse Migration
Migrate from on-premises data warehouses (Teradata, Oracle, Netezza) to modern cloud platforms.
- Assessment & Planning
- Schema Conversion
- Data Migration
- Cutover & Validation
Modern Data Warehouse Platforms
We're experts in the leading cloud data warehouse technologies
Snowflake
The data cloud. We build scalable, secure, and high-performance Snowflake warehouses with automated optimization.
Amazon Redshift
Fast, fully managed data warehouse at scale. We optimize for performance and cost with RA3 nodes and concurrency scaling.
Google BigQuery
Serverless, highly scalable data warehouse. We design cost-optimized pipelines and leverage BigQuery ML.
Azure Synapse
Unified analytics platform. We build integrated data warehouses and data lakes with Synapse serverless and dedicated pools.
Data Warehouse vs. Data Lake
Choosing the right architecture for your needs
📦 Data Warehouse
Structured, processed data for analytics and reporting
- Schema-on-write (data structured before loading)
- Optimized for complex queries and aggregations
- High performance for BI and reporting
- Strong data governance and quality
- Best for business users and dashboards
🌊 Data Lake
Raw, unprocessed data in native format
- Schema-on-read (structure applied when read)
- Stores all data types (structured, semi-structured, raw)
- Ideal for data science and machine learning
- Cost-effective storage for massive volumes
- Best for data exploration and advanced analytics
Many modern architectures combine both—the lakehouse—to get the best of both worlds. We help you design the right approach.
Our Data Warehouse Methodology
A proven approach for successful data warehousing
We follow a structured, iterative methodology to deliver data warehouses that meet your business needs and scale with your data.
Requirements & Discovery
We identify business questions, define KPIs, map source systems, and understand data needs for reporting and analytics.
Data Modeling & Architecture
We design the dimensional model (star schema, snowflake), define ETL/ELT strategy, and select the target platform.
Data Pipeline Development
We build robust data pipelines to extract, transform, and load data with quality checks and error handling.
Testing & Validation
We validate data accuracy, test query performance, and ensure the warehouse meets business requirements.
Deployment & Integration
We deploy to production, integrate with BI tools, and train your team on usage and governance.
Monitoring & Optimization
We continuously monitor performance, optimize queries, and evolve the model as new data sources emerge.
Success Stories
Real results from our data warehousing projects
Snowflake for Global Retailer
Built a cloud data warehouse on Snowflake consolidating data from 20+ sources—POS, e-commerce, inventory, loyalty—for real-time analytics.
Legacy Teradata to Redshift Migration
Migrated a 50TB Teradata warehouse to Amazon Redshift, reducing costs and enabling new analytics capabilities.
BigQuery for Healthcare Analytics
Built a HIPAA-compliant data warehouse on Google BigQuery, integrating EHR, claims, and clinical trial data for analytics.
Tools & Technologies
Modern data stack for warehousing and analytics
Snowflake
Redshift
BigQuery
Azure Synapse
dbt
Fivetran
Airflow
Prefect
Databricks
Looker
Power BI
Tableau
Ready to Build Your Data Warehouse?
Let's discuss how a modern data warehouse can unify your data, accelerate analytics, and drive better decisions.
Frequently Asked Questions
Common questions about data warehousing
A data warehouse is a centralized repository that stores integrated data from multiple sources for reporting and analytics. It's optimized for read-heavy queries and provides a single source of truth for business intelligence.
A database is designed for transactional processing (OLTP)—handling many small, concurrent reads/writes. A data warehouse is designed for analytical processing (OLAP)—handling complex queries on large volumes of historical data. They serve different purposes and are optimized differently.
Yes, for most organizations, cloud data warehouses offer significant advantages: scalability, pay-as-you-go pricing, reduced maintenance, automatic updates, and advanced features. We help you choose the right platform (Snowflake, Redshift, BigQuery, Synapse) based on your needs.
Timelines vary based on complexity, data sources, and requirements. A simple data mart can be built in 4-8 weeks. An enterprise data warehouse with multiple sources may take 3-6 months. We deliver value iteratively.
We implement data quality checks at every stage: validation rules, anomaly detection, reconciliation, and data profiling. We also establish data governance practices to maintain quality over time.
Absolutely. We ensure seamless integration with leading BI tools like Power BI, Tableau, Looker, and Qlik. We also build semantic layers to simplify access for business users.