Data Warehousing
The Foundation for Enterprise Analytics
A well-architected data warehouse is the backbone of every analytics initiative. GRAVITI designs, builds, and manages data warehouses that scale with your business and deliver reliable, query-ready data for every team.
Microsoft Azure
Amazon Web Services
Google Cloud
IBM Cloud
Oracle Cloud- Full flexibility in deployment options. We are not commercial partners of software vendors
Who Is It For
Data warehousing engagements serve organizations that have outgrown ad-hoc data stores and need a scalable, governed analytics foundation.
- Data teams struggling with slow queries, inconsistent schemas, and fragmented data stores
- IT leaders planning cloud migration of on-premises data infrastructure
- Analytics teams that need a reliable, performant foundation for BI and ML workloads
- Enterprises consolidating data estates after mergers, acquisitions, or platform changes
Our Approach to Data Warehousing
GRAVITI builds modern data warehouses using proven architectural patterns—star schemas, data vaults, or data mesh topologies—selected based on your organization's scale, query patterns, and team structure. We design for both current requirements and future growth, ensuring your warehouse handles increasing data volumes without costly re-architecture.
Our engineers implement the warehouse on your preferred cloud platform—Snowflake, BigQuery, Redshift, or Databricks—with automated ingestion pipelines, transformation layers, and access controls. We follow dimensional modeling best practices to ensure fast query performance and intuitive data navigation for analysts and business users.
Every warehouse we deliver includes documentation, data lineage tracking, and monitoring dashboards so your team understands what data exists, where it came from, and whether pipelines are running as expected.
Connecting to systems already in your organization
Our solutions include integration with popular market systems, as well as any additional system as needed
How We Deliver
- Requirements & Architecture: Assess data volumes, query patterns, and team needs to select the optimal warehouse design
- Schema Design: Build dimensional models, define grain, and establish naming conventions
- Pipeline Development: Create ETL/ELT pipelines for data ingestion and transformation
- Access & Governance: Implement role-based access controls, data cataloging, and lineage tracking
- Performance Tuning: Optimize query performance, partition strategies, and materialization schedules
Expected Outcomes
- Centralized, query-optimized data store serving all analytics and reporting needs
- Sub-second to low-second query performance for standard business intelligence workloads
- Automated data ingestion with monitoring and alerting for pipeline failures
- Clear data governance with lineage, cataloging, and role-based access controls
Service Model
- Assessment: 2-week architecture review and platform selection
- Build: 8-14 week warehouse design, pipeline development, and deployment
- Managed: Ongoing performance optimization, pipeline monitoring, and schema evolution
Frequently Asked Questions
Which cloud platforms do you support?
We build on Snowflake, Google BigQuery, Amazon Redshift, and Databricks. Platform selection depends on your existing cloud footprint, query patterns, budget, and team expertise. We provide a recommendation as part of the assessment phase.
Should we use ETL or ELT?
Most modern cloud warehouses favor ELT—extracting and loading raw data first, then transforming in the warehouse. This approach leverages the warehouse's compute power and simplifies pipeline maintenance. We evaluate your specific requirements to recommend the optimal pattern.
How do you handle schema changes over time?
We implement schema evolution strategies including versioned models, backward-compatible changes, and migration scripts. Our warehouse designs accommodate additive changes without breaking existing queries or downstream dependencies.
What about data mesh vs. centralized warehouse?
We assess your organizational structure, team autonomy, and data ownership patterns to recommend the right approach. Many enterprises benefit from a hybrid model with centralized infrastructure and domain-owned data products.
Build Your Analytics Foundation
Every analytics initiative depends on reliable, well-structured data. Let GRAVITI design and build a data warehouse that scales with your ambitions.
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