Data warehouse consulting and implementation services
Bring your scattered data into one reliable source
Data warehouse services we provide.
Data warehouse consulting and strategy
Before building anything, we map what you're reporting on and assess your current setup. Our data warehouse consulting services cover picking the platform and approach that fit your case.
Strategy and roadmap. Sequencing so high-value reporting comes online first, rather than all at once.
Readiness and maturity assessment. A vendor-neutral read on your current infrastructure and team skills.
Requirements engineering. Stakeholder alignment on what "one source of truth" means before drawing a schema.
Deployment-model selection. Cloud, on-premises, or hybrid, based on compliance needs and existing setup.
Proof of concept and prototyping. Testing the riskiest architecture assumption before committing budget to the full build.
Cost optimization advisory. Modeling what the platform actually costs at your data volume, before you commit.
Data warehouse design and architecture
The schema decisions made here determine how expensive every future change becomes, which is why we design for the data volume you'll have in two years and more.
Architecture design. Top-down, bottom-up, hybrid, or federated, chosen to fit how your organization works.
Data modeling. Conceptual, logical, and physical models, relational or dimensional, depending on query patterns.
Schema and data-flow design. A map of how data moves from source to report, so every number can be traced back.
Scalability and performance planning. Built to hold up as data volume and query load grow.
Data mart design. Focused, department-specific views for fast answers to narrower questions.
Enterprise and real-time design. EDW architecture for multi-team consolidation, or streaming for real-time needs.
Data warehouse implementation and development
Our data warehouse development services take the approved design through infrastructure, schema, pipelines, and deployment, with one team accountable for the entire build.
End-to-end build. Infrastructure, schema, and pipelines delivered as one continuous project.
Infrastructure provisioning. Compute, storage, and networking sized to your actual workload.
Database structures and schema development. The tables, relationships, and indexes the design calls for.
Historical data loading and validation. Every load checked against the source system before go-live.
Data quality controls. Rules that catch bad data at the point of entry.
Testing and deployment. Functional, performance, and security testing, with a rollback plan for cutover.
Data integration and ETL/ELT
A warehouse is only as trustworthy as the pipelines feeding it. Brights connects the systems your data is scattered across and keeps them in sync.
Source integration. ERP, CRM, IoT, third-party, and in-house systems connected into one repository.
Pipeline development. Automated ETL/ELT, batch or real-time, depending on the need.
Data mapping and transformation. Naming conventions and formats reconciled across systems never built to talk to each other.
Data consolidation. Fragmented data merged into a single repository with one consistent definition per metric.
Data lineage and monitoring. A trace of where every number came from, so a discrepancy is traceable in minutes.
Legacy-system integration. Older systems without modern APIs, connected through custom connectors.
Data warehouse migration
Migrating a live warehouse is where most of the risk sits. We plan for that risk directly: what moves first, how long the cutover takes, and what happens if something doesn't go as expected.
Migration strategy and planning. What moves first and how the cutover window is sequenced.
Pre-migration assessment. An audit of the current system for technical debt and undocumented dependencies.
Lift-and-shift or redesign. The faster path, or the one that fixes structural issues permanently.
Legacy-to-cloud migration. A move from on-premises systems to AWS, Azure, or Google Cloud.
Re-platforming. A vendor switch without breaking the reports built on the old system.
Post-migration validation. Every table checked against the source before the legacy system is retired.
Data warehouse modernization
A full rebuild is the expensive answer, and it's not always the right one. We look at what's still working in your current warehouse and fix what isn't, instead of starting over by default.
Re-architecture. Redesign of the parts of an existing system that no longer serve current reporting needs.
ETL re-engineering. Brittle, hard-coded pipelines rebuilt into something a new team member can actually maintain.
Cloud-native or hybrid re-platforming. Compute-heavy workloads moved to the cloud while sensitive data stays on-premises.
Performance optimization. A diagnosis of why queries that used to run in seconds now take minutes.
Lakehouse development. Unstructured data storage added alongside your existing warehouse, often built on Databricks.
Streaming analytics enablement. Real-time ingestion retrofitted onto a warehouse built for daily batch loads.
Data governance
A polished dashboard built on bad data is more dangerous than no dashboard at all. We build governance into the design from the outset.
Governance framework. Clear ownership of data and what counts as correct for each metric.
Metadata and master data management. One authoritative version of core entities like customer or product.
Data quality management. Cleansing, standardization, and enrichment on an ongoing basis.
Access controls and security. Role-based access and encryption, scoped by job function.
Compliance advisory. GDPR, HIPAA, PCI DSS, SOC 1/2, GLBA, and AML/KYC, built in from the start.
Lineage and auditability. Documentation that traces every number back to its source.
AI-readiness and advanced integration
We treat AI-consumed data with the same governance as everything else in the warehouse, because an AI system won't tell you when the data underneath it is wrong.
AI-ready data foundations. Data structured for consumption by AI systems.
Data preparation for LLMs and agentic systems. Data formatted for RAG pipelines, copilots, and autonomous agents.
Vector store integration. Pinecone, Weaviate, FAISS, or pgvector, matched to your existing stack.
Feature and context modeling. Data structured so AI tools can use relationships meaningfully.
Embedding freshness. Vector data kept up to date so AI tools don't answer with stale information.
AI governance. The same access and lineage standards apply to AI-consumed data as everywhere else.
BI and analytics enablement
Connecting a BI tool to a warehouse is the easy part. Building dashboards people open daily and training teams to self-serve is where the effort goes.
BI tool deployment. Power BI, Tableau, Qlik, or Data Studio, configured directly against the warehouse.
Dashboard development. Views built for what each team actually needs to see.
Self-service BI. Governed access for business users to build their own reports without filing a ticket.
Predictive and advanced analytics. Forecasting and anomaly detection layered onto a warehouse that supports it.
Data visualization. Custom visual work that a standard BI template doesn't cover.
Data literacy programs. Training for the team to use what's been built.
Get the full picture first
Data warehouse consulting or implementation?
Data warehouse consulting services
What it is: Advisory work — strategy, readiness assessment, architecture design
What you get: A roadmap, a schema, a platform recommendation
Cost: $10K to $50K, depending on scope
Timeline: 2 to 6 weeks
When you need it: Before committing budget to a build, or when a previous attempt needs diagnosing
Data warehouse implementation services
What it is: Delivery — the actual build, from pipelines to production
What you get: A running warehouse, tested and deployed
Cost: $30K to $1M+, depending on scope
Timeline: 2 to 18 months
When you need it: Once the architecture is settled, and you need someone to build it
Our data warehouse implementation process.
Cost and timeline for data warehouse development: what to expect.
Data warehouse costs break into two pieces: the build, and the cloud bill that keeps running afterward. Consulting — strategy, assessment, architecture — typically runs $10K to $50K. Full implementations range from around $30K for a focused, single-team build to $1M+ for enterprise scope, with most first production deployments landing between 12 and 24 weeks. Snowflake, BigQuery, and similar platforms bill separately, and that bill scales with your data volume.
We're upfront about pricing from the start, and flexible in how we scope it. The estimate reflects the actual work involved, your data volume, your source count, your compliance requirements. You see exactly what you're paying for and why, before you commit to anything.
Platforms and technologies we work with.
When do you need a data warehouse consulting partner?
You don't have an in-house data architect, and the team makes schema decisions on a guess that gets expensive to unwind later.
You need to migrate a production system, and downtime during the cutover would cost you something real.
You fall under compliance requirements, GDPR, HIPAA, PCI DSS, which means the design has to hold up under an audit.
You need someone to diagnose why a previous attempt stalled before building a data warehouse again.
You need to consolidate data across multiple teams or business units, and nobody currently owns that architecture end-to-end.
You want a vendor-neutral read on which platform actually fits your data, independent of who's selling what.
You want the architecture checked before you commit budget to the build.
If you're on this list, let's talk.
Pick the engagement model that fits your needs.
One-off advisory
When the need is a strategy review, readiness assessment, or architecture design, we deliver the roadmap and hand it off, whether you build it yourselves or bring in another partner for implementation. This works well for teams that already have engineering capacity and just need the design decisions made right the first time.
End-to-end project delivery
For projects that need the whole path, architecture, pipelines, testing, and deployment, our data warehouse implementation services are delivered by one team, from the first requirements call through go-live. We bring architects, engineers, and QA specialists onto the same project from day one, and the people who design the schema are still there testing it under real query load twenty weeks later.
Managed services
A live warehouse still needs monitoring, performance tuning, and cost optimization as data volume grows. We stay on after launch instead of disappearing once the contract renews, catching the kind of slow performance drift and cost creep that usually goes unnoticed until someone finally checks the cloud bill.
Staff augmentation
To move a specific phase faster without hiring, our specialists work within your existing team structure while you continue running the project. This fits teams that have the process and ownership figured out already and just need extra hands with specific expertise, migrations, compliance work, a platform none of your engineers have used before, for a defined stretch of the build.
Why companies choose Brights.
We size every architecture for the data volume and query load you'll have in the years ahead. That's shaped by 300+ projects across startups consolidating their first few sources and enterprises running compliance-heavy reporting across multiple business units, so the problem in front of you is rarely one we're seeing for the first time.
More success stories delivered by Brights.
FAQ.
Data warehouse consulting and implementation are two distinct phases of the same project. Consulting is advisory work: our data warehouse consultants define strategy, assess readiness, select a platform, and design the architecture and roadmap. Implementation is the build itself: developing ETL/ELT pipelines, loading and validating historical data, testing, and deploying to production. Most projects move from consulting into implementation, with the design phase reducing the risk of costly changes once the build is underway. Together, these data warehousing services help reduce project risk and create a smoother path from planning to production.
Request a quote.
Thanks for scrolling this far. Let's take the next step. Provide us with a brief description of what you are going to build.