Data warehouse consulting on Redshift, Snowflake, BigQuery and Databricks
Data Warehouse Consulting Services
The warehouse designed for your questions, proved against a baseline before it runs your reports.
Dedicatted assesses, designs, builds and migrates data warehouses on Amazon Redshift, Snowflake, BigQuery and Databricks. A two-week assessment with a costed plan, fixed-scope build or migration phases with parallel runs, and the optimization that keeps the warehouse fast and affordable, delivered by engineers who work with AI.
- 2-week assessment Inventory, target design and a costed plan for the warehouse in two weeks
- From $10,000 Fixed-scope data migration assessment. The price is set before any migration work
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AWS Premier Tier
Partnered with AWS, Microsoft Azure, Google Cloud and Databricks -
SOC 2 + ISO 27001
HIPAA, PCI and GDPR zones designed into the warehouse, not bolted on
What is data warehouse consulting?
What is data warehouse consulting?
Data warehouse consulting assesses how a company stores and reports on its data, designs the target warehouse or lakehouse, and plans and delivers the build, migration and optimization, covering platform choice, data model, pipelines, governance and cost.
What do data warehouse consulting services include?
An assessment of the current estate, a target design on Redshift, Snowflake, BigQuery or Databricks, a dimensional or lakehouse data model, ELT pipelines, a catalog with lineage, access control, and a migration plan with parallel runs and reconciliation.
What does a data warehouse consultant do?
A consultant inventories the sources, reports and pipelines, models the warehouse around the questions the business asks, chooses the platform for the workloads and budget, and proves every pipeline against a baseline before cutover.
When data warehouse consulting is the right call
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Reports disagree with each other
Three teams, three numbers for revenue. The warehouse is where one definition of the truth gets modeled and enforced.
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The warehouse is slow or expensive
Queries that time out, a monthly bill that only goes up, or an on-premises appliance nearing its end of support.
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AI and analytics need a foundation
Machine learning and self-service analytics need governed, documented, reliable data. That foundation is the warehouse or lakehouse, built right once.
Our data warehouse consulting services
Fixed-scope entry points. Each ends with a document your team can act on, and the migration assessment is priced before any work starts.
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Start here
Data warehouse assessment
For a warehouse decision you can defend
- Duration
- Two weeks
- Price
- Scoped in the first call
- You get
- An inventory, a target design and a costed plan
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Warehouse build or migration
For a platform to build or move
- Duration
- Fixed-scope phases
- Price
- Priced before each phase
- You get
- Model, pipelines and catalog as code, reconciled before cutover
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Data migration assessment
For an estate that has to move to the cloud
- Duration
- Fixed scope
- Price
- $10,000
- You get
- An object inventory and a migration priced in phases
Data warehouse, data lake or lakehouse
Three platform shapes that get called a data warehouse. The assessment chooses one for your workloads and budget, not by default.
- Data warehouse Structured, modeled data tuned for reporting and BI
- Data lake Raw and semi-structured data on object storage
- Lakehouse Object storage with warehouse performance and governance on top
| Data warehouse Structured, modeled data tuned for reporting and BI | Data lake Raw and semi-structured data on object storage | Our default Lakehouse Object storage with warehouse performance and governance on top | |
|---|---|---|---|
| Data | Structured, modeled | Raw, any shape | Structured and raw together |
| Best at | Fast SQL for known questions | Cheap storage and exploration | BI, streaming and machine learning on one copy |
| Typical platforms | Redshift, Snowflake, BigQuery | Amazon S3 with Athena or Spark | Databricks, Iceberg on S3 with Trino or Redshift |
| Governance | Built in | Has to be added | Catalog and lineage from day one |
| Cost profile | Grows with every copy of the data | Lowest storage cost | Storage and compute scale apart |
| Best for | Finance and operational reporting | Science, logs and archives | Most new platforms |
| See our data architecture services → |
The assessment settles the choice with your workloads, your budget and your team's skills, in two weeks.
Book the assessmentHow does a data warehouse project run?
How do companies use data warehouse consulting?
To consolidate reporting on one platform, to move an on-premises warehouse to the cloud, to replace a warehouse that has grown slow and expensive, and to put the governed foundation in place that analytics and AI depend on.
What is the data warehouse consulting process?
Assess, design, build or migrate, optimize. A two-week assessment with an inventory and a costed plan, a target design and data model, pipelines built as code and tested against a baseline, migration in phases with parallel runs, then tuning and cost control.
How long does a data warehouse project take?
The assessment takes two weeks. A first warehouse phase typically runs eight to twelve weeks. A migration runs in phases scoped and priced after the assessment, each with a parallel run before cutover.
How data warehouse consulting works with us
Engineers use AI at every stage. A person owns every pipeline that ships.
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Weeks 1 to 2
Assessment
Sources, reports, pipelines and costs are inventoried, and the target platform and data model are chosen with reasons.
- Inventory and target design
- A phased plan with a price per phase
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Weeks 3 to 6
Design and foundation
The data model and naming standards are agreed, and storage, compute and access control are built as code.
- Data model and standards
- Catalog and lineage in place before data flows
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Weeks 7 to 12
Build or migrate
ELT pipelines are built and tested against a baseline, with parallel runs until row counts and checksums match.
- Pipelines as code, tested against a baseline
- Reconciliation reports before cutover
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Ongoing
Optimize and run
Queries and cost are tuned every month, data quality checks run inside the pipelines, and your team is trained to run the platform.
- Monthly query and cost tuning
- Your team trained to run it
A warehouse you can trust on day one
The method is the same on every platform. Inventory first, baseline everything, cut over only on evidence.
- 2 weeks from kickoff to a costed plan
- 100% of pipelines tested against a baseline before they run on your data
- 4 platforms Amazon Redshift, Snowflake, BigQuery and Databricks, chosen per workload
- 3 zones HIPAA, PCI and GDPR designed into the model where they apply
Delivered under the controls audited for our SOC 2 and ISO 27001 programs, on AWS, Microsoft Azure or Google Cloud.
What are the benefits of data warehouse consulting?
Why hire a data warehouse consulting company?
Because the platform is easy to buy and hard to model. A consulting partner brings the data models, the migration tooling and the reconciliation method that make the warehouse trusted on day one, without a year of internal build.
What are the key benefits of a modern data warehouse?
One source of truth for reporting, queries that return in seconds on cloud storage, cost that scales with use rather than with hardware, lineage that answers auditors, and a foundation ready for machine learning.
How does a data warehouse improve business performance?
By shortening the path from a question to a trusted answer, retiring the reporting systems that disagreed with each other, and giving finance, operations and AI teams numbers they can rely on.
Data platforms we have delivered
All case studies
Self-Service Clinical Analytics on a FHIR Data Store for a Cardiology Network
About project The Challenge Three copies of the record, and none of them queryable. The clinical data existed in three disconnected forms: inside each device vendor's proprietary analysis.
Legacy Data Platform Modernization for a European Pet Insurance Provider
Modernize legacy DB2 systems to AWS. Discover how a European insurer unified 18 databases, cut costs by 45%, and achieved 7× faster analytic
How do you build a data warehouse in 2026: Ultimate Guide by Dedicatted’ Experts
Learn how to set up a scalable data warehouse in 2026 with Dedicatted: architecture, pipelines, and data quality best practices
The data warehouse stack we work in
Chosen per workload, never by default.
Platforms
Warehouses and lakehouses, picked for your workloads and budget.
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Databricks -
Amazon Redshift -
Snowflake -
BigQuery
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Pipelines
Batch, streaming and change data capture, built as code.
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Airflow -
dbt -
Kafka -
Debezium
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Storage and formats
Open formats on object storage, queried where the data lives.
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Amazon S3 -
Apache Iceberg -
Spark -
Trino
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BI and reporting
The dashboards finance and operations already use.
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Power BI -
Tableau -
QuickSight -
Looker
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Governance and quality
Catalog, lineage and tests that answer an auditor.
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Unity Catalog -
DataHub -
Great Expectations
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Cloud and infrastructure
Environments and access control as code on your cloud.
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AWS -
Microsoft Azure -
Google Cloud -
Terraform
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FAQ
Which data warehouse platform do you recommend?
The one the assessment picks for your workloads, budget and team, whether that is Amazon Redshift, Snowflake, BigQuery or Databricks. We are partnered with AWS and Databricks and build on all four.
Can you migrate our on-premises warehouse to the cloud?
Yes. Migration runs in fixed-scope phases, each priced before it starts, with parallel runs and reconciliation reports until the numbers match. See our cloud data migration services.
How do you keep the numbers accurate through a migration?
Row counts, checksums and reconciliation reports at every phase, pipelines tested against a baseline before they run on your data, and cutover only when they match.
Who owns the warehouse afterwards?
You do. The model, pipelines and infrastructure are delivered as code into your repositories and accounts, and your team is trained to run them.
Do you handle regulated data?
Yes. HIPAA, PCI and GDPR zones are designed into the model, access follows your policies, and our own delivery is audited under SOC 2 and ISO 27001.
Do we need a data warehouse or a data lake?
Usually a lakehouse, which is object storage with warehouse performance and governance on top. Our guide on data lake vs data warehouse walks through the choice, and the assessment settles it for your case.
How do you use AI in delivery?
Our engineers use AI at every stage, profiling sources and drafting models, pipelines, tests and documentation. Every result is verified by an engineer against a baseline before it ships.
What do data warehouse consultants do in the first two weeks?
Our data warehouse consultants inventory the sources, reports, pipelines and costs, interview the people who own the numbers, choose the target platform and data model with reasons, and hand you a phased plan with a price for each phase.
Do you provide data warehousing services after the build?
Yes. Data warehousing services continue after go-live as query and cost tuning, data quality checks in the pipelines, new sources and marts, and training for the team that runs the platform.
Question not answered? Ask a data architect. Same-day reply on feasibility questions.
Data warehouse consulting company on AWS, Azure and Google Cloud
Dedicatted provides data warehouse consulting services for organizations that need one trusted platform for reporting and analytics, from warehouse assessment, target design and data modeling to build and migration on Amazon Redshift, Snowflake, BigQuery and Databricks, and ongoing optimization. The work sits inside our data and analytics services, next to data architecture and cloud data migration, and is delivered by an AWS Premier Tier Services Partner.
Cloud data warehouse consulting services
Dedicatted’s data warehouse consultants help enterprises consolidate reporting, move on-premises warehouses to the cloud, and build the governed data foundation that analytics and AI depend on. We combine data warehouse consulting, dimensional and lakehouse data modeling, ELT pipeline engineering, governance and cost optimization to deliver warehouses that are accurate, compliant and fast.
We support businesses with
- data warehouse assessment and platform selection
- cloud data warehouse consulting on Amazon Redshift, Snowflake, BigQuery and Databricks
- data modeling and ELT pipelines as code
- data warehouse migration with parallel runs and reconciliation
- catalog, lineage, access control and compliance zones
- warehouse performance and cost optimization
Data warehousing services
Our data warehousing services cover the whole life of the platform, from the first inventory to monthly tuning. Assessment, data modeling, ELT pipelines, governance, migration and optimization are delivered by the same data warehouse consultants, so nothing is lost between phases.
Data warehouse consultants for cloud migration
Moving an on-premises warehouse to the cloud is a migration and a redesign at once. Our data warehouse consultants inventory every object and dependency, run the old and new platforms in parallel, and cut over only when row counts and checksums reconcile.
Cloud data warehouse consulting services
Cloud data warehouse consulting services choose between Amazon Redshift, Snowflake, BigQuery and Databricks on evidence. Your workloads, your budget and your team decide, and the platform is delivered as code into your own accounts.
Get started with a data architect
Tell us which reports disagree and where your warehouse runs today. Our team responds within one business day with relevant experience and a first read.
Thanks, we have it.
Our team replies within one business day, with relevant experience and a first read on your problem.