The Right Data Warehouse, or a Clear Reason to Wait
We help you decide whether a data warehouse is worth it yet, which platform fits and how it should be designed. You get a clear recommendation, a running cost estimate and a phased plan before any build starts.
Core stackSQL Server · Azure Data Factory · Microsoft Fabric · Azure Data Lake · Python · Power BI · PostgreSQL · Microsoft Azure
When a Warehouse Decision Is Due
Warehouse questions usually come up when reporting starts getting in the way of the business.
Design agreed and ready to build?
Our data warehousing and ETL service builds the warehouse and the loads that keep it current.
Data warehousing and ETLReports slow down your live systems
Heavy reports run against the same database your team works in, so everything slows down at month end.
No single source of truth
Customer, sales and finance data sit in many tools and each report pulls from a different one.
Unsure between a warehouse, Fabric or Power BI alone
Each vendor recommends its own platform and nobody has compared the options against what you actually need.
Running costs are a worry
You want to know what a warehouse will cost each month before committing to one.
Six Parts of Our Warehouse Advice
Pick one to see what it usually involves and how we typically build it.
Needs assessment
A look at your reporting needs, data volumes and sources to decide whether a warehouse is worth it. Sometimes the honest answer is "not yet".
- Reporting and analysis needs
- Data volume and source review
- Warehouse or simpler alternative
Platform choice
Comparing SQL Server, Microsoft Fabric and Snowflake against your data, skills and budget, with the reasons written down.
- Side-by-side comparison
- Fit with your existing tools
- Skills your team will need
Architecture and data model design
How data flows in, how it is stored and how tables are modeled so reports stay fast and consistent as the business grows.
- Source to report data flow
- Data model design
- Separate raw and reporting layers
Access and governance
Who can see which data, who owns each dataset and how shared definitions are agreed and kept current.
- Roles and access rules
- Data ownership
- Shared metric definitions
Running cost estimate
An estimate of monthly storage, compute and license costs based on your data volumes and usage, compared across platforms.
- Monthly cost by platform
- Growth scenarios
- Ways to keep costs down
Phased roadmap
A build plan in stages, starting with the reporting area that gains the most, so value arrives before the whole warehouse is done.
- First area to build
- Stage plan and effort
- Build and handover options
Need First. Platform Second.
Every engagement follows the same problem-first Systems Engineering Method we use across AppPotter.
Understand the reporting
We look at who reports on what, how often and where the current setup falls short.
Review the data
We check sources, volumes, quality and how the data needs to join up.
Decide if a warehouse is needed
We compare a warehouse with simpler options and tell you plainly which one fits.
Choose the platform
SQL Server, Microsoft Fabric and Snowflake are compared on fit, skills and running cost.
Design the warehouse
Architecture, data model and access rules are designed and reviewed with your team.
Plan the build
You get a phased roadmap and cost estimate, ready for your team or ours to build from.
What Changes When the Process Runs on the Right System
We agree on what to measure during discovery, so success is defined before the build starts, not after launch.
Before: Reports slowing down live systems
After: A plan to move reporting off your working databases
Before: Vendors each pushing their own platform
After: An independent comparison against your needs
Before: Data spread across many tools
After: A design for one source of truth
Before: No idea what a warehouse would cost to run
After: A monthly cost estimate before you commit
Before: A big project with no clear starting point
After: A phased roadmap that starts where the value is
Chosen for Your Systems, Not Our Preferences
We advise on SQL Server, Microsoft Fabric and Snowflake, with Power BI on top for reporting. The recommendation follows your data, your team and your budget, not a preferred platform.
Where we have the deepest production experience
- SQL ServerData
- Azure Data FactoryPipelines
- Microsoft FabricData platform
- Azure Data LakeStorage
- PythonData engineering
- Power BIReporting
- PostgreSQLData
- Microsoft AzureCloud
Also used when the project calls for it
- Data platforms
- Sources we migrate from
- Cloud
Senior Engineers, Directly Involved
- Founder-led delivery
Parth and Umesh are personally involved in every engagement.
From scoping the work to standing behind its outcome, backed by Parth's 14+ years of enterprise technology and delivery experience.
Meet our leadership - Current engagement · under NDA
A long-term software engagement with a US-based technology company.
Client identity and project details are undisclosed due to confidentiality obligations. We treat your work the same way.
See our approach
Ways to Work Together
- Advisory & Discovery
Short-term Strategy Engagements
For a focused, short-term engagement - an assessment, an architecture review, or technical due diligence - before any build begins.
- Comprehensive architecture reviews
- Due Diligence of your tech stack
- Roadmap and Estimation
- Fixed Price
Fixed Cost Model
For clearly-scoped integration or build work, where the outcome and boundaries are already well defined.
- Change Management process
- Milestone-based delivery
- Quality & SLA Commitments
- Dedicated Team
Dedicated Product Engineering Team
For long-term product or systems work - a sustained AppPotter engineering team working against your roadmap, not rotated project to project.
- Seamless onboarding and roadmap planning
- Fixed Monthly Retainer
- PO/Tech-lead Collaborations
Whichever structure fits, Parth and Umesh are personally involved, from scoping the engagement to standing behind its outcome.
Meet our leadershipPart of Business Systems Engineering
Connecting the operational systems, workflows and data a growing business runs on.
Data Warehouse Consulting FAQs
Still unsure? A 30-minute conversation usually answers the rest.
Book a callNot necessarily. If your data volumes are modest and your sources are few, Power BI on your existing data may be enough. The needs assessment tells you which. If the honest answer is "not yet", we will say so.
It depends on data volumes, the tools you already use and your team’s skills. Fabric suits Microsoft-centered teams that want one platform. SQL Server, run as a managed database on Azure or AWS, suits smaller structured workloads. Snowflake suits teams working outside Microsoft or across clouds.
Yes. Our data warehousing and ETL service builds it. You can also take the design to your own team or another partner.
A written recommendation, a platform comparison, the architecture and data model design, a running cost estimate and a phased build roadmap.
It depends on the number of sources and how much is already documented. Scope and timeline are agreed before work starts.
It is usually fixed price, based on the number of data sources and reporting areas in scope.
Tell Us What Your Reporting Struggles With
We'll look at your data and reporting and tell you honestly whether a warehouse is the right next step.
Book a GTM Systems Assessment30-minute call · No obligation · No sales pitch